From 9764e70cdcb8585f4827cedda3bdbafa0d2f7153 Mon Sep 17 00:00:00 2001
From: erex <ericincrail@gmail.com>
Date: Sun, 12 May 2024 11:12:02 +0100
Subject: [PATCH 1/6] start make_pkgdown branch

---
 .Rbuildignore                                 |   5 +
 .gitignore                                    |   2 +
 _pkgdown.yml                                  |  30 +
 vignettes/lines_gomex/mexdolph.bib            | 100 +++
 vignettes/lines_gomex/mexdolphins-extra.rda   | Bin 0 -> 106124 bytes
 vignettes/lines_gomex/mexico-analysis.Rmd     | 604 ++++++++++++++++++
 .../points_france/Contour_Rouillacais.dbf     | Bin 0 -> 174 bytes
 .../points_france/Contour_Rouillacais.shp     | Bin 0 -> 18300 bytes
 .../points_france/Contour_Rouillacais.shx     | Bin 0 -> 108 bytes
 vignettes/points_france/Hare_data.csv         | 576 +++++++++++++++++
 .../points_france/Rouillacais_points.dbf      | Bin 0 -> 2161 bytes
 .../points_france/Rouillacais_points.shp      | Bin 0 -> 1500 bytes
 .../points_france/Rouillacais_points.shx      | Bin 0 -> 500 bytes
 .../points_france/hare_point_transect_dsm.Rmd | 289 +++++++++
 vignettes/points_france/hares.bib             |  38 ++
 15 files changed, 1644 insertions(+)
 create mode 100644 _pkgdown.yml
 create mode 100644 vignettes/lines_gomex/mexdolph.bib
 create mode 100644 vignettes/lines_gomex/mexdolphins-extra.rda
 create mode 100644 vignettes/lines_gomex/mexico-analysis.Rmd
 create mode 100644 vignettes/points_france/Contour_Rouillacais.dbf
 create mode 100644 vignettes/points_france/Contour_Rouillacais.shp
 create mode 100644 vignettes/points_france/Contour_Rouillacais.shx
 create mode 100644 vignettes/points_france/Hare_data.csv
 create mode 100644 vignettes/points_france/Rouillacais_points.dbf
 create mode 100644 vignettes/points_france/Rouillacais_points.shp
 create mode 100644 vignettes/points_france/Rouillacais_points.shx
 create mode 100644 vignettes/points_france/hare_point_transect_dsm.Rmd
 create mode 100644 vignettes/points_france/hares.bib

diff --git a/.Rbuildignore b/.Rbuildignore
index c63a066..a9fec17 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -3,3 +3,8 @@ LICENSE
 ^appveyor\.yml$
 .gitignore
 .github
+^.*\.Rproj$
+^\.Rproj\.user$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
diff --git a/.gitignore b/.gitignore
index 577f512..7ae87a9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,3 @@
 *.Rproj
+.Rproj.user
+docs
diff --git a/_pkgdown.yml b/_pkgdown.yml
new file mode 100644
index 0000000..c7e5850
--- /dev/null
+++ b/_pkgdown.yml
@@ -0,0 +1,30 @@
+url: ~
+template:
+  bootstrap: 5
+  bslib:
+    bg: "#fcfaf2"
+    fg: "#000000"
+    primary: "#0542a3"
+  include-in-header:
+    file: Clarity.txt
+
+navbar:
+  bg: primary
+  structure:
+    right: [twitter, github]
+  components:
+    twitter:
+      icon: fa-twitter
+      href: https://twitter.com/distancesamp
+      aria-label: Twitter
+footer:
+  structure:
+    right: donate
+    left: clarity
+
+  components:
+    donate: "If you wish to donate to development and maintenance"    
+    clarity:  "We improve our site and software support by using Microsoft Clarity to see <br>
+      how you use our website. By using our site, you agree that we and Microsoft <br>
+      can collect and use this data. Clarity is GDPR compliant."
+
diff --git a/vignettes/lines_gomex/mexdolph.bib b/vignettes/lines_gomex/mexdolph.bib
new file mode 100644
index 0000000..85047df
--- /dev/null
+++ b/vignettes/lines_gomex/mexdolph.bib
@@ -0,0 +1,100 @@
+
+@article{miller_spatial_2013,
+	title = {Spatial models for distance sampling data: recent developments and future directions},
+	volume = {4},
+	issn = {2041210X},
+	shorttitle = {Spatial models for distance sampling data},
+	url = {http://doi.wiley.com/10.1111/2041-210X.12105},
+	doi = {10.1111/2041-210X.12105},
+	language = {en},
+	number = {11},
+	urldate = {2018-01-25},
+	journal = {Methods in Ecology and Evolution},
+	author = {Miller, David L. and Burt, M. Louise and Rexstad, Eric A. and Thomas, Len},
+	editor = {Gimenez, Olivier},
+	month = nov,
+	year = {2013},
+	pages = {1001--1010},
+	file = {Methods in Ecology and Evolution 2013 Miller.pdf:/Users/dill/Zotero/storage/HJPNM9RM/Methods in Ecology and Evolution 2013 Miller.pdf:application/pdf}
+}
+
+@article{hedley_spatial_2004,
+	title = {Spatial models for line transect sampling},
+	volume = {9},
+	issn = {1085-7117, 1537-2693},
+	url = {http://link.springer.com/10.1198/1085711043578},
+	doi = {10.1198/1085711043578},
+	language = {en},
+	number = {2},
+	urldate = {2018-01-25},
+	journal = {Journal of Agricultural, Biological, and Environmental Statistics},
+	author = {Hedley, Sharon L. and Buckland, Stephen T.},
+	month = jun,
+	year = {2004},
+	pages = {181--199},
+	file = {JABES 2004 Hedley.pdf:/Users/dill/Zotero/storage/G95JGQ3H/JABES 2004 Hedley.pdf:application/pdf}
+}
+
+@book{wood_generalized_2017,
+	edition = {2nd},
+	series = {Texts in Statistical Science},
+	title = {Generalized Additive Models. An Introduction with R},
+	publisher = {CRC Press},
+	author = {Wood, S. N.},
+	year = {2017}
+}
+
+@article{wood_soap_2008,
+	title = {Soap film smoothing},
+	volume = {70},
+	issn = {13697412, 14679868},
+	url = {http://doi.wiley.com/10.1111/j.1467-9868.2008.00665.x},
+	doi = {10.1111/j.1467-9868.2008.00665.x},
+	abstract = {Conventional smoothing methods sometimes perform badly when used to smooth data over complex domains, by smoothing inappropriately across boundary features, such as peninsulas. Solutions to this smoothing problem tend to be computationally complex, and not to provide model smooth functions which are appropriate for incorporating as components of other models, such as generalized additive models, or mixed additive models. In this paper we propose a class of smoothers appropriate for smoothing over difficult regions of R2, which can be represented in terms of a low rank basis and one or two quadratic penalties. The key features of these smoothers are (i) that they do not {\textquoteleft}smooth across{\textquoteright} boundary features; (ii) that their representation in terms of a basis and penalties allows straightforward incorporation as components of GAMs, mixed models and other non-standard models; (iii) that smoothness selection for these model components is straightforward to accomplish in a computationally efficient manner via GCV, AIC or REML, for example; (iv) that their low rank means that their use is computationally efficient.},
+	language = {en},
+	number = {5},
+	urldate = {2018-04-03},
+	journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
+	author = {Wood, Simon N. and Bravington, Mark V. and Hedley, Sharon L.},
+	month = nov,
+	year = {2008},
+	pages = {931--955},
+	file = {Wood et al. - 2008 - Soap film smoothing.pdf:/Users/dill/Zotero/storage/U7WQB7WY/Wood et al. - 2008 - Soap film smoothing.pdf:application/pdf}
+}
+
+@article{bravington_reliable_2018,
+	title = {Reliable variance propagation for spatial density surface models},
+	abstract = {Density Surface Models (DSMs) are two-stage models for estimating animal density from line-transect data. First, detectability is estimated by distance sampling, and then a Generalized Additive Model (GAM) is used to estimate animal density as a function of location and/or other spatially-varying environmental covariates. One criticism of DSMs has been that uncertainty from the two stages is not usually propagated correctly into the final variance estimates. We show how to reformulate a DSM so that the uncertainty in detection probability from the distance sampling stage (regardless of its complexity) is captured as a random effect in the GAM stage. This random effect acts as a quadratic approximation to the detection probability centred at the point estimate of detectability, effectively fitting the detection function at the same time as the spatial model. This allows straightforward computation of the overall variance via standard GAM machinery. A further extension allows for spatial variation in group size, which can be an important covariate for detectability. We illustrate these models using line transect survey data of minke whales and harbour porpoise from a visual line transect survey of European waters.},
+	language = {en},
+	author = {Bravington, Mark V and Miller, David L and Hedley, Sharon L},
+	year = {2018},
+	pages = {11},
+	file = {Bravington - 2018 - Reliable variance propagation for spatial density .pdf:/Users/dill/Zotero/storage/B3JMA9BQ/Bravington - 2018 - Reliable variance propagation for spatial density .pdf:application/pdf}
+}
+
+@book{bivand_applied_2008,
+	series = {Use R!},
+	title = {Applied Spatial Data Analysis with R},
+	isbn = {978-0-387-78171-6},
+	url = {https://books.google.co.uk/books?id=4gg8yx_lcj0C},
+	publisher = {Springer New York},
+	author = {Bivand, R.S. and Pebesma, E.J. and G{\'o}mez-Rubio, V.},
+	year = {2008},
+	lccn = {2008931196}
+}
+
+@article{dunn_randomized_1996,
+ ISSN = {10618600},
+ URL = {http://www.jstor.org/stable/1390802},
+ abstract = {In this article we give a general definition of residuals for regression models with independent responses. Our definition produces residuals that are exactly normal, apart from sampling variability in the estimated parameters, by inverting the fitted distribution function for each response value and finding the equivalent standard normal quantile. Our definition includes some randomization to achieve continuous residuals when the response variable is discrete. Quantile residuals are easily computed in computer packages such as SAS, S-Plus, GLIM, or LispStat, and allow residual analyses to be carried out in many commonly occurring situations in which the customary definitions of residuals fail. Quantile residuals are applied in this article to three example data sets.},
+ author = {Peter K. Dunn and Gordon K. Smyth},
+ journal = {Journal of Computational and Graphical Statistics},
+ number = {3},
+ pages = {236--244},
+ publisher = {[American Statistical Association, Taylor & Francis, Ltd., Institute of Mathematical Statistics, Interface Foundation of America]},
+ title = {Randomized Quantile Residuals},
+ volume = {5},
+ year = {1996}
+}
+
+
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diff --git a/vignettes/lines_gomex/mexico-analysis.Rmd b/vignettes/lines_gomex/mexico-analysis.Rmd
new file mode 100644
index 0000000..87db0be
--- /dev/null
+++ b/vignettes/lines_gomex/mexico-analysis.Rmd
@@ -0,0 +1,604 @@
+---
+title: 'Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico'
+description: |
+  Example spatial analysis of pantropical spotted dolphins.
+author:
+  - name: David L Miller
+    url: http://distancesampling.org
+    affiliation: CREEM, Univ of St Andrews
+    affiliation_url: https://creem.st-andrews.ac.uk
+date: "`r Sys.Date()`"
+output: 
+  distill::distill_article:
+    toc: true
+    toc_depth: 2
+bibliography: mexdolph.bib
+csl: apa.csl
+---
+
+```{r echo=FALSE}
+library(knitr)
+```
+
+```{r include=FALSE}
+knitr::opts_chunk$set(eval=TRUE, echo=TRUE, message=FALSE, warnings=FALSE, progress=FALSE)
+```
+
+# Introduction
+
+The analysis is based on a dataset of observations of pantropical dolphins in the Gulf of Mexico (shipped with Distance 6.0 and later). For convenience the data are bundled in an `R`-friendly format, although all of the code necessary for creating the data from the Distance project files is available [on github](http://github.com/dill/mexico-data). The OBIS-SEAMAP page for the data may be found at the [SEFSC GoMex Oceanic 1996](http://seamap.env.duke.edu/dataset/25) survey page.
+
+The intention here is to highlight the features of the `dsm` package, rather than perform a full analysis of the data. For that reason, some important steps are not fully explored. Some familiarity with density surface modelling [@miller_spatial_2013] [@hedley_spatial_2004] is assumed.
+
+# Preamble
+
+Before we start, we load the `dsm` package (and its dependencies) and set some options:
+
+```{r loadlibraries}
+library(dsm)
+library(ggplot2)
+
+# plotting options
+gg.opts <- theme(panel.grid.major=element_blank(),
+                 panel.grid.minor=element_blank(),
+                 panel.background=element_blank())
+```
+
+In order to run this vignette, you'll need to install a few R packages. This can be done via the following call to `install.packages`:
+
+```
+install.packages(c("dsm", "Distance", "knitr", "distill", "ggplot2", "rgdal",
+                   "maptools", "plyr", "tweedie"))
+```
+
+# The data
+
+Most of the data we need is included in the `dsm` package, but two additional objects needed for plotting are required and can be downloaded [here](https://examples.distancesampling.org/dsm-line-dolphins/mexdolphins-extra.rda) and should be put into the same directory as this file. The data can then be loaded into R using the following code:
+
+```{r download-extra-data}
+load("mexdolphins-extra.rda")
+```
+
+This should add the objects `survey.area` and `pred.polys` to your environment.
+
+## Observation and segment data
+
+All of the data for this analysis has been nicely pre-formatted and is shipped with `dsm`. Loading that data, we can see that we have four `data.frame`s, the first few lines of each are shown:
+
+```{r loaddata}
+data(mexdolphins)
+```
+
+`segdata` holds the segment data: the transects have already been "chopped" into segments.
+
+```{r head-segdata}
+head(segdata)
+```
+
+`distdata` holds the distance sampling data that will be used to fit the detection function.
+
+```{r head-distdata}
+head(distdata)
+```
+
+`obsdata` links the distance data to the segments.
+
+```{r head-obsdata}
+head(obsdata)
+```
+
+`preddata` holds the prediction grid (which includes all the necessary covariates).
+
+```{r head-preddata}
+head(preddata)
+```
+
+Typically (i.e. for other datasets) it will be necessary divide the transects into segments, and allocate observations to the correct segments using a GIS or other similar package[^MGET], before starting an analysis using `dsm`.
+
+
+## Shapefiles and converting units
+
+Often data in a spatial analysis comes from many different sources. It is important to ensure that the measurements to be used in the analysis are in compatible units, otherwise the resulting estimates will be incorrect or hard to interpret. Having all of our measurements in SI units from the outset removes the need for conversion later, making life much easier.
+
+The data are already in the appropriate units (Northings and Eastings: kilometres from a centroid, projected using the [North American Lambert Conformal Conic projection](https://en.wikipedia.org/wiki/Lambert_conformal_conic_projection)).
+
+There is extensive literature about when particular projections of latitude and longitude are appropriate and we highly recommend the reader review this for their particular study area; [@bivand_applied_2008] is a good starting point. The other data frames have already had their measurements appropriately converted. By convention the directions are named `x` and `y`.
+
+Using latitude and longitude when performing spatial smoothing can be problematic when certain smoother bases are used. In particular when bivariate isotropic bases are used the non-isotropic nature of latitude and longitude is inconsistent (moving one degree in one direction is not the same as moving one degree in the other).
+
+We give an example of projecting the polygon that defines the survey area (which as simply been read into R using `readShapeSpatial` from a shapefile produced by GIS).
+
+```{r projectsurvey, results='hide', message=FALSE}
+library(rgdal)
+library(maptools)
+library(plyr)
+
+# tell R that the survey.area object is currently in lat/long
+proj4string(survey.area) <- CRS("+proj=longlat +datum=WGS84")
+
+# proj 4 string
+# using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/
+lcc_proj4 <- CRS("+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
+
+# project using LCC
+survey.area <- spTransform(survey.area, CRSobj=lcc_proj4)
+
+# simplify the object
+survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords)
+names(survey.area) <- c("x", "y")
+```
+
+The below code generates this plot, which shows the survey area with the transect lines overlaid (using data from `segdata`).
+
+```{r areawithtransects, fig.cap="", fig.height=4}
+p <- qplot(data=survey.area, x=x, y=y, geom="polygon",fill=I("lightblue"),
+ylab="y", xlab="x", alpha=I(0.7))
+p <- p + coord_equal()
+p <- p + geom_line(aes(x,y,group=Transect.Label),data=segdata)
+p <- p + gg.opts
+
+print(p)
+```
+
+Also note that since we've projected our prediction grid, the "squares" don't look quite like squares. So for plotting we'll use the polygons that we've saved, these polygons (stored in `pred.polys`) are read from a shapefile created in GIS, the object itself is of class `SpatialPolygons` from the `sp` package. This plotting method makes plotting take a little longer, but avoids gaps and overplotting. The following plot compares using latitude/longitude (left) with a projection (right).
+
+```{r projection-compare, fig.cap="", fig.height=3}
+par(mfrow=c(1,2))
+
+# put pred.polys into lat/long
+pred_latlong <- spTransform(pred.polys,CRSobj=CRS("+proj=longlat +datum=WGS84"))
+
+# plot latlong
+plot(pred_latlong, xlab="Longitude", ylab="Latitude")
+axis(1); axis(2); box()
+
+# plot as projected
+plot(pred.polys, xlab="Northing", ylab="Easting")
+axis(1); axis(2); box()
+```
+
+
+Tips on plotting polygons are available from [the `ggplot2` wiki](https://github.com/hadley/ggplot2/wiki/plotting-polygon-shapefiles).
+
+Here we define a convenience function to generate an appropriate data structure for `ggplot2` to plot:
+
+```{r ggpoly}
+# given the argument fill (the covariate vector to use as the fill) and a name,
+# return a geom_polygon object
+# fill must be in the same order as the polygon data
+grid_plot_obj <- function(fill, name, sp){
+
+  # what was the data supplied?
+  names(fill) <- NULL
+  row.names(fill) <- NULL
+  data <- data.frame(fill)
+  names(data) <- name
+
+  spdf <- SpatialPolygonsDataFrame(sp, data)
+  spdf@data$id <- rownames(spdf@data)
+  spdf.points <- fortify(spdf, region="id")
+  spdf.df <- join(spdf.points, spdf@data, by="id")
+
+  # seems to store the x/y even when projected as labelled as
+  # "long" and "lat"
+  spdf.df$x <- spdf.df$long
+  spdf.df$y <- spdf.df$lat
+
+  geom_polygon(aes_string(x="x",y="y",fill=name, group="group"), data=spdf.df)
+}
+```
+
+
+# Exploratory data analysis
+
+## Distance data
+
+The top panels of the EDA plots below show histograms of observed distances and cluster size, while the bottom panels show the relationship between observed distance and observed cluster size, and the relationship between observed distance and Beaufort sea state. The plots show that there is some relationship between cluster size and observed distance (fewer smaller clusters seem to be seen at larger distances).
+
+The following code generates the EDA plots:
+
+```{r EDA-plots, fig.height=7, fig.width=7, fig.cap="", results="hide"}
+par(mfrow=c(2,2))
+
+# histograms
+hist(distdata$distance,main="",xlab="Distance (m)")
+hist(distdata$size,main="",xlab="Cluster size")
+
+# plots of distance vs. cluster size
+plot(distdata$distance, distdata$size, main="", xlab="Distance (m)",
+     ylab="Group size", pch=19, cex=0.5, col=gray(0.7))
+
+# lm fit
+l.dat <- data.frame(distance=seq(0,8000,len=1000))
+lo <- lm(size~distance, data=distdata)
+lines(l.dat$distance, as.vector(predict(lo,l.dat)))
+
+plot(distdata$distance,distdata$beaufort, main="", xlab="Distance (m)",
+     ylab="Beaufort sea state", pch=19, cex=0.5, col=gray(0.7))
+```
+
+Top row, left to right: histograms of distance and cluster size; bottom row: plot of distance against cluster size and plot of distances against Beaufort sea state.
+
+
+## Spatial data
+
+Looking separately at the spatial data without thinking about the distances, we can plot the observed group sizes in space (point size is proportional to the group size for each observation). Circle size indicates the size of the group in the observation. There are rather large areas with no observations, which might cause our variance estimates for abundance to be rather large. We also see the depth data which we will use depth later as an explanatory covariate in our spatial model.
+
+```{r spatialEDA, fig.cap=""}
+p <- ggplot() + grid_plot_obj(preddata$depth, "Depth", pred.polys) + coord_equal()
+p <- p + labs(fill="Depth",x="x",y="y",size="Group size")
+p <- p + geom_line(aes(x, y, group=Transect.Label), data=segdata)
+p <- p + geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.7))
+p <- p + gg.opts
+print(p)
+```
+
+
+# Estimating the detection function
+
+We use the `ds` function in the package `Distance` to fit the detection function. (The `Distance` package is intended to make standard distance sampling in `R` relatively straightforward. For a more flexible but more complex alternative, see the function `ddf` in the `mrds` library.)
+
+
+First, loading the `Distance` library:
+
+```{r loadDistance}
+library(Distance)
+```
+
+We can then fit a detection function with hazard-rate key with no adjustment terms:
+
+```{r hrmodel}
+detfc.hr.null <- ds(distdata, max(distdata$distance), key="hr", adjustment=NULL)
+```
+
+Calling `summary` gives us information about parameter estimates, probability of detection, AIC, etc:
+
+```{r hrmodelsummary}
+summary(detfc.hr.null)
+```
+
+
+The following code generates a plot of the fitted detection function (left) and quantile-quantile plot (right):
+
+``` {r hr-detfct, fig.cap="", fig.width=9, fig.height=6, results="hide"}
+layout(matrix(c(1, 2), 1, 2), width=c(1.5, 1))
+plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2)
+gof_ds(detfc.hr.null)
+```
+
+The quantile-quantile plot show relatively good goodness of fit for the hazard-rate detection function.
+
+
+## Adding covariates to the detection function
+
+It is common to include covariates in the detection function (so-called Multiple Covariate Distance Sampling or MCDS). In this dataset there are two covariates that were collected on each individual: Beaufort sea state and size. For brevity we fit only a hazard-rate detection functions with the sea state included as a factor covariate as follows:
+
+```{r hrcovdf, message=FALSE, cache=TRUE, warning=FALSE}
+detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),
+                  key="hr", adjustment=NULL)
+```
+
+Again looking at the `summary`,
+
+```{r hrcovdfsummary, cache=TRUE}
+summary(detfc.hr.beau)
+```
+
+Here the detection function with covariates does not give a lower AIC than the model without covariates (`r round(detfc.hr.beau$ddf$criterion,2)` vs. `r round(detfc.hr.null$ddf$criterion,2)` for the hazard-rate model without covariates). Looking back to the bottom-right panel of the EDA plots, we can see there is not a discernible pattern in the plot of Beaufort vs distance.
+
+For brevity, detection function model selection has been omitted here. In practise we would fit many different forms for the detection function (and select a model based on goodness of fit testing and AIC).
+
+
+# Fitting a DSM
+
+Before fitting a `dsm` model, the data must be segmented; this consists of chopping up the transects and attributing counts to each of the segments. As mentioned above, these data have already been segmented.
+
+
+## A simple model
+
+We begin with a very simple model. We assume that the number of individuals in each segment are quasi-Poisson distributed and that they are a smooth function of their spatial coordinates (note that the formula is exactly as one would specify to `gam` in `mgcv`). By setting `group=TRUE`, the abundance of clusters/groups rather than individuals can be estimated (though we ignore this here). Note we set `method="REML"` to ensure that smooth terms are estimated reliably.
+
+Running the model:
+
+```{r dsm-xy}
+dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method="REML")
+```
+
+We can then obtain a summary of the fitted model:
+
+```{r dsm-xy-summary}
+summary(dsm.xy)
+```
+
+The exact interpretation of the model summary results can be found in [@wood_generalized_2017]; here we can see various information about the smooth components fitted and general model statistics.
+
+We can use the deviance explained to compare between models[^rsqoffset].
+
+
+We can also get a rough idea of what the smooth of space looks like using `vis.gam` (white/yellow indicates high values, red low indicates low values):
+
+```{r visgam1, fig.cap=''}
+vis.gam(dsm.xy, plot.type="contour", view=c("x","y"), asp=1, type="response", contour.col="black", n.grid=100)
+```
+
+The `type="response"` argument ensures that the plot is on the scale of abundance but the values are relative (as the offsets are set to be their median values). This means that the plot is useful to get an idea of the general shape of the smooth but cannot be interpreted directly.
+
+## Adding another environmental covariate to the spatial model
+
+The data set also contains a `depth` covariate (which we plotted above). We can include in the model very simply:
+
+```{r depthmodel}
+dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method="REML")
+summary(dsm.xy.depth)
+```
+
+Here we see a drop in deviance explained, so perhaps this model is not as useful as the first. We discuss setting the `k` parameter in [Model checking], below.
+
+Setting `select=TRUE` here (as an argument to `gam`) would impose extra shrinkage terms on each smooth in the model (allowing smooth terms to be removed from the model during fitting; see `?gam` for more information). This is not particularly useful here, so we do not include it. However when there are many environmental predictors is in the model this can be a good way (along with looking at $p$-values) to perform term selection.
+
+Simply calling `plot` on the model object allows us to look at the relationship between depth and the linear predictor:
+
+```{r dsm-xy-depth-depth, fig.cap=""}
+plot(dsm.xy.depth, select=2)
+```
+
+Omitting the argument `select` in the call to `plot` will plot each of the smooth terms, one at a time.
+
+
+## Spatial models when there are covariates in the detection function
+
+The code to fit the DSM when there are covariates in the detection function is similar to the other models, above. However since the detection function has observation-level covariates, we must estimate the abundance per segment using a Horvitz-Thompson-like estimator before modelling, so we change the response to be `abundance.est`:
+
+``` {r dsm-est-xy, cache=TRUE}
+dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method="REML")
+```
+
+As we can see, the `summary` results are rather similar:
+
+```{r dsm-est-xy-summary}
+summary(dsm.est.xy)
+```
+
+As is the resulting spatial smooth (though the resulting surface is somewhat "amplified"):
+
+```{r visgam5, fig.cap=""}
+vis.gam(dsm.est.xy, plot.type="contour", view=c("x","y"), asp=1, type="response", zlim=c(0, 300), contour.col="black", n.grid=100)
+```
+
+
+## Other response distributions
+
+Often the quasi-Poisson distribution doesn't give adequate flexibility and doesn't capture the overdispersion in the response data (see [Model checking] and [Model selection] below), so below we illustrate two additional distributions that can be used with count data.
+
+For the models in this section, we'll move back to the `count` response, though the estimated abundance would also work.
+
+### Tweedie
+
+Response distributions other than the quasi-Poisson can be used, for example the Tweedie distribution. The Tweedie distribution is available in `dsm` by setting `family=tw()`.
+
+```{r tweedie-fit}
+dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method="REML")
+summary(dsm.xy.tweedie)
+```
+
+
+
+### Negative binomial
+
+Though not used here there are, similarly, two options for the negative binomial distribution: `negbin` and `nb`. The former requires the user specification single parameter `theta` or a range of values for the parameter (specified as a vector), the latter estimates the value of `theta` during the model fitting process (and is generally faster). The latter is recommended for most users.
+
+## Other spatial modelling options
+
+There is a large literature on spatial modelling using GAMs, much of which can be harnessed in a DSM context. Here are a few highlights.
+
+### Soap film smoothing
+
+To account for a complex region (e.g., a region that includes peninsulae) we can use the soap film smoother [@wood_soap_2008].
+
+To use a soap film smoother for the spatial part of the model we must create a set of knots for the smoother to use. This is easily done using the `make.soapgrid()` function in `dsm`:
+
+```{r soap-knots}
+soap.knots <- make.soapgrid(survey.area,c(15,10))
+```
+
+where the second argument specifies the number of points (in each direction) in the grid that will be used to create the knots (knots in the grid outside of `survey.area` are removed).
+
+As we saw in the exploratory analysis, some of the transect lines are outside of the survey area. These will cause the soap film smoother to fail, so we remove them:
+
+```{r soap-setup}
+x <- segdata$x; y<-segdata$y
+onoff <- inSide(x=x,y=y, bnd=as.list(survey.area))
+rm(x,y)
+segdata.soap <- segdata[onoff,]
+```
+
+Note that the [`soap_checker` script available here](https://github.com/dill/soap_checker) can be useful in ensuring that the boundary, data and knots are in the correct format to use with the soap film smoother.
+
+We can run a model with both the `depth` covariate along with a spatial (soap film) smooth. Note that the `k` argument now refers to the complexity of the boundary smooth in the soap film, and the complexity of the film is controlled by the knots given in the `xt` argument.
+
+``` {r soap-fit, cache=TRUE}
+dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs="so", k=15, xt=list(bnd=list(survey.area))) +
+            s(depth),
+          family=tw(), method="REML",
+          detfc.hr.null, segdata.soap, obsdata, knots=soap.knots)
+summary(dsm.xy.tweedie.soap)
+```
+
+
+# Model checking
+
+Fitting models is all well and good, but we'd like to confirm that the models we have are reasonable; `dsm` provides some functions for model checking.
+
+We can use `gam.check` to generate diagnostic plots:
+
+```{r dsm.xy-check, fig.cap="", fig.width=6, fig.height=6}
+gam.check(dsm.xy)
+```
+
+These show that there is some deviation in the Q-Q plot. The "line" of points in the plot of the residuals vs. linear predictor plot corresponds to the zeros in the data.
+
+Note that as well as the plots, `gam.check` also produces information about the model fitting. Of particular interest to us is the last few lines that tell us about the basis size.
+
+The `k` parameter provided to `s` (and `te`) terms in `dsm` controls the complexity of the smooths in the model.
+
+By setting the `k` parameter we specify the largest complexity for that smooth term in the model; as long as this is high enough, we can be sure that there is enough flexibility. In the output from `gam.check` above, we can see that there is a "p-value" calculated for the size of the basis, this can be a good guide as to whether the basis size needs to be increased.
+
+The `?choose.k` manual page from `mgcv` gives further guidance and technical details on this matter.
+
+
+We can look at the same model form but with a Tweedie distribution specified as the response:
+
+``` {r dsm.xy.tweedie-check, fig.cap="", fig.width=6, fig.height=6}
+gam.check(dsm.xy.tweedie)
+```
+
+The Q-Q plot now seems much better (closer to the $y=x$ line). In both plots the histogram of residuals is rather hard to interpret due to the large number of zeros in the data.
+
+Further guidance on interpreting `gam.check` output can be found in [@wood_generalized_2017].
+
+### Randomised quantile residuals
+
+In the top right panel of the above `gam.check` plots the residuals vs. linear predictor plot includes a odd line of predictions. These are an artifact of the link function, showing the exact zeros in the data. These can be misleading and distracting, making it difficult to see whether residuals show heteroskedasticity.
+
+Randomised quantile residuals [@dunn_randomized_1996] avoid this issue by transforming the residuals to be exactly normally distributed. This makes the residuals vs. linear predictor plot much easier to interpret as it therefore doesn't include the artifacts generated by the link function. These plots can be produced using `rqgam.check` in `dsm`:
+
+``` {r dsm.xy.tweedie-rqcheck, fig.cap="", fig.width=6, fig.height=6}
+rqgam.check(dsm.xy.tweedie)
+```
+
+Here we can see that there is no issue with heteroskedasticity (no increase in spread in the residuals vs. linear predictor plot with increasing values of the linear predictor). One can also plot these residuals against covariate values to check for pattern in the residuals.
+
+Note that in general, plots other than "Resids vs. linear pred." should be interpreted with caution in the output of `rqgam.check` as the residuals generated are normal by construction (so for example the Q-Q plot and histogram of residuals will always look fine).
+
+
+# Model selection
+
+Assuming that models have "passed" the checks in `gam.check`, `rqgam.check` and are sufficiently flexible, we may be left with a choice of which model is "best". There are several methods for choosing the best model -- AIC, REML/GCV scores, deviance explained, full cross-validation with test data and so on.
+
+Though this document doesn't intend to be a full analysis of the pantropical dolphin data, we can create a results table to compare the various models that have been fitted so far in terms of their abundance estimates and associated uncertainties.
+
+```{r modelcomp, cache=TRUE}
+# make a data.frame to print out
+mod_results <- data.frame("Model name" = c("`dsm.xy`", "`dsm.xy.depth`", "`dsm.xy.tweedie`", "`dsm.xy.tweedie.soap`",
+                                           "`dsm.est.xy`"),
+                          "Description" = c("Bivariate smooth of location, quasipoisson",
+                                            "Bivariate smooth of location, smooth of depth, quasipoisson",
+                                            "Bivariate smooth of location, smooth of depth, Tweedie",
+                                            "Soap film smooth of location, smooth of depth, Tweedie",
+                                            "Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function"),
+                          "Deviance explained" = c(unlist(lapply(list(dsm.xy,
+                                                                      dsm.xy.depth,
+                                                                      dsm.xy.tweedie,
+                                                                      dsm.xy.tweedie.soap),
+                 function(x){paste0(round(summary(x)$dev.expl*100,2),"%")})),NA))
+
+
+```
+
+We can then use the resulting `data.frame` to build a table of results using the `kable` function:
+
+```{r results-table, results='asis'}
+kable(mod_results, col.names=c("Model name", "Description", "Deviance explained"))
+```
+
+# Abundance estimation
+
+Once a model has been checked and selected, we can make predictions over the grid and calculate abundance. The offset is stored in the `area` column[^offsetcorrection].
+
+```{r dsm-xy-pred}
+dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area)
+```
+
+We use the `grid_plot_obj` helper function to assign the predictions to grid cells (polygons):
+
+```{r dsm.xy-preds, fig.cap=""}
+p <- ggplot() + grid_plot_obj(dsm.xy.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
+p <- p + geom_path(aes(x=x, y=y),data=survey.area)
+p <- p + labs(fill="Abundance")
+print(p)
+```
+
+We can calculate abundance over the survey area by simply summing these predictions:
+
+``` {r dsm.xy-abund}
+sum(dsm.xy.pred)
+```
+
+We can compare this with a plot of the predictions from this `dsm.xy.depth`:
+
+```{r dsm.xy.depth-preds, fig.cap=""}
+dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)
+p <- ggplot() + grid_plot_obj(dsm.xy.depth.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
+p <- p + geom_path(aes(x=x, y=y), data=survey.area)
+p <- p + labs(fill="Abundance")
+print(p)
+```
+
+We can see the inclusion of depth into the model has had a noticeable effect on the distribution (note the difference in legend scale between the two plots). We can again also look at the total abundance:
+
+``` {r dsm.xy.depth-abund}
+sum(dsm.xy.depth.pred)
+```
+
+Here we see that there is not much of a change in the abundance, so in terms of abundance alone there isn't much between the two models. Next we'll go on to look at variance next where we can see bigger differences between the models.
+
+# Variance estimation
+
+Obviously point estimates of abundance are important, but we should also calculate uncertainty around these abundance estimates. Fortunately `dsm` provides functions to perform these calculations and display the resulting uncertainty estimates.
+
+There are two approaches to estimating the uncertainty of an abundance estimate in `dsm`.
+
+- `dsm.var.gam` which assumes that the spatial model and detection function parts of the model are independent of each other. In this case the squared coefficients of variation for each model component are added.
+- `dsm.var.prop` which takes into account the fact that detection probability may be correlated with the spatial part of the model. It uses methods described in [@bravington_reliable_2018].
+
+`dsm.var.prop` can only be applied when there is a covariate in the detection function that varies at the level of the segments, and is recorded at each segment (for example Beaufort). We don't have that situation here, so we opt for `dsm.var.gam`.
+
+ Both methods estimate the variance of the abundance for each element in the list provided in `pred.data`. In our case we wish to obtain an abundance for each of the prediction cells, so we use `split` to chop our data set into list elements to give to `dsm.var.gam` (or `dsm.var.prop`).
+
+```{r dsm.xy-var, cache=TRUE}
+preddata.var <- split(preddata, 1:nrow(preddata))
+dsm.xy.var <- dsm.var.gam(dsm.xy, pred.data=preddata.var,
+                          off.set=preddata$area)
+```
+
+Calling `summary` will give some information about uncertainty estimation:
+
+``` {r}
+summary(dsm.xy.var)
+```
+
+We can also make a plot of the CVs (with transect lines and observations overlaid) using the following code.
+
+```{r dsm.xyvarplot, fig.cap=""}
+p <- ggplot() + grid_plot_obj(sqrt(dsm.xy.var$pred.var)/unlist(dsm.xy.var$pred),
+                              "CV", pred.polys) + coord_equal() +gg.opts
+p <- p + geom_path(aes(x=x, y=y), data=survey.area)
+p <- p + geom_line(aes(x, y, group=Transect.Label), data=segdata)
+print(p)
+```
+
+Note the increase in CV away from the transect lines.
+
+We can revisit the model that included both depth and location smooths and observe that the coefficient of variation for that model is larger than that of the model with only the location smooth.
+
+```{r dsm.xy.depth-varprop, cache=TRUE}
+dsm.xy.depth.var <- dsm.var.gam(dsm.xy.depth, pred.data=preddata.var,
+                                off.set=preddata$area)
+summary(dsm.xy.depth.var)
+```
+
+
+# Conclusions
+
+This document has outlined an analysis of spatially-explicit distance sampling data using the `dsm` package. Note that there are many possible models that can be fitted using `dsm` and that the aim here was to show just a few of the options. Results from the models can be rather different, so care must be taken in performing model selection, discrimination and criticism.
+
+
+# Software
+
+ * `Distance` is available at [http://github.com/DistanceDevelopment/Distance](http://github.com/DistanceDevelopment/Distance) as well as on CRAN.
+ * `dsm` is available at [http://github.com/DistanceDevelopment/dsm](http://github.com/DistanceDevelopment/dsm), as well as on CRAN.
+
+
+[^offsetcorrection]: An earlier version of this vignette incorrectly stated that the areas of the prediction cells were 444km$^2$. This has been corrected. Thanks to Phil Bouchet for pointing this out.
+[^MGET]: These operations can be performed in R using the `sp` and `rgeos` packages. It may, however, be easier to perform these operations in GIS such as ArcGIS -- in which case the MGET Toolbox may be useful.
+[^rsqoffset]: Note though that the adjusted $R^2$ for the model is defined as the proportion of variance explained but the "original" variance used for comparison doesn't include the offset (the area (or effective of the segments). It is therefore not recommended for one to directly interpret the $R^2$ value (see the `summary.gam` manual page for further details).
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diff --git a/vignettes/points_france/Contour_Rouillacais.shx b/vignettes/points_france/Contour_Rouillacais.shx
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diff --git a/vignettes/points_france/Hare_data.csv b/vignettes/points_france/Hare_data.csv
new file mode 100644
index 0000000..2be7398
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+++ b/vignettes/points_france/Hare_data.csv
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diff --git a/vignettes/points_france/hare_point_transect_dsm.Rmd b/vignettes/points_france/hare_point_transect_dsm.Rmd
new file mode 100644
index 0000000..77b4515
--- /dev/null
+++ b/vignettes/points_france/hare_point_transect_dsm.Rmd
@@ -0,0 +1,289 @@
+---
+title: "Density surface model for hare point transect survey"
+description: |
+  Point transects for hares analysed with model-based inference to study area.
+author:
+  - name: Guillaume Souchay 
+    url: https://scholar.google.com/citations?user=mFKm9kAAAAAJ&hl=en
+    affiliation: French National Hunting and Wildlife Agency
+    affiliation_url: https://ofb.gouv.fr/
+date: "`r Sys.Date()`"
+output: 
+  distill::distill_article:
+    toc: true
+    toc_depth: 2
+bibliography: hares.bib
+csl: apa.csl
+---
+
+```{r echo=FALSE}
+library(knitr)
+opts_chunk$set(message=FALSE, echo=TRUE, error=FALSE, warning=FALSE)
+```
+
+In France, the French National Agency for Hunting and Wildlife has recently created the Hare network [@Mauvy2017], a network of voluntary sites where hares and foxes are sampled every year in February using point transect sampling [@Peroux1997].
+
+As an illustration, we used the sampling data from Rouillacais for hare only, in western part of France. At this site, ~ 50 points were sampled during 3 successive nights in February of 2015 and 2016, distance from observers to hares and foxes were recorded. Presence of other species was also noted. Fieldwork was undertaken by skilled observers only. Data for the two years are pooled to produce a single estimate that represents the average density for the two years.
+
+*The material of this analysis is provided for illustration purpose only. For other use or questions regarding the Hare network, feel free to contact us at reseau.lievre@oncfs.gouv.fr*
+
+*Place all following files in same directory as .Rmd*
+
+- [Point transect survey csv](Hare_data.csv)
+- [Study area shapefile shp](Contour_Rouillacais.shp)
+- [Study area shapefile shx](Contour_Rouillacais.shx)
+- [Study area shapefile dbf](Contour_Rouillacais.dbf)
+- [Point station shapefile shp](Rouillacais_points.shp)
+- [Point station shapefile shx](Rouillacais_points.shx)
+- [Point station shapefile dbf](Rouillacais_points.dbf)
+
+
+
+## Observation data and covariates
+
+First, we load point transect survey data into `R` [@r_core_team_r_2019].
+
+```{r data}
+# load raw data
+data <- read.table("Hare_data.csv", header = TRUE, sep = ";", stringsAsFactors=FALSE)
+str(data)
+```
+
+Then, we build our dataset for the detection function.
+
+```{r Dataset}
+DSdata <- data.frame(Sample.Label = sub("Rouillacais_2016", "", data$point_ID),
+                     Point = data$point_ID,
+                     Xcoord = as.integer(data$Xcoord),
+                     Ycoord = as.integer(data$Ycoord),
+                     Area = 1)
+```
+
+
+Distance are given in metres, so density would be estimated as the number of individuals per square metre. To avoid this, we convert metres into kilometres.
+
+```{r tokm}
+DSdata$distance <- as.numeric(data$distance)/1000
+```
+
+Note that the `Effort` column contains `3`, as each point was visited 3 times.  It is worth noting that in our study, we are not interested in abundance but in density only.
+
+```{r effort}
+DSdata$Effort <- 3
+```
+
+### GIS data
+
+We now load and format the spatial data from shapefiles. Note there is a shape file for the study area (Contour_Rouillacais) and the point station locations (Rouillacais_points).  These shape files reside in the same directory as the point survey data and Rmarkdown file.
+
+```{r map_data}
+library("rgdal")
+library("maptools")
+library("ggplot2")
+library("plyr")
+
+# provide the correct projection for the data
+newproj <- "+proj=lcc +nadgrids=ntf_r93.gsb,null +a=6378249.2000 +rf=293.4660210000000  +pm=2.337229167 +lat_0=46.800000000 +lon_0=0.000000000 +k_0=0.99987742 +lat_1=46.800000000 +x_0=600000.000 +y_0=200000.000 +units=m +no_defs"
+# import shapefile for the survey area
+shape <- readShapeSpatial("Contour_Rouillacais.shp", proj4string = CRS(newproj),
+                          repair=TRUE, force_ring=T, verbose=TRUE)
+# import shapefile for the points
+EPP <- readShapeSpatial("Rouillacais_points.shp", proj4string = CRS(newproj),
+                        repair=TRUE, force_ring=T, verbose=TRUE)
+# make the object simpler
+survey.area <- data.frame(shape@polygons[[1]]@Polygons[[1]]@coords)
+names(survey.area) <- c("x","y")
+```
+
+We can find the area of the study area (in km$^2$):
+
+```{r area}
+shape@data$AREA/(1000^2)
+```
+
+We can then produce the map of the area with the sampled points (Figure \@ref(fig:map1)).
+
+```{r map1, fig.cap="Map with sampled points"}
+# produce a map of the survey area with all the point sampled
+p <- qplot(data=survey.area, x=x, y=y, geom="polygon",
+           fill=I("lightblue"), ylab="y", xlab="x", alpha=I(0.7)) +
+  geom_point(aes(x=Xcoord, y=Ycoord, group="Point"),
+             data=DSdata, colour="darkblue") +
+  coord_equal() +
+  theme_minimal()
+print(p)
+
+```
+
+Setting up the segment data, which in our case are the points that were visited...
+
+```{r segdata}
+# construct segment (point) data (x, y, Effort, Sample.Label)
+segdata <- as.data.frame(matrix(NA, ncol = 5, nrow=100))
+segdata <- DSdata[, c("Sample.Label", "Effort", "Point", "Xcoord", "Ycoord")]
+segdata <- segdata[!duplicated(segdata), ]
+colnames(segdata) <- c("Sample.Label", "Effort", "Segment.Label", "X", "Y")
+```
+
+Setting up the observation data, which links the observations with the segments (points):
+
+```{r obsdata}
+obsdata <- DSdata
+obsdata$size <- 1
+obsdata$object <- 1:nrow(obsdata)
+str(obsdata)
+```
+
+
+We then create the prediction grid (Figure \@ref(fig:projgrid)).
+
+```{r projgrid, fig.cap="Prediction grid"}
+# create a prediction grid
+# method from http://rfunctions.blogspot.co.uk/2014/12/how-to-create-grid-and-intersect-it.html
+library("raster")
+library("rgeos")
+library("dismo")
+
+# Create an empty raster
+grid <- raster(extent(shape))
+# Choose its resolution. 500 m in both X and Y (truncation distance)
+res(grid) <- 500
+# Make the grid have the same coordinate reference system (CRS) as the shapefile.
+proj4string(grid) <- proj4string(shape)
+# Transform this raster into a polygon and you will have a grid
+gridpolygon <- rasterToPolygons(grid)
+# Intersect our grid with shape
+pred.grid <- intersect(shape, gridpolygon)
+# Plot the intersected shape to check if everything is fine.
+plot(pred.grid)
+
+# create the data.frame for prediction
+preddata <- as.data.frame(matrix(NA, ncol=3, nrow=dim(pred.grid@data)[1]))
+colnames(preddata) <- c("X", "Y", "area")
+for (i in 1:dim(pred.grid@data)[1]){
+  preddata[i, c("X", "Y")] <- pred.grid@polygons[[i]]@labpt
+  preddata[i, c("area")] <- pred.grid@polygons[[i]]@area/(1000^2)
+}
+```
+
+The size of each cell in the prediction grid is 0.25 km$^2$ (created in metres and converted to kilometres).
+
+## Detection function
+
+Detection functions can be fitted using the `Distance` R package:
+
+```{r det_function}
+library("Distance")
+# define distance bins
+cut <- c(0, 0.180, 0.220, 0.280, 0.300)
+
+df_ht <- ds(DSdata, truncation=0.3, transect="point",
+            formula=~1, key="hn", adjustment=NULL, cutpoints=cut)
+```
+
+We can look at a plot of the detection function and results from goodness of fit testing (Figure \@ref(fig:gof)).
+
+```{r gof, fig.cap="Detection function:  half-normal."}
+plot(df_ht, pdf=TRUE)
+gof_ds(df_ht)
+```
+
+We can see from the above, the model fit seems adequate.
+
+We can now fit some DSMs [@Miller2013]:
+
+
+## Density surface modelling
+
+```{r dsmload}
+library("dsm")
+```
+
+Now fitting the DSMs, we model the count as a function of space, using a Tweedie (`tw()`) response distribution:
+
+```{r dsm-fitting}
+mod_tw <- dsm(count~s(X, Y), ddf.obj=df_ht, segment.data=segdata, 
+              observation.data=obsdata, family=tw(), transect="point")
+```
+
+Note that we need to specify `transect="point"` to ensure that the effort is calculated properly.
+
+We can then look at the `summary` and model checking output (Figure \@ref(fig:gamcheck)).
+
+```{r gamcheck, fig.cap="Results from `gam.check`", layout="l-body-outset"}
+summary(mod_tw)
+gam.check(mod_tw)
+```
+
+This model is fairly rudimentary, so the plot of response vs fitted values doesn't seem that great (note the difference in axis scales) but for illustration purposes here we accept what we have.
+
+## Making predictions
+
+We can now predict over the prediction grid.
+
+```{r makepred}
+mod_tw_pred <- predict(mod_tw, preddata, preddata$area)
+```
+
+Here we define a convenience function to generate an appropriate data structure for `ggplot2` to plot: given the argument `fill` (the covariate vector to use as the fill) and a name, return a `geom_polygon` object  (fill must be in the same order as the polygon data).
+
+
+```{r plotpred}
+grid_plot_obj <- function(shape,fill, name){
+  
+  # what data were supplied?
+  names(fill) <- NULL
+  row.names(fill) <- NULL
+  data <- data.frame(fill)
+  names(data) <- name
+  
+  # ! need to give the right name of the shapefile
+  sp <- shape
+  spdf <- SpatialPolygonsDataFrame(sp, data)
+  spdf@data$id <- rownames(spdf@data)
+  spdf.points <- fortify(spdf, region="id")
+  spdf.df <- join(spdf.points, spdf@data, by="id")
+  
+  # store the x/y even when projected and labelled as "long" and "lat"
+  spdf.df$x <- spdf.df$long
+  spdf.df$y <- spdf.df$lat
+  
+  geom_polygon(aes_string(x="x",y="y",fill=name, group="group"), data=spdf.df)
+}
+
+# make the plot
+pcount_tw <- ggplot() +
+  grid_plot_obj(pred.grid, mod_tw_pred, "Density") + 
+  scale_fill_gradient(low="white", high="chocolate4")  +
+  coord_equal() + theme_minimal() +
+  geom_path(aes(x=x, y=y), data=survey.area) +
+  geom_point(aes(x = Xcoord, y = Ycoord, group="Point"), data = DSdata, colour = "black") +
+  labs(fill="Density")
+```
+
+We can also estimate uncertainty in our abundance map in the form of a map of coefficients of variation. Note that since there are no covariates in the detection function, we use the `dsm.var.gam` function to estimate the variance (if there were covariates varying at the segment level, such as sea state or observer, we could use `dsm.var.prop`)  (Figure \@ref(fig:gridarr)).
+
+```{r cvpred}
+# data setup for plotting
+preddata.var <- split(preddata, 1:nrow(preddata))
+
+# estimate variance
+mod_tw_var <- dsm.var.gam(mod_tw, pred.data=preddata.var, off.set=preddata$area)
+summary(mod_tw_var)
+
+# plot
+pcount_cv_tw <- ggplot() + 
+  grid_plot_obj(pred.grid, sqrt(mod_tw_var$pred.var)/unlist(mod_tw_var$pred), "CV") + 
+  scale_fill_gradient(low = "white", high = "chocolate4") +
+  coord_equal() + theme_minimal() +
+  geom_path(aes(x=x, y=y),data=survey.area) +
+  geom_point(aes(x=Xcoord, y=Ycoord, group="Point"), data=DSdata, colour="black")
+```
+
+```{r, gridarr, fig.cap="Map of hare density (ind/km²) and of associated coefficient of variation in Rouillacais site from the DSM model with coordinates as covariates using Tweedie response. Black dots represent the sampled point.", layout="l-page"}
+library("gridExtra")
+grid.arrange(pcount_tw, pcount_cv_tw, ncol=2)
+```
+
+This is a small example of what can be done using DSM and the simplest covariates available: geographical coordinates.
+
diff --git a/vignettes/points_france/hares.bib b/vignettes/points_france/hares.bib
new file mode 100644
index 0000000..74c4d64
--- /dev/null
+++ b/vignettes/points_france/hares.bib
@@ -0,0 +1,38 @@
+% Encoding: UTF-8
+
+@InProceedings{Mauvy2017,
+  author    = {B. Mauvy and G. Souchay and J.-P. Arnauduc and J.-S. Guitton},
+  booktitle = {33rd IUBG Congress & 14th Perdix Symposium},
+  title     = {The French Hare Network : a tool to study European hare populations dynamics on a large scale},
+  year      = {2017},
+  url       = {http://iugb2017.com/wp-content/uploads/2017/08/abstract-book-FINAL-VERSION.pdf},
+}
+
+@Article{Peroux1997,
+  author  = {R. Peroux and B Mauvy and A. Lartiges and Y. Bray and E. Marboutin},
+  journal = {Game and Wildlife},
+  title   = {Point transects sampling: a new approach to estimate densities or abundances of European hare (Lepus europaeus) from spotlight counts},
+  year    = {1997},
+  pages   = {525-529},
+  volume  = {14},
+}
+
+
+@misc{r_core_team_r_2019,
+  address = {{Vienna Austria}},
+  title = {R: A Language and Environment for Statistical Computing},
+  howpublished = {R Foundation for Statistical Computing},
+  author = {{R Core Team}},
+  year = {2019}
+}
+
+@article{Miller2013,
+  author = {Miller, David L. and Burt, M. Louise and Rexstad, Eric A. and Thomas, Len},
+  title = {Spatial models for distance sampling data: recent developments and future directions},
+  journal = {Methods in Ecology and Evolution},
+  year = {2013},
+  volume = {4},
+  pages = {1001-1010},
+  url = {http://dx.doi.org/10.1111/2041-210X.12105},
+  doi = {10.1111/2041-210X.12105}
+}
\ No newline at end of file

From 1f7fe9cb83f1e063be840c9f55e78a8bef1808a1 Mon Sep 17 00:00:00 2001
From: erex <ericincrail@gmail.com>
Date: Sun, 12 May 2024 15:54:22 +0100
Subject: [PATCH 2/6] gomex vignette working, abandoned france vignette

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diff --git a/.Rhistory b/.Rhistory
new file mode 100644
index 0000000..b6ec966
--- /dev/null
+++ b/.Rhistory
@@ -0,0 +1,512 @@
+# p <- p + coord_equal()
+# p <- p + geom_line(aes(x,y,group=Transect.Label),data=segdata)
+# p <- p + gg.opts
+# print(p)
+?CRS
+?proj4string
+?make.soapgrid
+# Chunk 1
+library(knitr)
+# Chunk 2
+knitr::opts_chunk$set(eval=TRUE, echo=TRUE, message=FALSE, warnings=FALSE, progress=FALSE)
+# Chunk 3: loadlibraries
+library(dsm)
+library(ggplot2)
+# plotting options
+gg.opts <- theme(panel.grid.major=element_blank(),
+panel.grid.minor=element_blank(),
+panel.background=element_blank())
+# Chunk 4: download-extra-data
+load("mexdolphins-extra.rda")
+# Chunk 5: loaddata
+data(mexdolphins)
+# Chunk 6: head-segdata
+head(segdata)
+# Chunk 7: head-distdata
+head(distdata)
+# Chunk 8: head-obsdata
+head(obsdata)
+# Chunk 9: head-preddata
+head(preddata)
+# Chunk 10: projectsurvey
+library(sf)
+library(plyr)
+# tell R that the survey.area object is currently in lat/long
+sp::proj4string(survey.area) <- sp::CRS("+proj=longlat +datum=WGS84")
+predsf <- st_as_sf(pred.polys)
+area.sf <- st_as_sf(survey.area)
+st_crs(area.sf) <- "WGS84"
+area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))
+# Convert preddata to a spatial object
+preddata_sf <- st_as_sf(preddata, coords=c("x", "y"))
+st_crs(preddata_sf) <- st_crs(area.sf.proj)
+# Perform the intersection
+preddata_sf <- st_intersection(preddata_sf, area.sf.proj)
+coords_preddata <- data.frame(st_coordinates(preddata_sf))
+preddata_sf$x <- coords_preddata$X
+preddata_sf$y <- coords_preddata$Y
+# Chunk 11: studyareasoap
+# proj 4 string
+# using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/
+lcc_proj4 <- sp::CRS("+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
+# project using LCC
+survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)
+# simplify the object
+survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords)
+names(survey.area) <- c("x", "y")
+# Chunk 12: areawithtransects
+segdata_sf <- st_as_sf(segdata, coords = c("x","y"))
+st_crs(segdata_sf) <- st_crs(area.sf.proj)
+# study area outline and segment centres
+plot_segments <- ggplot() +
+geom_sf(data = area.sf.proj, fill="lightblue", color = "blue", linewidth=.1) +
+geom_sf(data=segdata_sf, fill=NA, color="black", linewidth=.3) +
+labs(title="1996 SE Fisheries Science Center Gulf of Mexico cruise",
+subtitle = "Points are segment centres") +
+scale_fill_viridis_c(option = "magma", guide = "none")
+plot_segments
+# p <- qplot(data=survey.area, x=x, y=y, geom="polygon",fill=I("lightblue"),
+# ylab="y", xlab="x", alpha=I(0.7))
+# p <- p + coord_equal()
+# p <- p + geom_line(aes(x,y,group=Transect.Label),data=segdata)
+# p <- p + gg.opts
+# print(p)
+# Chunk 15: EDA-plots
+par(mfrow=c(2,2))
+# histograms
+hist(distdata$distance,main="",xlab="Distance (m)")
+hist(distdata$size,main="",xlab="Cluster size")
+# plots of distance vs. cluster size
+plot(distdata$distance, distdata$size, main="", xlab="Distance (m)",
+ylab="Group size", pch=19, cex=0.5, col=gray(0.7))
+# lm fit
+l.dat <- data.frame(distance=seq(0,8000,len=1000))
+lo <- lm(size~distance, data=distdata)
+lines(l.dat$distance, as.vector(predict(lo,l.dat)))
+plot(distdata$distance,distdata$beaufort, main="", xlab="Distance (m)",
+ylab="Beaufort sea state", pch=19, cex=0.5, col=gray(0.7))
+# Chunk 17: loadDistance
+library(Distance)
+# Chunk 18: hrmodel
+detfc.hr.null <- ds(distdata, max(distdata$distance), key="hr", adjustment=NULL)
+# Chunk 19: hrmodelsummary
+summary(detfc.hr.null)
+# Chunk 20: hr-detfct
+layout(matrix(c(1, 2), 1, 2), width=c(1.5, 1))
+plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2)
+gof_ds(detfc.hr.null)
+par(mfrow=c(1,1))
+# Chunk 21: hrcovdf
+detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),
+key="hr", adjustment=NULL)
+# Chunk 22: hrcovdfsummary
+summary(detfc.hr.beau)
+# Chunk 23: dsm-xy
+dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method="REML")
+# Chunk 24: dsm-xy-summary
+summary(dsm.xy)
+# Chunk 25: visgam1
+vis.gam(dsm.xy, plot.type="contour", view=c("x","y"), asp=1, type="response", contour.col="black", n.grid=100)
+# Chunk 26: depthmodel
+dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method="REML")
+summary(dsm.xy.depth)
+# Chunk 27: dsm-xy-depth-depth
+plot(dsm.xy.depth, select=2)
+# Chunk 28: dsm-est-xy
+dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method="REML")
+# Chunk 29: dsm-est-xy-summary
+summary(dsm.est.xy)
+# Chunk 30: visgam5
+vis.gam(dsm.est.xy, plot.type="contour", view=c("x","y"), asp=1, type="response", zlim=c(0, 300), contour.col="black", n.grid=100)
+# Chunk 31: tweedie-fit
+dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method="REML")
+summary(dsm.xy.tweedie)
+# Chunk 32: soap-knots
+soap.knots <- make.soapgrid(survey.area,c(15,10))
+# Chunk 33: soap-setup
+x <- segdata$x; y<-segdata$y
+onoff <- inSide(x=x,y=y, bnd=as.list(survey.area))
+rm(x,y)
+segdata.soap <- segdata[onoff,]
+# Chunk 34: soap-fit
+dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs="so", k=15, xt=list(bnd=list(survey.area))) +
+s(depth),
+family=tw(), method="REML",
+detfc.hr.null, segdata.soap, obsdata, knots=soap.knots)
+summary(dsm.xy.tweedie.soap)
+# Chunk 35: dsm.xy-check
+gam.check(dsm.xy)
+# Chunk 36: dsm.xy.tweedie-check
+gam.check(dsm.xy.tweedie)
+# Chunk 37: dsm.xy.tweedie-rqcheck
+rqgam_check(dsm.xy.tweedie)
+# Chunk 38: modelcomp
+# make a data.frame to print out
+mod_results <- data.frame("Model name" = c("`dsm.xy`", "`dsm.xy.depth`", "`dsm.xy.tweedie`", "`dsm.xy.tweedie.soap`",
+"`dsm.est.xy`"),
+"Description" = c("Bivariate smooth of location, quasipoisson",
+"Bivariate smooth of location, smooth of depth, quasipoisson",
+"Bivariate smooth of location, smooth of depth, Tweedie",
+"Soap film smooth of location, smooth of depth, Tweedie",
+"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function"),
+"Deviance explained" = c(unlist(lapply(list(dsm.xy,
+dsm.xy.depth,
+dsm.xy.tweedie,
+dsm.xy.tweedie.soap),
+function(x){paste0(round(summary(x)$dev.expl*100,2),"%")})),NA))
+# Chunk 39: results-table
+kable(mod_results, col.names=c("Model name", "Description", "Deviance explained"))
+# Chunk 40: dsm-xy-pred
+dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area)
+# Chunk 41: dsm.xy-preds
+# p <- ggplot() + grid_plot_obj(dsm.xy.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
+# p <- p + geom_path(aes(x=x, y=y),data=survey.area)
+# p <- p + labs(fill="Abundance")
+# print(p)
+prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+preddata_sf$Prediction_xy <- dsm.xy.pred
+pred <- ggplot() +
+geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +
+geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
+labs(title="Spotted dolphins, Gulf of Mexico, abundance estimates",
+subtitle = "Bivariate smooth of location, quasipoisson") +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+pred
+# p <- ggplot() + grid_plot_obj(dsm.xy.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
+# p <- p + geom_path(aes(x=x, y=y),data=survey.area)
+# p <- p + labs(fill="Abundance")
+# print(p)
+prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+preddata_sf$Prediction_xy <- dsm.xy.pred
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+pred <- ggplot() +
+geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +
+geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
+labs(title="Spotted dolphins, Gulf of Mexico, abundance estimates",
+subtitle = "Bivariate smooth of location, quasipoisson") +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+pred
+dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)
+preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+pred <- ggplot() +
+geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +
+geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
+labs(title="Spotted dolphins, Gulf of Mexico, abundance estimates",
+subtitle = "Bivariate smooth of location, smooth of depth, quasipoisson") +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+pred
+# p <- ggplot() + grid_plot_obj(dsm.xy.depth.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
+# p <- p + geom_path(aes(x=x, y=y), data=survey.area)
+# p <- p + labs(fill="Abundance")
+# print(p)
+preddata.var <- split(preddata, 1:nrow(preddata))
+dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,
+off.set=preddata$area)
+summary(dsm.xy.var)
+# p <- ggplot() + grid_plot_obj(sqrt(dsm.xy.var$pred.var)/unlist(dsm.xy.var$pred),
+#                              "CV", pred.polys) + coord_equal() +gg.opts
+# p <- p + geom_path(aes(x=x, y=y), data=survey.area)
+# p <- p + geom_line(aes(x, y, group=Transect.Label), data=segdata)
+#print(p)
+preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction
+# p <- ggplot() + grid_plot_obj(sqrt(dsm.xy.var$pred.var)/unlist(dsm.xy.var$pred),
+#                              "CV", pred.polys) + coord_equal() +gg.opts
+# p <- p + geom_path(aes(x=x, y=y), data=survey.area)
+# p <- p + geom_line(aes(x, y, group=Transect.Label), data=segdata)
+#print(p)
+preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+CV <- ggplot() +
+geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +
+geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
+labs(title="Spotted dolphins, Gulf of Mexico, uncertainty (CV)",
+subtitle = "Bivariate smooth of location, quasipoisson") +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+CV
+?fig.dim
+data(mexdolphins)
+load("mexdolphins-extra.rda")
+predsf <- st_as_sf(pred.polys)
+# plot as projected
+plot(pred.polys, xlab="Northing", ylab="Easting")
+axis(1); axis(2); box()
+predsf <- st_as_sf(pred.polys)
+# plot as projected
+plot(st_geometry(pred.polys), axes=TRUE)
+predsf <- st_as_sf(pred.polys)
+# plot as projected
+plot(st_geometry(predsf), axes=TRUE)
+axis(1); axis(2); box()
+library(sf)
+library(plyr)
+# tell R that the survey.area object is currently in lat/long
+sp::proj4string(survey.area) <- sp::CRS("+proj=longlat +datum=WGS84")
+predsf <- st_as_sf(pred.polys)
+area.sf <- st_as_sf(survey.area)
+st_crs(area.sf) <- "WGS84"
+area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))
+# Convert preddata to a spatial object
+preddata_sf <- st_as_sf(preddata, coords=c("x", "y"))
+st_crs(preddata_sf) <- st_crs(area.sf.proj)
+# Perform the intersection
+preddata_sf <- st_intersection(preddata_sf, area.sf.proj)
+coords_preddata <- data.frame(st_coordinates(preddata_sf))
+preddata_sf$x <- coords_preddata$X
+preddata_sf$y <- coords_preddata$Y
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Gulf of Mexico study area",
+subtitle = "Depth in meters") +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Gulf of Mexico study area",
+subtitle = "Depth in meters") +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Gulf of Mexico study area",
+subtitle = "Depth in meters") +
+geom_point(aes(x, y, size=size), data=distdata) +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth
+View(distdata)
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Spotted dolphins, Gulf of Mexico",
+subtitle = "Depth in meters, size of detected dolphin groups") +
+geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.7)) +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Spotted dolphins, Gulf of Mexico",
+subtitle = "Depth in meters, size of detected dolphin groups") +
+geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Spotted dolphins, Gulf of Mexico",
+subtitle = "Depth in meters, size of detected dolphin groups",
+xlab("Longitude"), ylab=("Latitude")) +
+geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Spotted dolphins, Gulf of Mexico",
+subtitle = "Depth in meters, size of detected dolphin groups") +
+xlab("Longitude") + ylab=("Latitude") +
+geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Spotted dolphins, Gulf of Mexico",
+subtitle = "Depth in meters, size of detected dolphin groups") +
+xlab("Longitude") + ylab=("Latitude") +
+geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Spotted dolphins, Gulf of Mexico",
+subtitle = "Depth in meters, size of detected dolphin groups") +
+xlab("Longitude") + ylab("Latitude") +
+geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth
+setwd("~/GitHub/dsm/vignettes/points_france")
+shape <- st_read("Contour_Rouillacais.shp")
+plot(shape)
+# provide the correct projection for the data
+newproj <- "+proj=lcc +nadgrids=ntf_r93.gsb,null +a=6378249.2000 +rf=293.4660210000000  +pm=2.337229167 +lat_0=46.800000000 +lon_0=0.000000000 +k_0=0.99987742 +lat_1=46.800000000 +x_0=600000.000 +y_0=200000.000 +units=m +no_defs"
+bob <- st_transform(shape, newproj)
+bob <- st_transform(shape, crs=newproj)
+CRS(shape) <- 4326
+bob <- st_transform(x=shape, src=4326, dst=newproj)
+shape <- st_read("Contour_Rouillacais.shp")
+sp::CRS(shape) <- 4326
+sp:::CRS(shape) <- 4326
+?CRS
+sp::crs(shape) <- 4326
+st_crs(shape)
+st_transform(shape, crs=4326)
+shape <- st_read("Contour_Rouillacais.shp", crs=st_crs(4326))
+shape <- st_read("Contour_Rouillacais.shp", crs=st_crs(27562))
+EPP <- readShapeSpatial("Rouillacais_points.shp", crs=st_crs(27562))
+# make the object simpler
+survey.area <- data.frame(shape@polygons[[1]]@Polygons[[1]]@coords)
+library(sfheaders)
+install.packages("sfheaders")
+library(sfheaders)
+survey.area <- sf_to_df(shape)
+study <- ggplot() +
+geom_sf(data=shape, fill="lightblue") +
+geom_sf(data=EPP, color="black") +
+theme_minimal()
+EPP <- st_read("Rouillacais_points.shp", crs=st_crs(27562))
+study <- ggplot() +
+geom_sf(data=shape, fill="lightblue") +
+geom_sf(data=EPP, color="black") +
+theme_minimal()
+print(study)
+# load raw data
+data <- read.table("Hare_data.csv", header = TRUE, sep = ";", stringsAsFactors=FALSE)
+str(data)
+DSdata <- data.frame(Sample.Label = sub("Rouillacais_2016", "", data$point_ID),
+Point = data$point_ID,
+Xcoord = as.integer(data$Xcoord),
+Ycoord = as.integer(data$Ycoord),
+Area = 1)
+DSdata$distance <- as.numeric(data$distance)/1000
+DSdata$Effort <- 3
+shape$AREA/(1000^2)
+# construct segment (point) data (x, y, Effort, Sample.Label)
+segdata <- as.data.frame(matrix(NA, ncol = 5, nrow=100))
+segdata <- DSdata[, c("Sample.Label", "Effort", "Point", "Xcoord", "Ycoord")]
+segdata <- segdata[!duplicated(segdata), ]
+colnames(segdata) <- c("Sample.Label", "Effort", "Segment.Label", "X", "Y")
+obsdata <- DSdata
+obsdata$size <- 1
+obsdata$object <- 1:nrow(obsdata)
+str(obsdata)
+prediction_grid <- st_make_grid(shape, cellsize = c(500,500))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+hmm <- st_intersection(prediction_grid_sf, shape)
+plot(hmm)
+plot(prediction_grid_sf)
+library("Distance")
+# define distance bins
+cut <- c(0, 0.180, 0.220, 0.280, 0.300)
+df_ht <- ds(DSdata, truncation=0.3, transect="point",
+formula=~1, key="hn", adjustment=NULL, cutpoints=cut)
+mod_tw <- dsm(count~s(X, Y), ddf.obj=df_ht, segment.data=segdata,
+observation.data=obsdata, family=tw(), transect="point")
+mod_tw_pred <- predict(mod_tw, preddata, preddata$area)
+str(hmm)
+preddata <- as.data.frame(matrix(NA, ncol=3, nrow=length(hmm$geometry)))
+colnames(preddata) <- c("X", "Y", "area")
+head(hmm$geometry)
+head(hmm$geometry)[[1]]
+701*24
+hmm$geometry[[1]][[1:4]]
+hmm$geometry[[1]]
+tom <- hmm$geometry[[1]]
+str(tom)
+tom[[1]][1:4, 1:2]
+mean(tom[[1]][1:4, 1])
+mean(tom[[1]][1:4, 2])
+for (i in 1:length(hmm$geometry)){
+preddata[i, c("X")] <- mean(hmm$geometry[[i]][1:4, 1])
+preddata[i, c("Y")] <- mean(hmm$geometry[[i]][1:4, 2])
+preddata[i, c("area")] <- pred.grid$AREA[[i]]/(1000^2)
+}
+hmm$geometry[[22]]
+hmm$geometry[[22]][1:4, 1]
+hmm$geometry[[22]][[1]][1:4, 1]
+prediction_grid <- st_make_grid(shape, cellsize = c(500,500))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+hmm <- st_intersection(prediction_grid_sf, shape)
+preddata <- as.data.frame(matrix(NA, ncol=3, nrow=length(hmm$geometry)))
+colnames(preddata) <- c("X", "Y", "area")
+for (i in 1:length(hmm$geometry)){
+preddata[i, c("X")] <- mean(hmm$geometry[[i]][[1]][1:4, 1])
+preddata[i, c("Y")] <- mean(hmm$geometry[[i]][[1]][1:4, 2])
+preddata[i, c("area")] <- pred.grid$AREA[[i]]/(1000^2)
+}
+prediction_grid <- st_make_grid(shape, cellsize = c(500,500))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+hmm <- st_intersection(prediction_grid_sf, shape)
+preddata <- as.data.frame(matrix(NA, ncol=3, nrow=length(hmm$geometry)))
+colnames(preddata) <- c("X", "Y", "area")
+for (i in 1:length(hmm$geometry)){
+preddata[i, c("X")] <- mean(hmm$geometry[[i]][[1]][1:4, 1])
+preddata[i, c("Y")] <- mean(hmm$geometry[[i]][[1]][1:4, 2])
+preddata[i, c("area")] <- hmm$AREA[[i]]/(1000^2)
+}
+hmm$geometry[[23]][[1]][1:4,1]
+hmm$geometry[[23]][[1]][1:4,2]
+hmm$geometry[[700]][[1]][1:4,2]
+hmm$geometry[[500]][[1]][1:4,2]
+hmm$geometry[[600]][[1]][1:4,2]
+hmm$geometry[[700]][[1]][1:4,2]
+hmm$geometry[[650]][[1]][1:4,2]
+hmm$geometry[[680]][[1]][1:4,2]
+hmm$geometry[[690]][[1]][1:4,2]
+hmm$geometry[[699]][[1]][1:4,2]
+hmm$AREA[600]
+hmm$AREA[700]
+hmm$AREA[701]
+length(hmm$geometry)
+# create a prediction grid
+# method from http://rfunctions.blogspot.co.uk/2014/12/how-to-create-grid-and-intersect-it.html
+library("raster")
+library("rgeos")
+library("dismo")
+# Create an empty raster
+grid <- raster(extent(shape))
+# Choose its resolution. 500 m in both X and Y (truncation distance)
+res(grid) <- 500
+# Make the grid have the same coordinate reference system (CRS) as the shapefile.
+proj4string(grid) <- proj4string(shape)
+area.sf <- st_as_sf(survey.area)
+# provide the correct projection for the data
+newproj <- "+proj=lcc +nadgrids=ntf_r93.gsb,null +a=6378249.2000 +rf=293.4660210000000  +pm=2.337229167 +lat_0=46.800000000 +lon_0=0.000000000 +k_0=0.99987742 +lat_1=46.800000000 +x_0=600000.000 +y_0=200000.000 +units=m +no_defs"
+# import shapefile for the survey area
+shape <- readShapeSpatial("Contour_Rouillacais.shp", proj4string = CRS(newproj),
+repair=TRUE, force_ring=T, verbose=TRUE)
+library(sp)
+# provide the correct projection for the data
+newproj <- "+proj=lcc +nadgrids=ntf_r93.gsb,null +a=6378249.2000 +rf=293.4660210000000  +pm=2.337229167 +lat_0=46.800000000 +lon_0=0.000000000 +k_0=0.99987742 +lat_1=46.800000000 +x_0=600000.000 +y_0=200000.000 +units=m +no_defs"
+# import shapefile for the survey area
+shape <- readShapeSpatial("Contour_Rouillacais.shp", proj4string = CRS(newproj),
+repair=TRUE, force_ring=T, verbose=TRUE)
+# import shapefile for the points
+EPP <- readShapeSpatial("Rouillacais_points.shp", proj4string = CRS(newproj),
+repair=TRUE, force_ring=T, verbose=TRUE)
+?readShapeSpatial
+??readShapeSpatial
+prediction_grid <- st_make_grid(shape, cellsize = c(500,500))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+hmm <- st_intersection(prediction_grid_sf, shape)
+preddata <- as.data.frame(matrix(NA, ncol=3, nrow=length(hmm$geometry)))
+colnames(preddata) <- c("X", "Y", "area")
+for (i in 1:length(hmm$geometry)){
+preddata[i, c("X")] <- mean(hmm$geometry[[i]][[1]][1:4, 1])
+preddata[i, c("Y")] <- mean(hmm$geometry[[i]][[1]][1:4, 2])
+preddata[i, c("area")] <- hmm$AREA[[i]]/(1000^2)
+}
+# create a prediction grid
+# method from http://rfunctions.blogspot.co.uk/2014/12/how-to-create-grid-and-intersect-it.html
+library("raster")
+library("rgeos")
+library("dismo")
+# Create an empty raster
+grid <- raster(extent(shape))
+# Choose its resolution. 500 m in both X and Y (truncation distance)
+res(grid) <- 500
+# Make the grid have the same coordinate reference system (CRS) as the shapefile.
+proj4string(grid) <- proj4string(shape)
+# import shapefile for the survey area
+shape <- readShapeSpatial("Contour_Rouillacais.shp", proj4string = CRS(newproj),
+repair=TRUE, force_ring=T, verbose=TRUE)
+library(maptools)
+pkgdown::build_site()
+pkgdown::build_news()
+getwd()
+setwd("~/GitHub/dsm")
+pkgdown::build_news()
diff --git a/Clarity.txt b/Clarity.txt
new file mode 100644
index 0000000..d1986cd
--- /dev/null
+++ b/Clarity.txt
@@ -0,0 +1,7 @@
+<script type="text/javascript">
+    (function(c,l,a,r,i,t,y){
+        c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)};
+        t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i;
+        y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);
+    })(window, document, "clarity", "script", "maw5mo10ad");
+</script>
\ No newline at end of file
diff --git a/DESCRIPTION b/DESCRIPTION
index ae58c3e..fdf4fd4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -3,8 +3,28 @@ Maintainer: Laura Marshall <lhm@st-andrews.ac.uk>
 License: GPL (>= 2)
 Title: Density Surface Modelling of Distance Sampling Data
 LazyLoad: yes
-Author: David L. Miller, Eric Rexstad, Louise Burt, Mark V. Bravington,
-    Sharon Hedley, Megan Ferguson, Natalie Kelly.
+Authors@R: 
+  c(person(given=c("David", "L."),
+           family="Miller",
+           role=c("aut")),
+    person(given="Eric",
+           family="Rexstad",
+           role="ctb"),
+    person(given="Louise",
+           family="Burt",
+           role="ctb"),
+    person(given=c("Mark", "V."),
+           family="Bravington",
+           role="ctb"),
+    person(given="Sharon",
+           family="Hedley",
+           role="ctb"),
+    person(given="Megan",
+           family="Ferguson",
+           role="ctb"),
+    person(given="Natalie",
+           family="Kelly",
+           role="ctb"))
 Description: Density surface modelling of line transect data. A Generalized
     Additive Model-based approach is used to calculate spatially-explicit estimates
     of animal abundance from distance sampling (also presence/absence and strip
diff --git a/NEWS b/NEWS.md
similarity index 92%
rename from NEWS
rename to NEWS.md
index 23aa023..0194b16 100644
--- a/NEWS
+++ b/NEWS.md
@@ -1,11 +1,9 @@
-dsm 2.3.4
-------------------
+# dsm 2.3.4
 
 - fix bug where if predictions were NA (e.g., predictions outside the soap boundary), all variances were NA due to propagation (#4)
 
 
-dsm 2.3.3
-------------------
+# dsm 2.3.3
 
 * To improve consistency in functions in the package, some functions have changed from . separation to _. For function arguments some separators have been switched from _ to .. An error is now thrown when the "old" arguments/functions using . are used. This error will be removed in dsm 2.3.4.
   - dsm.var.prop -> dsm_var_prop
@@ -17,14 +15,12 @@ dsm 2.3.3
   - dsm_varprop
     - var_type -> var.type
 
-dsm 2.3.2
-------------------
+# dsm 2.3.2
 
 * fixed bug in offset calculation in dsm_varprop se estimation where offsets were logged twice. Thanks to Megan Ferguson for spotting this.
 * documentation now in rmarkdown format
 
-dsm 2.3.1
-------------------
+- dsm 2.3.1
 
 * transect type (line or point) is now determined from the detection function rather than using the transect= argument
 * multiple detection functions are now possible, use a list() of fitted detection functions supplied to ddf.obj= argument to dsm(). dsm.var.gam, dsm.var.prop and dsm_varprop are compatible. Vignette coming soon. Thanks to Dave Fifield and Ewan Wakefield for extensive testing and suggestions. See ?"dsm-data" for more information on how to set up your data.
@@ -38,25 +34,21 @@ dsm 2.3.1
 * availability= argument now works differently for count models. availability must now be the same length as number of segments (nrow(segment.data)) and will multiply the offset (effective area) rather than the number of animals observed to be more coherent with how detectability is handled in this case.
 * dsm_varprop no longer requies newdata to be supplied. If newdata isn't present just the refitted and original models are returned, with other entries NA.
 
-dsm 2.3.0
-------------------
+# dsm 2.3.0
 
 * fixed dsm_varprop (gam.fixed.priors) for response distributions with fixed scale parameters. Negative binomial results before now may have been incorrect!
 
-dsm 2.2.18
-------------------
+# dsm 2.2.18
 
 * Clarification of the predict.dsm return documentation. Thanks to Dave Fifield for the suggestion.
 * off.set=NULL lead to predictions of 0 when density was used. Now just returns density. Thanks to Dave Fifield for the catch.
 
 
-dsm 2.2.17
-------------------
+# dsm 2.2.17
 
 * Now Suggests package "sp" (needed for some parts of the mexdolphin example data)
 
-dsm 2.2.16
-------------------
+# dsm 2.2.16
 
 * check that any Sample.Labels match up, if none do then throw an error. Thanks to Madhura Davate for bringing this issue to my attention.
 * gamma option removed, since it now is not ignored by gam(). If using method="GCV.Cp" then gamma=1.4 should still be used (but generally we advise using REML rather than GCV for smoothing parameter selection)
@@ -64,8 +56,7 @@ dsm 2.2.16
 * dsm.var.prop (and dsm_varprop) now throw an error when there are no covariates in the detection function as it can't do anything useful
 * new function obs_exp is a diagnostic function to compare observed and expected counts at different covariate aggregations
 
-dsm 2.2.15
-------------------
+# dsm 2.2.15
 
 * when mexdolphins is loaded using data(), the data no longer need to be attach()ed
 * dsm.var.prop throws an error when count or N are not used as the response.
@@ -78,22 +69,19 @@ dsm 2.2.15
 * fix potential issue with negative binomial response is used with variance propagation (due to scale parameterisations). dsm_varprop now refits nb() models as negbin() but we advise users to look into this more deeply. Thanks to Eric Rexstad for spotting this.
 * fix bug in predict.dsm() where predictions for "density" response were not including the offset. Thanks to Len Thomas for spotting this.
 
-dsm 2.2.14
-------------------
+# dsm 2.2.14
 
 * nasty bug in predict.dsm fixed: was logging the offset twice when newdata was not provided, leading to very odd checking plots. This also effected the results of predict() when newdata= was not specified. Thanks to Megan Ferguson for alerting us to this.
 * add support for point transects(!)
 * rqgam.check() now produced ONLY the linear predictor vs. randomised quantile residuals plot to avoid confusion in interpreting the other plots
 * REML is now default method for smoothing parameter selection
 
-dsm 2.2.13
-------------------
+# dsm 2.2.13
 
 * dsm.var.gam now has an example
 * dsm.var.prop now checks that the detection function model does not have covariates
 
-dsm 2.2.12
-------------------
+# dsm 2.2.12
 
 * The alpha argument now works for analytical variance reported using dsm.var.prop and dsm.var.gam print.summary and summary methods. Thanks to Jason Roberts and Eric Rexstad for spotting this issue.
 * dsm now uses numDeriv::grad to calculate derivatives for variance estimation -- this should be much faster. Thanks to Jason Roberts for this fix.
@@ -102,102 +90,86 @@ dsm 2.2.12
 * ensured that ordering of segments matched the ordering of the observations so that aggregation of observations are correct. Thanks to Dave Fifield for reporting this bug and suggesting a fix.
 * check.cols no longer checks to see if there is a column called "distance" when strip transects are being used
 
-dsm 2.2.11
-------------------
+# dsm 2.2.11
 
 * dsm.var.prop uses update() rather than messing around with eval()
 * print.summary.dsm.var didn't work for GAM variance when no detection function was specified, now fixed (thanks to Natalia Dellabianca for spotting this)
 * Removed non-working plot() code in variance examples --  see the Mexico pantropical spotted dolphins for a proper treatment of plotting using projections (http://distancesampling.org/R/vignettes/mexico-analysis.html)
 * plot.var.gam no longer crashes by default when distance data doesn't have columns "x" and "y"
 
-dsm 2.2.10
-------------------
+# dsm 2.2.10
 
 * corrected offsets in examples in dsm, dsm.var.prop, dsm.var.movblk
 * updated mexdolphins data with lat/long reversal in prediction data and non-list storage
 * stopped use of rqgam.check from being used with non-negbin/Tweedie responses (thanks to Phil Bouchet for highlighting this issue).
 
-dsm 2.2.9
-------------------
+# dsm 2.2.9
 
 * Moving block bootstrap now works when there is no detection function (suggested by Laura Williamson)
 * Mexico dolphin data has had the areas of the prediction grids and segments re-projected. Thanks to Phil Bouchet for pointing out this error. Context: https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ
 
 
-dsm 2.2.8
-------------------
+# dsm 2.2.8
 
 * Speed up and reduced memory usage in variance calculations, thanks to Filipe Dias for asking about this.
 * Better error when no observations are matched to the detection function. Thanks to Ricardo Lima for highlighting this.
 * Check for missing columns had incorrect logic, now corrected and with tests. Thanks to Julia Migné for spotting this.
 
-dsm 2.2.7
-------------------
+# dsm 2.2.7
 
 * Plotting check plots using random quantile residuals (rqgam.check) didn't work for Tweedie or tw family models, now fixed. Thanks to Laura Mannocci and Jason Roberts for pointing this out.
 * Plotting CV used matrix() to coerce data into a data.frame -- thus converting all elements to the simplest type. Errors ensued. Thanks to Adrian Schiavini for finding this bug!
 
-dsm 2.2.6
-------------------
+# dsm 2.2.6
 
 * dsm.var.prop and dsm.var.gam now allow you to supply a single number as an offset, then copy it as many times as you need. Thanks to Filipe Dias for suggesting this.
 
-dsm 2.2.5
-------------------
+# dsm 2.2.5
 
 * dsm.var.prop will now throw an error if there is no detection function in the dsm that it's trying to calculate the variance for (thanks to Adrián Schiavini for finding this)
 * variance/CV plotting threw an error when no detection function was supplied but observation=TRUE. Now error is sidestepped (again, thanks to Adrián Schiavini for finding this).
 
-dsm 2.2.4
-------------------
+# dsm 2.2.4
 
 * changed stop() to warning() when data not in the detection function are included in the observation data frame, as they are automatically excluded later, bug found by Eric Rexstad and Anna Cucknell
 
-dsm 2.2.3
-------------------
+# dsm 2.2.3
 
 * moving block boostrap was using observed abundances as the residuals and therefore gave wrong answers (Eric Rexstad found this bug)
 
-dsm 2.2.2
-------------------
+# dsm 2.2.2
 
 * version number reporting now works (Len Thomas found this bug)
 * more documentation
 * by default density and presence models are now weighted by area of each segment
 
-dsm 2.2.1
-------------------
+# dsm 2.2.1
 
 * switch over to new testthat spec
 * added new response variables (Dhat, density.est, count, n), suggestion from Len Thomas
 
-dsm 2.1.4
-------------------
+# dsm 2.1.4
 
 * check that all observed distances are within width will now throw an error
 
-dsm 2.1.3
-------------------
+# dsm 2.1.3
 
 * CRAN-compatibility fixes
 
-dsm 2.1.2
-------------------
+# dsm 2.1.2
 
 * updates to dsm.cor
 * more unified plotting
 * variance estimation tweaks for non-dsm models
 
-dsm 2.0.6
-------------------
+# dsm 2.0.6
 
 * removed warning about if() comparison length in make.soapgrid need to pass bnd as a list()
 * dsm.cor now handles gamm objects
 * dsm.cor allows one to check residual autocorrelation in your models
 * presence/absence data now no longer needs a dummy detection function object
 
-dsm 2.0.5
-------------------
+# dsm 2.0.5
 
 * started NEWS file
 * fixed bug where dsm.var.gam() didn't return predictions so plotting the result would fail
diff --git a/vignettes/apa.csl b/vignettes/apa.csl
new file mode 100644
index 0000000..3f2ccc8
--- /dev/null
+++ b/vignettes/apa.csl
@@ -0,0 +1,1539 @@
+<?xml version="1.0" encoding="utf-8"?>
+<style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0" demote-non-dropping-particle="never" page-range-format="expanded">
+  <info>
+    <title>American Psychological Association 6th edition</title>
+    <title-short>APA (6th ed.)</title-short>
+    <id>http://www.zotero.org/styles/apa-6th-edition</id>
+    <link href="http://www.zotero.org/styles/apa-6th-edition" rel="self"/>
+    <link href="http://owl.english.purdue.edu/owl/resource/560/01/" rel="documentation"/>
+    <author>
+      <name>Simon Kornblith</name>
+      <email>simon@simonster.com</email>
+    </author>
+    <author>
+      <name> Brenton M. Wiernik</name>
+      <email>zotero@wiernik.org</email>
+    </author>
+    <contributor>
+      <name>Bruce D'Arcus</name>
+    </contributor>
+    <contributor>
+      <name>Curtis M. Humphrey</name>
+    </contributor>
+    <contributor>
+      <name>Richard Karnesky</name>
+      <email>karnesky+zotero@gmail.com</email>
+      <uri>http://arc.nucapt.northwestern.edu/Richard_Karnesky</uri>
+    </contributor>
+    <contributor>
+      <name>Sebastian Karcher</name>
+    </contributor>
+    <category citation-format="author-date"/>
+    <category field="psychology"/>
+    <category field="generic-base"/>
+    <updated>2020-01-03T14:43:16+00:00</updated>
+    <rights license="http://creativecommons.org/licenses/by-sa/3.0/">This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License</rights>
+  </info>
+  <locale xml:lang="en">
+    <terms>
+      <term name="editortranslator" form="short">
+        <single>ed. &amp; trans.</single>
+        <multiple>eds. &amp; trans.</multiple>
+      </term>
+      <term name="translator" form="short">trans.</term>
+      <term name="interviewer" form="short">
+        <single>interviewer</single>
+        <multiple>interviewers</multiple>
+      </term>
+      <term name="collection-editor" form="short">
+        <single>series ed.</single>
+        <multiple>series eds.</multiple>
+      </term>
+      <term name="circa" form="short">ca.</term>
+      <term name="bc"> B.C.E.</term>
+      <term name="ad"> C.E.</term>
+    </terms>
+  </locale>
+  <locale xml:lang="es">
+    <terms>
+      <term name="from">de</term>
+    </terms>
+  </locale>
+  <locale xml:lang="de">
+    <terms>
+      <term name="et-al">et al.</term>
+    </terms>
+  </locale>
+  <locale xml:lang="da">
+    <terms>
+      <term name="et-al">et al.</term>
+    </terms>
+  </locale>
+  <locale xml:lang="nn">
+    <terms>
+      <term name="et-al">et al.</term>
+    </terms>
+  </locale>
+  <locale xml:lang="nl">
+    <terms>
+      <term name="et-al">et al.</term>
+    </terms>
+  </locale>
+  <locale xml:lang="nb">
+    <terms>
+      <term name="et-al">et al.</term>
+    </terms>
+  </locale>
+  <macro name="container-contributors-booklike">
+    <choose>
+      <if variable="container-title">
+        <names variable="editor translator" delimiter=", &amp; ">
+          <name and="symbol" initialize-with=". " delimiter=", "/>
+          <label form="short" prefix=" (" text-case="title" suffix=")"/>
+          <substitute>
+            <names variable="editorial-director"/>
+            <names variable="collection-editor"/>
+            <names variable="container-author"/>
+          </substitute>
+        </names>
+      </if>
+    </choose>
+  </macro>
+  <macro name="container-contributors">
+    <choose>
+      <!-- book is here to catch software with container titles -->
+      <if type="book broadcast chapter entry entry-dictionary entry-encyclopedia graphic map personal_communication report speech" match="any">
+        <text macro="container-contributors-booklike"/>
+      </if>
+      <else-if type="paper-conference">
+        <choose>
+          <if variable="collection-editor container-author editor" match="any">
+            <text macro="container-contributors-booklike"/>
+          </if>
+        </choose>
+      </else-if>
+    </choose>
+  </macro>
+  <macro name="secondary-contributors-booklike">
+    <group delimiter="; ">
+      <choose>
+        <if variable="title">
+          <names variable="interviewer">
+            <name and="symbol" initialize-with=". " delimiter=", "/>
+            <label form="short" prefix=", " text-case="title"/>
+          </names>
+        </if>
+      </choose>
+      <choose>
+        <if variable="container-title" match="none">
+          <group delimiter="; ">
+            <names variable="container-author">
+              <label form="verb-short" suffix=" " text-case="title"/>
+              <name and="symbol" initialize-with=". " delimiter=", "/>
+            </names>
+            <names variable="editor translator" delimiter="; ">
+              <name and="symbol" initialize-with=". " delimiter=", "/>
+              <label form="short" prefix=", " text-case="title"/>
+            </names>
+          </group>
+        </if>
+      </choose>
+    </group>
+  </macro>
+  <macro name="secondary-contributors">
+    <choose>
+      <!-- book is here to catch software with container titles -->
+      <if type="book broadcast chapter entry entry-dictionary entry-encyclopedia graphic map report" match="any">
+        <text macro="secondary-contributors-booklike"/>
+      </if>
+      <else-if type="personal_communication">
+        <group delimiter="; ">
+          <group delimiter=" ">
+            <choose>
+              <if variable="genre" match="any">
+                <text variable="genre" text-case="capitalize-first"/>
+              </if>
+              <else>
+                <text term="letter" text-case="capitalize-first"/>
+              </else>
+            </choose>
+            <names variable="recipient" delimiter=", ">
+              <label form="verb" suffix=" "/>
+              <name and="symbol" delimiter=", "/>
+            </names>
+          </group>
+          <text variable="medium" text-case="capitalize-first"/>
+          <choose>
+            <if variable="container-title" match="none">
+              <names variable="editor translator" delimiter="; ">
+                <name and="symbol" initialize-with=". " delimiter=", "/>
+                <label form="short" prefix=", " text-case="title"/>
+              </names>
+            </if>
+          </choose>
+        </group>
+      </else-if>
+      <else-if type="song">
+        <choose>
+          <if variable="original-author composer" match="any">
+            <group delimiter="; ">
+              <!-- Replace prefix with performer label as that becomes available -->
+              <names variable="author" prefix="Recorded by ">
+                <label form="verb" text-case="title"/>
+                <name and="symbol" initialize-with=". " delimiter=", "/>
+              </names>
+              <names variable="translator">
+                <name and="symbol" initialize-with=". " delimiter=", "/>
+                <label form="short" prefix=", " text-case="title"/>
+              </names>
+            </group>
+          </if>
+        </choose>
+      </else-if>
+      <else-if type="article-journal article-magazine article-newspaper" match="any">
+        <group delimiter="; ">
+          <choose>
+            <if variable="title">
+              <names variable="interviewer" delimiter="; ">
+                <name and="symbol" initialize-with=". " delimiter=", "/>
+                <label form="short" prefix=", " text-case="title"/>
+              </names>
+            </if>
+          </choose>
+          <names variable="translator" delimiter="; ">
+            <name and="symbol" initialize-with=". " delimiter=", "/>
+            <label form="short" prefix=", " text-case="title"/>
+          </names>
+        </group>
+      </else-if>
+      <else-if type="paper-conference">
+        <choose>
+          <if variable="collection-editor editor" match="any">
+            <text macro="secondary-contributors-booklike"/>
+          </if>
+          <else>
+            <group delimiter="; ">
+              <choose>
+                <if variable="title">
+                  <names variable="interviewer" delimiter="; ">
+                    <name and="symbol" initialize-with=". " delimiter=", "/>
+                    <label form="short" prefix=", " text-case="title"/>
+                  </names>
+                </if>
+              </choose>
+              <names variable="translator" delimiter="; ">
+                <name and="symbol" initialize-with=". " delimiter=", "/>
+                <label form="short" prefix=", " text-case="title"/>
+              </names>
+            </group>
+          </else>
+        </choose>
+      </else-if>
+      <else>
+        <group delimiter="; ">
+          <choose>
+            <if variable="title">
+              <names variable="interviewer">
+                <name and="symbol" initialize-with=". " delimiter="; "/>
+                <label form="short" prefix=", " text-case="title"/>
+              </names>
+            </if>
+          </choose>
+          <names variable="editor translator" delimiter="; ">
+            <name and="symbol" initialize-with=". " delimiter=", "/>
+            <label form="short" prefix=", " text-case="title"/>
+          </names>
+        </group>
+      </else>
+    </choose>
+  </macro>
+  <macro name="author">
+    <choose>
+      <if type="song">
+        <names variable="composer" delimiter=", ">
+          <name name-as-sort-order="all" and="symbol" sort-separator=", " initialize-with=". " delimiter=", " delimiter-precedes-last="always"/>
+          <substitute>
+            <names variable="original-author"/>
+            <names variable="author"/>
+            <names variable="translator">
+              <name name-as-sort-order="all" and="symbol" sort-separator=", " initialize-with=". " delimiter=", " delimiter-precedes-last="always"/>
+              <label form="short" prefix=" (" suffix=")" text-case="title"/>
+            </names>
+            <group delimiter=" ">
+              <text macro="title"/>
+              <text macro="description"/>
+              <text macro="format"/>
+            </group>
+          </substitute>
+        </names>
+      </if>
+      <else-if type="treaty"/>
+      <else>
+        <names variable="author" delimiter=", ">
+          <name name-as-sort-order="all" and="symbol" sort-separator=", " initialize-with=". " delimiter=", " delimiter-precedes-last="always"/>
+          <substitute>
+            <names variable="illustrator"/>
+            <names variable="composer"/>
+            <names variable="director">
+              <name name-as-sort-order="all" and="symbol" sort-separator=", " initialize-with=". " delimiter=", " delimiter-precedes-last="always"/>
+              <label form="long" prefix=" (" suffix=")" text-case="title"/>
+            </names>
+            <choose>
+              <if variable="container-title">
+                <choose>
+                  <if type="book entry entry-dictionary entry-encyclopedia">
+                    <text macro="title"/>
+                  </if>
+                  <else>
+                    <names variable="translator"/>
+                  </else>
+                </choose>
+                <names variable="translator">
+                  <name name-as-sort-order="all" and="symbol" sort-separator=", " initialize-with=". " delimiter=", " delimiter-precedes-last="always"/>
+                  <label form="short" prefix=" (" suffix=")" text-case="title"/>
+                </names>
+              </if>
+            </choose>
+            <names variable="editor translator" delimiter=", ">
+              <name name-as-sort-order="all" and="symbol" sort-separator=", " initialize-with=". " delimiter=", " delimiter-precedes-last="always"/>
+              <label form="short" prefix=" (" suffix=")" text-case="title"/>
+            </names>
+            <names variable="editorial-director">
+              <name name-as-sort-order="all" and="symbol" sort-separator=", " initialize-with=". " delimiter=", " delimiter-precedes-last="always"/>
+              <label form="short" prefix=" (" suffix=")" text-case="title"/>
+            </names>
+            <names variable="collection-editor">
+              <name name-as-sort-order="all" and="symbol" sort-separator=", " initialize-with=". " delimiter=", " delimiter-precedes-last="always"/>
+              <label form="short" prefix=" (" suffix=")" text-case="title"/>
+            </names>
+            <choose>
+              <if type="report">
+                <text variable="publisher"/>
+              </if>
+            </choose>
+            <group delimiter=" ">
+              <text macro="title"/>
+              <text macro="description"/>
+              <text macro="format"/>
+            </group>
+          </substitute>
+        </names>
+      </else>
+    </choose>
+  </macro>
+  <macro name="author-short">
+    <choose>
+      <if type="patent" variable="number" match="all">
+        <text macro="patent-number"/>
+      </if>
+      <else-if type="treaty">
+        <text variable="title" form="short"/>
+      </else-if>
+      <else-if type="personal_communication">
+        <choose>
+          <if variable="archive DOI publisher URL" match="none">
+            <group delimiter=", ">
+              <names variable="author">
+                <name and="symbol" delimiter=", " initialize-with=". "/>
+                <substitute>
+                  <text variable="title" form="short" quotes="true"/>
+                </substitute>
+              </names>
+              <!-- This should be localized -->
+              <text value="personal communication"/>
+            </group>
+          </if>
+          <else>
+            <names variable="author" delimiter=", ">
+              <name form="short" and="symbol" delimiter=", " initialize-with=". "/>
+              <substitute>
+                <names variable="editor"/>
+                <names variable="translator"/>
+                <choose>
+                  <if variable="container-title">
+                    <text variable="title" form="short" quotes="true"/>
+                  </if>
+                  <else>
+                    <text variable="title" form="short" font-style="italic"/>
+                  </else>
+                </choose>
+                <text macro="format-short" prefix="[" suffix="]"/>
+              </substitute>
+            </names>
+          </else>
+        </choose>
+      </else-if>
+      <else-if type="song">
+        <names variable="composer" delimiter=", ">
+          <name form="short" and="symbol" delimiter=", " initialize-with=". "/>
+          <substitute>
+            <names variable="original-author"/>
+            <names variable="author"/>
+            <names variable="translator"/>
+            <choose>
+              <if variable="container-title">
+                <text variable="title" form="short" quotes="true"/>
+              </if>
+              <else>
+                <text variable="title" form="short" font-style="italic"/>
+              </else>
+            </choose>
+            <text macro="format-short" prefix="[" suffix="]"/>
+          </substitute>
+        </names>
+      </else-if>
+      <else>
+        <names variable="author" delimiter=", ">
+          <name form="short" and="symbol" delimiter=", " initialize-with=". "/>
+          <substitute>
+            <names variable="illustrator"/>
+            <names variable="composer"/>
+            <names variable="director"/>
+            <choose>
+              <if variable="container-title">
+                <choose>
+                  <if type="book entry entry-dictionary entry-encyclopedia">
+                    <text variable="title" form="short" quotes="true"/>
+                  </if>
+                  <else>
+                    <names variable="translator"/>
+                  </else>
+                </choose>
+              </if>
+            </choose>
+            <names variable="editor"/>
+            <names variable="editorial-director"/>
+            <names variable="translator"/>
+            <choose>
+              <if type="report" variable="publisher" match="all">
+                <text variable="publisher"/>
+              </if>
+              <else-if type="legal_case">
+                <text variable="title" font-style="italic"/>
+              </else-if>
+              <else-if type="bill legislation" match="any">
+                <text variable="title" form="short"/>
+              </else-if>
+              <else-if variable="reviewed-author" type="review review-book" match="any">
+                <text macro="format-short" prefix="[" suffix="]"/>
+              </else-if>
+              <else-if type="post post-weblog webpage" variable="container-title" match="any">
+                <text variable="title" form="short" quotes="true"/>
+              </else-if>
+              <else>
+                <text variable="title" form="short" font-style="italic"/>
+              </else>
+            </choose>
+            <text macro="format-short" prefix="[" suffix="]"/>
+          </substitute>
+        </names>
+      </else>
+    </choose>
+  </macro>
+  <macro name="patent-number">
+    <!-- authority: U.S. ; genre: patent ; number: 123,445 -->
+    <group delimiter=" ">
+      <text variable="authority"/>
+      <choose>
+        <if variable="genre">
+          <text variable="genre" text-case="capitalize-first"/>
+        </if>
+        <else>
+          <!-- This should be localized -->
+          <text value="patent" text-case="capitalize-first"/>
+        </else>
+      </choose>
+      <group delimiter=" ">
+        <text term="issue" form="short" text-case="capitalize-first"/>
+        <text variable="number"/>
+      </group>
+    </group>
+  </macro>
+  <macro name="access">
+    <choose>
+      <if type="bill legal_case legislation" match="any"/>
+      <else-if variable="DOI" match="any">
+        <text variable="DOI" prefix="https://doi.org/"/>
+      </else-if>
+      <else-if variable="URL">
+        <group delimiter=" ">
+          <text term="retrieved" text-case="capitalize-first"/>
+          <choose>
+            <if type="post post-weblog webpage" match="any">
+              <date variable="accessed" form="text" suffix=","/>
+            </if>
+          </choose>
+          <text term="from"/>
+          <choose>
+            <if type="report">
+              <choose>
+                <if variable="author editor translator" match="any">
+                  <!-- This should be localized -->
+                  <text variable="publisher" suffix=" website:"/>
+                </if>
+              </choose>
+            </if>
+            <else-if type="post post-weblog webpage" match="any">
+              <!-- This should be localized -->
+              <text variable="container-title" suffix=" website:"/>
+            </else-if>
+          </choose>
+          <text variable="URL"/>
+        </group>
+      </else-if>
+      <else-if variable="archive">
+        <choose>
+          <if type="article article-journal article-magazine article-newspaper dataset paper-conference report speech thesis" match="any">
+            <!-- This section is for electronic database locations. Physical archives for these and other item types are called in 'publisher' macro -->
+            <choose>
+              <if variable="archive-place" match="none">
+                <group delimiter=" ">
+                  <text term="retrieved" text-case="capitalize-first"/>
+                  <text term="from"/>
+                  <text variable="archive" suffix="."/>
+                  <text variable="archive_location" prefix="(" suffix=")"/>
+                </group>
+              </if>
+              <else>
+                <text macro="publisher" suffix="."/>
+              </else>
+            </choose>
+          </if>
+          <else>
+            <text macro="publisher" suffix="."/>
+          </else>
+        </choose>
+      </else-if>
+      <else>
+        <text macro="publisher" suffix="."/>
+      </else>
+    </choose>
+  </macro>
+  <macro name="title">
+    <choose>
+      <if type="treaty">
+        <group delimiter=", ">
+          <text variable="title" text-case="title"/>
+          <names variable="author">
+            <name initialize-with="." form="short" delimiter="-"/>
+          </names>
+        </group>
+      </if>
+      <else-if type="patent" variable="number" match="all">
+        <text macro="patent-number" font-style="italic"/>
+      </else-if>
+      <else-if variable="title">
+        <choose>
+          <if variable="version" type="book" match="all">
+            <!---This is a hack until we have a software type -->
+            <text variable="title"/>
+          </if>
+          <else-if variable="reviewed-author reviewed-title" type="review review-book" match="any">
+            <choose>
+              <if variable="reviewed-title">
+                <choose>
+                  <if type="post post-weblog webpage" variable="container-title" match="any">
+                    <text variable="title"/>
+                  </if>
+                  <else>
+                    <text variable="title" font-style="italic"/>
+                  </else>
+                </choose>
+              </if>
+            </choose>
+          </else-if>
+          <else-if type="post post-weblog webpage" variable="container-title" match="any">
+            <text variable="title"/>
+          </else-if>
+          <else>
+            <text variable="title" font-style="italic"/>
+          </else>
+        </choose>
+      </else-if>
+      <else-if variable="interviewer" type="interview" match="any">
+        <names variable="interviewer">
+          <label form="verb-short" suffix=" " text-case="capitalize-first"/>
+          <name and="symbol" initialize-with=". " delimiter=", "/>
+        </names>
+      </else-if>
+    </choose>
+  </macro>
+  <!-- APA has four descriptive sections following the title: -->
+  <!-- (description), [format], container, event -->
+  <macro name="description">
+    <group prefix="(" suffix=")">
+      <choose>
+        <!-- book is here to catch software with container titles -->
+        <if type="book report" match="any">
+          <choose>
+            <if variable="container-title">
+              <text macro="secondary-contributors"/>
+            </if>
+            <else>
+              <group delimiter="; ">
+                <text macro="description-report"/>
+                <text macro="secondary-contributors"/>
+              </group>
+            </else>
+          </choose>
+        </if>
+        <else-if type="thesis">
+          <group delimiter="; ">
+            <group delimiter=", ">
+              <text variable="genre" text-case="capitalize-first"/>
+              <choose>
+                <!-- In APA journals, the university of a thesis is always cited, even if another locator is given -->
+                <if variable="DOI URL archive" match="any">
+                  <text variable="publisher"/>
+                </if>
+              </choose>
+            </group>
+            <text macro="locators"/>
+            <text macro="secondary-contributors"/>
+          </group>
+        </else-if>
+        <else-if type="book interview manuscript motion_picture musical_score pamphlet post-weblog speech webpage" match="any">
+          <group delimiter="; ">
+            <text macro="locators"/>
+            <text macro="secondary-contributors"/>
+          </group>
+        </else-if>
+        <else-if type="song">
+          <choose>
+            <if variable="container-title" match="none">
+              <text macro="locators"/>
+            </if>
+          </choose>
+        </else-if>
+        <else-if type="article dataset figure" match="any">
+          <choose>
+            <if variable="container-title">
+              <text macro="secondary-contributors"/>
+            </if>
+            <else>
+              <group delimiter="; ">
+                <text macro="locators"/>
+                <text macro="secondary-contributors"/>
+              </group>
+            </else>
+          </choose>
+        </else-if>
+        <else-if type="bill legislation legal_case patent treaty personal_communication" match="none">
+          <text macro="secondary-contributors"/>
+        </else-if>
+      </choose>
+    </group>
+  </macro>
+  <macro name="format">
+    <group prefix="[" suffix="]">
+      <choose>
+        <if variable="reviewed-author reviewed-title" type="review review-book" match="any">
+          <group delimiter=", ">
+            <choose>
+              <if variable="genre">
+                <!-- Delimiting by , rather than "of" to avoid incorrect grammar -->
+                <group delimiter=", ">
+                  <text variable="genre" text-case="capitalize-first"/>
+                  <choose>
+                    <if variable="reviewed-title">
+                      <text variable="reviewed-title" font-style="italic"/>
+                    </if>
+                    <else>
+                      <!-- Assume `title` is title of reviewed work -->
+                      <text variable="title" font-style="italic"/>
+                    </else>
+                  </choose>
+                </group>
+              </if>
+              <else>
+                <!-- This should be localized -->
+                <group delimiter=" ">
+                  <text value="Review of"/>
+                  <choose>
+                    <if variable="reviewed-title">
+                      <text variable="reviewed-title" font-style="italic"/>
+                    </if>
+                    <else>
+                      <!-- Assume `title` is title of reviewed work -->
+                      <text variable="title" font-style="italic"/>
+                    </else>
+                  </choose>
+                </group>
+              </else>
+            </choose>
+            <names variable="reviewed-author">
+              <label form="verb-short" suffix=" "/>
+              <name and="symbol" initialize-with=". " delimiter=", "/>
+            </names>
+          </group>
+        </if>
+        <else>
+          <text macro="format-short"/>
+        </else>
+      </choose>
+    </group>
+  </macro>
+  <macro name="format-short">
+    <choose>
+      <if variable="reviewed-author reviewed-title" type="review review-book" match="any">
+        <choose>
+          <if variable="reviewed-title" match="none">
+            <choose>
+              <if variable="genre">
+                <!-- Delimiting by , rather than "of" to avoid incorrect grammar -->
+                <group delimiter=", ">
+                  <text variable="genre" text-case="capitalize-first"/>
+                  <text variable="title" form="short" font-style="italic"/>
+                </group>
+              </if>
+              <else>
+                <!-- This should be localized -->
+                <group delimiter=" ">
+                  <text value="Review of"/>
+                  <text variable="title" form="short" font-style="italic"/>
+                </group>
+              </else>
+            </choose>
+          </if>
+          <else>
+            <text variable="title" form="short" quotes="true"/>
+          </else>
+        </choose>
+      </if>
+      <else-if type="speech thesis" match="any">
+        <text variable="medium" text-case="capitalize-first"/>
+      </else-if>
+      <!-- book is here to catch software with container titles -->
+      <else-if type="book report" match="any">
+        <choose>
+          <if variable="container-title" match="none">
+            <text macro="format-report"/>
+          </if>
+        </choose>
+      </else-if>
+      <else-if type="manuscript pamphlet" match="any">
+        <text variable="medium" text-case="capitalize-first"/>
+      </else-if>
+      <else-if type="personal_communication">
+        <text macro="secondary-contributors"/>
+      </else-if>
+      <else-if type="song">
+        <group delimiter="; ">
+          <text macro="secondary-contributors"/>
+          <choose>
+            <if variable="container-title" match="none">
+              <group delimiter=", ">
+                <text variable="genre" text-case="capitalize-first"/>
+                <text variable="medium" text-case="capitalize-first"/>
+              </group>
+            </if>
+          </choose>
+        </group>
+      </else-if>
+      <else-if type="paper-conference">
+        <group delimiter=", ">
+          <choose>
+            <if variable="collection-editor editor issue page volume" match="any">
+              <text variable="genre" text-case="capitalize-first"/>
+            </if>
+          </choose>
+          <text variable="medium" text-case="capitalize-first"/>
+        </group>
+      </else-if>
+      <else-if type="bill legislation legal_case patent treaty" match="none">
+        <choose>
+          <if variable="genre medium" match="any">
+            <group delimiter=", ">
+              <text variable="genre" text-case="capitalize-first"/>
+              <text variable="medium" text-case="capitalize-first"/>
+            </group>
+          </if>
+          <else-if type="dataset">
+            <!-- This should be localized -->
+            <text value="Data set"/>
+          </else-if>
+        </choose>
+      </else-if>
+    </choose>
+  </macro>
+  <macro name="description-report">
+    <choose>
+      <if variable="number">
+        <group delimiter="; ">
+          <group delimiter=" ">
+            <text variable="genre" text-case="title"/>
+            <!-- Replace with term="number" if that becomes available -->
+            <text term="issue" form="short" text-case="capitalize-first"/>
+            <text variable="number"/>
+          </group>
+          <text macro="locators"/>
+        </group>
+      </if>
+      <else>
+        <text macro="locators"/>
+      </else>
+    </choose>
+  </macro>
+  <macro name="format-report">
+    <choose>
+      <if variable="number">
+        <text variable="medium" text-case="capitalize-first"/>
+      </if>
+      <else>
+        <group delimiter=", ">
+          <text variable="genre" text-case="capitalize-first"/>
+          <text variable="medium" text-case="capitalize-first"/>
+        </group>
+      </else>
+    </choose>
+  </macro>
+  <macro name="archive">
+    <group delimiter=". ">
+      <group delimiter=", ">
+        <choose>
+          <if type="manuscript">
+            <text variable="genre"/>
+          </if>
+        </choose>
+        <group delimiter=" ">
+          <!-- Replace "archive" with "archive_collection" as that becomes available -->
+          <text variable="archive"/>
+          <text variable="archive_location" prefix="(" suffix=")"/>
+        </group>
+      </group>
+      <group delimiter=", ">
+        <!-- Move "archive" here when "archive_collection" becomes available -->
+        <text variable="archive-place"/>
+      </group>
+    </group>
+  </macro>
+  <macro name="publisher">
+    <choose>
+      <if type="manuscript pamphlet" match="any">
+        <choose>
+          <if variable="archive archive_location archive-place" match="any">
+            <group delimiter=". ">
+              <group delimiter=": ">
+                <text variable="publisher-place"/>
+                <text variable="publisher"/>
+              </group>
+              <text macro="archive"/>
+            </group>
+          </if>
+          <else>
+            <group delimiter=", ">
+              <text variable="genre"/>
+              <text variable="publisher"/>
+              <text variable="publisher-place"/>
+            </group>
+          </else>
+        </choose>
+      </if>
+      <else-if type="thesis" match="any">
+        <group delimiter=". ">
+          <group delimiter=", ">
+            <text variable="publisher"/>
+            <text variable="publisher-place"/>
+          </group>
+          <text macro="archive"/>
+        </group>
+      </else-if>
+      <else-if type="patent">
+        <group delimiter=". ">
+          <group delimiter=": ">
+            <text variable="publisher-place"/>
+            <text variable="publisher"/>
+          </group>
+          <text macro="archive"/>
+        </group>
+      </else-if>
+      <else-if type="article-journal article-magazine article-newspaper" match="any">
+        <text macro="archive"/>
+      </else-if>
+      <else-if type="post post-weblog webpage" match="none">
+        <group delimiter=". ">
+          <choose>
+            <if variable="event">
+              <choose>
+                <!-- Only print publisher info if published in a proceedings -->
+                <if variable="collection-editor editor issue page volume" match="any">
+                  <group delimiter=": ">
+                    <text variable="publisher-place"/>
+                    <text variable="publisher"/>
+                  </group>
+                </if>
+              </choose>
+            </if>
+            <else>
+              <group delimiter=": ">
+                <text variable="publisher-place"/>
+                <text variable="publisher"/>
+              </group>
+            </else>
+          </choose>
+          <text macro="archive"/>
+        </group>
+      </else-if>
+    </choose>
+  </macro>
+  <macro name="event">
+    <choose>
+      <if variable="event" type="speech paper-conference" match="any">
+        <choose>
+          <!-- Don't print event info if published in a proceedings -->
+          <if variable="collection-editor editor issue page volume" match="none">
+            <group delimiter=" ">
+              <text variable="genre" text-case="capitalize-first"/>
+              <group delimiter=" ">
+                <choose>
+                  <if variable="genre">
+                    <text term="presented at"/>
+                  </if>
+                  <else>
+                    <text term="presented at" text-case="capitalize-first"/>
+                  </else>
+                </choose>
+                <group delimiter=", ">
+                  <text variable="event"/>
+                  <text variable="event-place"/>
+                </group>
+              </group>
+            </group>
+          </if>
+        </choose>
+      </if>
+    </choose>
+  </macro>
+  <macro name="issued">
+    <choose>
+      <if type="bill legal_case legislation" match="any"/>
+      <else-if variable="issued">
+        <group>
+          <date variable="issued">
+            <date-part name="year"/>
+          </date>
+          <text variable="year-suffix"/>
+          <choose>
+            <if type="speech">
+              <date variable="issued" delimiter=" ">
+                <date-part prefix=", " name="month"/>
+              </date>
+            </if>
+            <else-if type="article article-magazine article-newspaper broadcast interview pamphlet personal_communication post post-weblog treaty webpage" match="any">
+              <date variable="issued">
+                <date-part prefix=", " name="month"/>
+                <date-part prefix=" " name="day"/>
+              </date>
+            </else-if>
+            <else-if type="paper-conference">
+              <choose>
+                <if variable="container-title" match="none">
+                  <date variable="issued">
+                    <date-part prefix=", " name="month"/>
+                    <date-part prefix=" " name="day"/>
+                  </date>
+                </if>
+              </choose>
+            </else-if>
+            <!-- Only year: article-journal chapter entry entry-dictionary entry-encyclopedia dataset figure graphic motion_picture manuscript map musical_score paper-conference [published] patent report review review-book song thesis -->
+          </choose>
+        </group>
+      </else-if>
+      <else-if variable="status">
+        <group>
+          <text variable="status" text-case="lowercase"/>
+          <text variable="year-suffix" prefix="-"/>
+        </group>
+      </else-if>
+      <else>
+        <group>
+          <text term="no date" form="short"/>
+          <text variable="year-suffix" prefix="-"/>
+        </group>
+      </else>
+    </choose>
+  </macro>
+  <macro name="issued-sort">
+    <choose>
+      <if type="article article-magazine article-newspaper broadcast interview pamphlet personal_communication post post-weblog speech treaty webpage" match="any">
+        <date variable="issued">
+          <date-part name="year"/>
+          <date-part name="month"/>
+          <date-part name="day"/>
+        </date>
+      </if>
+      <else>
+        <date variable="issued">
+          <date-part name="year"/>
+        </date>
+      </else>
+    </choose>
+  </macro>
+  <macro name="issued-year">
+    <group>
+      <choose>
+        <if type="personal_communication">
+          <choose>
+            <if variable="archive DOI publisher URL" match="none">
+              <!-- These variables indicate that the letter is retrievable by the reader. If not, then use the APA in-text-only personal communication format -->
+              <date variable="issued" form="text"/>
+            </if>
+            <else>
+              <date variable="issued">
+                <date-part name="year"/>
+              </date>
+            </else>
+          </choose>
+        </if>
+        <else>
+          <date variable="issued">
+            <date-part name="year"/>
+          </date>
+        </else>
+      </choose>
+      <text variable="year-suffix"/>
+    </group>
+  </macro>
+  <macro name="issued-citation">
+    <choose>
+      <if variable="issued">
+        <group delimiter="/">
+          <choose>
+            <if is-uncertain-date="original-date">
+              <group prefix="[" suffix="]" delimiter=" ">
+                <text term="circa" form="short"/>
+                <date variable="original-date">
+                  <date-part name="year"/>
+                </date>
+              </group>
+            </if>
+            <else>
+              <date variable="original-date">
+                <date-part name="year"/>
+              </date>
+            </else>
+          </choose>
+          <choose>
+            <if is-uncertain-date="issued">
+              <group prefix="[" suffix="]" delimiter=" ">
+                <text term="circa" form="short"/>
+                <text macro="issued-year"/>
+              </group>
+            </if>
+            <else>
+              <text macro="issued-year"/>
+            </else>
+          </choose>
+        </group>
+      </if>
+      <else-if variable="status">
+        <text variable="status" text-case="lowercase"/>
+        <text variable="year-suffix" prefix="-"/>
+      </else-if>
+      <else>
+        <text term="no date" form="short"/>
+        <text variable="year-suffix" prefix="-"/>
+      </else>
+    </choose>
+  </macro>
+  <macro name="original-date">
+    <choose>
+      <if type="bill legal_case legislation" match="any"/>
+      <else-if type="speech">
+        <date variable="original-date" delimiter=" ">
+          <date-part name="month"/>
+          <date-part name="year"/>
+        </date>
+      </else-if>
+      <else-if type="article article-magazine article-newspaper broadcast interview pamphlet personal_communication post post-weblog treaty webpage" match="any">
+        <date variable="original-date" form="text"/>
+      </else-if>
+      <else>
+        <date variable="original-date">
+          <date-part name="year"/>
+        </date>
+      </else>
+    </choose>
+  </macro>
+  <macro name="original-published">
+    <!--This should be localized -->
+    <choose>
+      <if type="bill legal_case legislation" match="any"/>
+      <else-if type="interview motion_picture song" match="any">
+        <text value="Original work recorded"/>
+      </else-if>
+      <else-if type="broadcast">
+        <text value="Original work broadcast"/>
+      </else-if>
+      <else>
+        <text value="Original work published"/>
+      </else>
+    </choose>
+  </macro>
+  <macro name="edition">
+    <choose>
+      <if is-numeric="edition">
+        <group delimiter=" ">
+          <number variable="edition" form="ordinal"/>
+          <text term="edition" form="short"/>
+        </group>
+      </if>
+      <else>
+        <text variable="edition"/>
+      </else>
+    </choose>
+  </macro>
+  <macro name="locators">
+    <choose>
+      <if type="article-journal article-magazine figure review review-book" match="any">
+        <group delimiter=", ">
+          <group>
+            <text variable="volume" font-style="italic"/>
+            <text variable="issue" prefix="(" suffix=")"/>
+          </group>
+          <text variable="page"/>
+        </group>
+      </if>
+      <else-if type="article-newspaper">
+        <group delimiter=" ">
+          <label variable="page" form="short"/>
+          <text variable="page"/>
+        </group>
+      </else-if>
+      <else-if type="paper-conference">
+        <choose>
+          <if variable="collection-editor editor" match="any">
+            <text macro="locators-booklike"/>
+          </if>
+          <else>
+            <group delimiter=", ">
+              <group>
+                <text variable="volume" font-style="italic"/>
+                <text variable="issue" prefix="(" suffix=")"/>
+              </group>
+              <text variable="page"/>
+            </group>
+          </else>
+        </choose>
+      </else-if>
+      <else-if type="bill broadcast interview legal_case legislation patent post post-weblog speech treaty webpage" match="none">
+        <text macro="locators-booklike"/>
+      </else-if>
+    </choose>
+  </macro>
+  <macro name="locators-booklike">
+    <group delimiter=", ">
+      <text macro="edition"/>
+      <group delimiter=" ">
+        <text term="version" text-case="capitalize-first"/>
+        <text variable="version"/>
+      </group>
+      <choose>
+        <if variable="volume" match="any">
+          <choose>
+            <if is-numeric="volume" match="none"/>
+            <else-if variable="collection-title">
+              <choose>
+                <if variable="editor translator" match="none">
+                  <choose>
+                    <if variable="collection-number">
+                      <group>
+                        <text term="volume" form="short" text-case="capitalize-first" suffix=" "/>
+                        <number variable="volume" form="numeric"/>
+                      </group>
+                    </if>
+                  </choose>
+                </if>
+              </choose>
+            </else-if>
+            <else>
+              <group>
+                <text term="volume" form="short" text-case="capitalize-first" suffix=" "/>
+                <number variable="volume" form="numeric"/>
+              </group>
+            </else>
+          </choose>
+        </if>
+        <else>
+          <group>
+            <text term="volume" form="short" plural="true" text-case="capitalize-first" suffix=" "/>
+            <number variable="number-of-volumes" form="numeric" prefix="1&#8211;"/>
+          </group>
+        </else>
+      </choose>
+      <group>
+        <label variable="page" form="short" suffix=" "/>
+        <text variable="page"/>
+      </group>
+    </group>
+  </macro>
+  <macro name="citation-locator">
+    <group>
+      <choose>
+        <if locator="chapter">
+          <label variable="locator" text-case="capitalize-first"/>
+        </if>
+        <else>
+          <label variable="locator" form="short"/>
+        </else>
+      </choose>
+      <text variable="locator" prefix=" "/>
+    </group>
+  </macro>
+  <macro name="container">
+    <choose>
+      <if type="article article-journal article-magazine article-newspaper review review-book" match="any">
+        <group delimiter=", ">
+          <text macro="container-title"/>
+          <text macro="locators"/>
+        </group>
+        <choose>
+          <!--for advance online publication-->
+          <if variable="issued">
+            <choose>
+              <if variable="page issue" match="none">
+                <text variable="status" text-case="capitalize-first" prefix=". "/>
+              </if>
+            </choose>
+          </if>
+        </choose>
+      </if>
+      <else-if type="article dataset figure" match="any">
+        <choose>
+          <if variable="container-title">
+            <group delimiter=", ">
+              <text macro="container-title"/>
+              <text macro="locators"/>
+            </group>
+            <choose>
+              <!--for advance online publication-->
+              <if variable="issued">
+                <choose>
+                  <if variable="page issue" match="none">
+                    <text variable="status" text-case="capitalize-first" prefix=". "/>
+                  </if>
+                </choose>
+              </if>
+            </choose>
+          </if>
+        </choose>
+      </else-if>
+      <!-- book is here to catch software with container titles -->
+      <else-if type="book" variable="container-title" match="all">
+        <group delimiter=" ">
+          <text term="in" text-case="capitalize-first" suffix=" "/>
+          <group delimiter=", ">
+            <text macro="container-contributors"/>
+            <group delimiter=" ">
+              <text macro="container-title"/>
+              <text macro="description-report" prefix="(" suffix=")"/>
+              <text macro="format-report" prefix="[" suffix="]"/>
+            </group>
+          </group>
+        </group>
+      </else-if>
+      <else-if type="report" variable="container-title" match="all">
+        <group delimiter=" ">
+          <text term="in" text-case="capitalize-first" suffix=" "/>
+          <group delimiter=", ">
+            <text macro="container-contributors"/>
+            <group delimiter=" ">
+              <text macro="container-title"/>
+              <text macro="description-report" prefix="(" suffix=")"/>
+              <text macro="format-report" prefix="[" suffix="]"/>
+            </group>
+          </group>
+        </group>
+      </else-if>
+      <else-if type="song" variable="container-title" match="all">
+        <group delimiter=" ">
+          <text term="in" text-case="capitalize-first" suffix=" "/>
+          <group delimiter=", ">
+            <text macro="container-contributors"/>
+            <group delimiter=" ">
+              <text macro="container-title"/>
+              <text macro="locators" prefix="(" suffix=")"/>
+              <group delimiter=", " prefix="[" suffix="]">
+                <text variable="genre" text-case="capitalize-first"/>
+                <text variable="medium" text-case="capitalize-first"/>
+              </group>
+            </group>
+          </group>
+        </group>
+      </else-if>
+      <else-if type="paper-conference">
+        <choose>
+          <if variable="editor collection-editor container-author" match="any">
+            <text macro="container-booklike"/>
+          </if>
+          <else>
+            <group delimiter=", ">
+              <text macro="container-title"/>
+              <text macro="locators"/>
+            </group>
+          </else>
+        </choose>
+      </else-if>
+      <else-if type="book">
+        <choose>
+          <!-- book and software should not cite collection-title, only container-title -->
+          <if variable="container-title">
+            <text macro="container-booklike"/>
+          </if>
+        </choose>
+      </else-if>
+      <else-if type="broadcast chapter entry entry-dictionary entry-encyclopedia graphic map speech" match="any">
+        <text macro="container-booklike"/>
+      </else-if>
+      <else-if type="bill legal_case legislation treaty" match="any">
+        <text macro="legal-cites"/>
+      </else-if>
+    </choose>
+  </macro>
+  <macro name="container-booklike">
+    <choose>
+      <if variable="container-title collection-title" match="any">
+        <group delimiter=" ">
+          <text term="in" text-case="capitalize-first"/>
+          <group delimiter=", ">
+            <text macro="container-contributors"/>
+            <choose>
+              <if variable="container-author editor translator" match="none">
+                <group delimiter=". ">
+                  <group delimiter=": ">
+                    <text variable="collection-title" font-style="italic" text-case="title"/>
+                    <choose>
+                      <if variable="collection-title">
+                        <group delimiter=" ">
+                          <text term="volume" form="short" font-style="italic" text-case="capitalize-first"/>
+                          <number variable="collection-number" font-style="italic" form="numeric"/>
+                          <choose>
+                            <if variable="collection-number" match="none">
+                              <number variable="volume" font-style="italic" form="numeric"/>
+                            </if>
+                          </choose>
+                        </group>
+                      </if>
+                    </choose>
+                  </group>
+                  <!-- Replace with volume-title as that becomes available -->
+                  <group delimiter=": ">
+                    <text macro="container-title"/>
+                    <choose>
+                      <if variable="collection-title" is-numeric="volume" match="none">
+                        <group delimiter=" ">
+                          <text term="volume" form="short" font-style="italic" text-case="capitalize-first"/>
+                          <text variable="volume" font-style="italic"/>
+                        </group>
+                      </if>
+                    </choose>
+                  </group>
+                </group>
+              </if>
+              <else>
+                <!-- Replace with volume-title as that becomes available -->
+                <group delimiter=": ">
+                  <text macro="container-title"/>
+                  <choose>
+                    <if is-numeric="volume" match="none">
+                      <group delimiter=" ">
+                        <text term="volume" form="short" font-style="italic" text-case="capitalize-first"/>
+                        <text variable="volume" font-style="italic"/>
+                      </group>
+                    </if>
+                  </choose>
+                </group>
+              </else>
+            </choose>
+          </group>
+          <group delimiter="; " prefix="(" suffix=")">
+            <text macro="locators"/>
+            <names variable="container-author">
+              <label form="verb-short" suffix=" " text-case="title"/>
+              <name and="symbol" initialize-with=". " delimiter=", "/>
+            </names>
+          </group>
+        </group>
+      </if>
+    </choose>
+  </macro>
+  <macro name="container-title">
+    <choose>
+      <if type="article article-journal article-magazine article-newspaper dataset" match="any">
+        <text variable="container-title" font-style="italic" text-case="title"/>
+      </if>
+      <else-if type="paper-conference speech">
+        <choose>
+          <if variable="collection-editor container-author editor" match="any">
+            <text variable="container-title" font-style="italic"/>
+          </if>
+          <else>
+            <text variable="container-title" font-style="italic" text-case="title"/>
+          </else>
+        </choose>
+      </else-if>
+      <else-if type="bill legal_case legislation post-weblog webpage" match="none">
+        <text variable="container-title" font-style="italic"/>
+      </else-if>
+    </choose>
+  </macro>
+  <macro name="legal-cites">
+    <choose>
+      <if type="legal_case">
+        <group prefix=", " delimiter=" ">
+          <group delimiter=" ">
+            <choose>
+              <if variable="container-title">
+                <text variable="volume"/>
+                <text variable="container-title"/>
+                <group delimiter=" ">
+                  <!--change to label variable="section" as that becomes available -->
+                  <text term="section" form="symbol"/>
+                  <text variable="section"/>
+                </group>
+                <text variable="page"/>
+              </if>
+              <else>
+                <group delimiter=" ">
+                  <choose>
+                    <if is-numeric="number">
+                      <!-- Replace with term="number" if that becomes available -->
+                      <text term="issue" form="short" text-case="capitalize-first"/>
+                    </if>
+                  </choose>
+                  <text variable="number"/>
+                </group>
+              </else>
+            </choose>
+          </group>
+          <group prefix="(" suffix=")" delimiter=" ">
+            <text variable="authority"/>
+            <choose>
+              <if variable="container-title" match="any">
+                <!--Only print year for cases published in reporters-->
+                <date variable="issued" form="numeric" date-parts="year"/>
+              </if>
+              <else>
+                <date variable="issued" form="text"/>
+              </else>
+            </choose>
+          </group>
+        </group>
+      </if>
+      <else-if type="bill legislation" match="any">
+        <group prefix=", " delimiter=" ">
+          <group delimiter=", ">
+            <choose>
+              <if variable="number">
+                <!--There's a public law number-->
+                <text variable="number" prefix="Pub. L. No. "/>
+                <group delimiter=" ">
+                  <!--change to label variable="section" as that becomes available -->
+                  <text term="section" form="symbol"/>
+                  <text variable="section"/>
+                </group>
+                <group delimiter=" ">
+                  <text variable="volume"/>
+                  <text variable="container-title"/>
+                  <text variable="page-first"/>
+                </group>
+              </if>
+              <else>
+                <group delimiter=" ">
+                  <text variable="volume"/>
+                  <text variable="container-title"/>
+                  <!--change to label variable="section" as that becomes available -->
+                  <text term="section" form="symbol"/>
+                  <text variable="section"/>
+                </group>
+              </else>
+            </choose>
+          </group>
+          <date variable="issued" prefix="(" suffix=")">
+            <date-part name="year"/>
+          </date>
+        </group>
+      </else-if>
+      <else-if type="treaty">
+        <group delimiter=" ">
+          <number variable="volume"/>
+          <text variable="container-title"/>
+          <text variable="page"/>
+        </group>
+      </else-if>
+    </choose>
+  </macro>
+  <citation et-al-min="6" et-al-use-first="1" et-al-subsequent-min="3" et-al-subsequent-use-first="1" disambiguate-add-year-suffix="true" disambiguate-add-names="true" disambiguate-add-givenname="true" collapse="year" givenname-disambiguation-rule="primary-name">
+    <sort>
+      <key macro="author" names-min="8" names-use-first="6"/>
+      <key macro="issued-sort"/>
+    </sort>
+    <layout prefix="(" suffix=")" delimiter="; ">
+      <group delimiter=", ">
+        <text macro="author-short"/>
+        <text macro="issued-citation"/>
+        <text macro="citation-locator"/>
+      </group>
+    </layout>
+  </citation>
+  <bibliography hanging-indent="true" et-al-min="8" et-al-use-first="6" et-al-use-last="true" entry-spacing="0" line-spacing="2">
+    <sort>
+      <key macro="author"/>
+      <key macro="issued-sort" sort="ascending"/>
+      <key macro="title"/>
+    </sort>
+    <layout>
+      <group suffix=".">
+        <group delimiter=". ">
+          <text macro="author"/>
+          <choose>
+            <if is-uncertain-date="issued">
+              <group prefix=" [" suffix="]" delimiter=" ">
+                <text term="circa" form="short"/>
+                <text macro="issued"/>
+              </group>
+            </if>
+            <else>
+              <text macro="issued" prefix=" (" suffix=")"/>
+            </else>
+          </choose>
+          <group delimiter=" ">
+            <text macro="title"/>
+            <choose>
+              <if variable="title interviewer" type="interview" match="any">
+                <group delimiter=" ">
+                  <text macro="description"/>
+                  <text macro="format"/>
+                </group>
+              </if>
+              <else>
+                <group delimiter=" ">
+                  <text macro="format"/>
+                  <text macro="description"/>
+                </group>
+              </else>
+            </choose>
+          </group>
+          <text macro="container"/>
+        </group>
+        <text macro="event" prefix=". "/>
+      </group>
+      <text macro="access" prefix=" "/>
+      <choose>
+        <if is-uncertain-date="original-date">
+          <group prefix=" [" suffix="]" delimiter=" ">
+            <text macro="original-published"/>
+            <text term="circa" form="short"/>
+            <text macro="original-date"/>
+          </group>
+        </if>
+        <else-if variable="original-date">
+          <group prefix=" (" suffix=")" delimiter=" ">
+            <text macro="original-published"/>
+            <text macro="original-date"/>
+          </group>
+        </else-if>
+      </choose>
+    </layout>
+  </bibliography>
+</style>
diff --git a/vignettes/lines_gomex/mexdolph.bib b/vignettes/lines_gomex/mexdolph.bib
index 85047df..49e8a91 100644
--- a/vignettes/lines_gomex/mexdolph.bib
+++ b/vignettes/lines_gomex/mexdolph.bib
@@ -38,7 +38,7 @@ @article{hedley_spatial_2004
 @book{wood_generalized_2017,
 	edition = {2nd},
 	series = {Texts in Statistical Science},
-	title = {Generalized Additive Models. An Introduction with R},
+	title = {Generalized Additive Models. An Introduction with {R}},
 	publisher = {CRC Press},
 	author = {Wood, S. N.},
 	year = {2017}
@@ -62,19 +62,24 @@ @article{wood_soap_2008
 	file = {Wood et al. - 2008 - Soap film smoothing.pdf:/Users/dill/Zotero/storage/U7WQB7WY/Wood et al. - 2008 - Soap film smoothing.pdf:application/pdf}
 }
 
-@article{bravington_reliable_2018,
-	title = {Reliable variance propagation for spatial density surface models},
-	abstract = {Density Surface Models (DSMs) are two-stage models for estimating animal density from line-transect data. First, detectability is estimated by distance sampling, and then a Generalized Additive Model (GAM) is used to estimate animal density as a function of location and/or other spatially-varying environmental covariates. One criticism of DSMs has been that uncertainty from the two stages is not usually propagated correctly into the final variance estimates. We show how to reformulate a DSM so that the uncertainty in detection probability from the distance sampling stage (regardless of its complexity) is captured as a random effect in the GAM stage. This random effect acts as a quadratic approximation to the detection probability centred at the point estimate of detectability, effectively fitting the detection function at the same time as the spatial model. This allows straightforward computation of the overall variance via standard GAM machinery. A further extension allows for spatial variation in group size, which can be an important covariate for detectability. We illustrate these models using line transect survey data of minke whales and harbour porpoise from a visual line transect survey of European waters.},
-	language = {en},
-	author = {Bravington, Mark V and Miller, David L and Hedley, Sharon L},
-	year = {2018},
-	pages = {11},
-	file = {Bravington - 2018 - Reliable variance propagation for spatial density .pdf:/Users/dill/Zotero/storage/B3JMA9BQ/Bravington - 2018 - Reliable variance propagation for spatial density .pdf:application/pdf}
+@article{bravington_variance_2021,
+  title = {Variance {{Propagation}} for {{Density Surface Models}}},
+  author = {Bravington, Mark V. and Miller, David L. and Hedley, Sharon L.},
+  year = {2021},
+  volume = {26},
+  pages = {306--323},
+  month = feb,
+  journal = {Journal of Agricultural, Biological and Environmental Statistics},
+  issn = {1537-2693},
+  doi = {10.1007/s13253-021-00438-2},
+  urldate = {2021-02-24},
+  abstract = {Spatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density surface models (DSMs) are a two-stage approach for estimating spatially varying density from distance sampling data. First, detection probabilities---perhaps depending on covariates---are estimated based on details of individual encounters; next, local densities are estimated using a GAM, by fitting local encounter rates to location and/or spatially varying covariates while allowing for the estimated detectabilities. One criticism of DSMs has been that uncertainty from the two stages is not usually propagated correctly into the final variance estimates. We show how to reformulate a DSM so that the uncertainty in detection probability from the distance sampling stage (regardless of its complexity) is captured as an extra random effect in the GAM stage. In effect, we refit an approximation to the detection function model at the same time as fitting the spatial model. This allows straightforward computation of the overall variance via exactly the same software already needed to fit the GAM. A further extension allows for spatial variation in group size, which can be an important covariate for detectability as well as directly affecting abundance. We illustrate these models using point transect survey data of Island Scrub-Jays on Santa Cruz Island, CA, and harbour porpoise from the SCANS-II line transect survey of European waters. Supplementary materials accompanying this paper appear on-line.},
 }
 
+
 @book{bivand_applied_2008,
 	series = {Use R!},
-	title = {Applied Spatial Data Analysis with R},
+	title = {Applied Spatial Data Analysis with {R}},
 	isbn = {978-0-387-78171-6},
 	url = {https://books.google.co.uk/books?id=4gg8yx_lcj0C},
 	publisher = {Springer New York},
diff --git a/vignettes/lines_gomex/mexico-analysis.Rmd b/vignettes/lines_gomex/mexico-analysis.Rmd
index 87db0be..fee7f88 100644
--- a/vignettes/lines_gomex/mexico-analysis.Rmd
+++ b/vignettes/lines_gomex/mexico-analysis.Rmd
@@ -7,13 +7,21 @@ author:
     url: http://distancesampling.org
     affiliation: CREEM, Univ of St Andrews
     affiliation_url: https://creem.st-andrews.ac.uk
-date: "`r Sys.Date()`"
+date: "`r format(Sys.time(), '%B %Y')`"
 output: 
-  distill::distill_article:
+  bookdown::html_document2:
+    number_sections: false
     toc: true
     toc_depth: 2
+    base_format: rmarkdown::html_vignette
+pkgdown:
+  as_is: true  
 bibliography: mexdolph.bib
-csl: apa.csl
+csl: ../apa.csl
+vignette: >
+  %\VignetteIndexEntry{Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico}
+  %\VignetteEngine{knitr::rmarkdown}
+  \usepackage[utf8]{inputenc}
 ---
 
 ```{r echo=FALSE}
@@ -109,19 +117,39 @@ Using latitude and longitude when performing spatial smoothing can be problemati
 We give an example of projecting the polygon that defines the survey area (which as simply been read into R using `readShapeSpatial` from a shapefile produced by GIS).
 
 ```{r projectsurvey, results='hide', message=FALSE}
-library(rgdal)
-library(maptools)
+library(sf)
 library(plyr)
 
 # tell R that the survey.area object is currently in lat/long
-proj4string(survey.area) <- CRS("+proj=longlat +datum=WGS84")
+sp::proj4string(survey.area) <- sp::CRS("+proj=longlat +datum=WGS84")
 
+predsf <- st_as_sf(pred.polys)
+
+area.sf <- st_as_sf(survey.area)
+st_crs(area.sf) <- "WGS84"
+area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))
+
+# Convert preddata to a spatial object
+preddata_sf <- st_as_sf(preddata, coords=c("x", "y"))
+st_crs(preddata_sf) <- st_crs(area.sf.proj)
+# Perform the intersection
+preddata_sf <- st_intersection(preddata_sf, area.sf.proj)
+
+coords_preddata <- data.frame(st_coordinates(preddata_sf))
+
+preddata_sf$x <- coords_preddata$X
+preddata_sf$y <- coords_preddata$Y 
+```
+
+Next bit of code works with the `study.area` outline, massaging it for use with soap film smoother. If you will not be using soap film smoothers, the code is extraneous.
+
+```{r studyareasoap}
 # proj 4 string
 # using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/
-lcc_proj4 <- CRS("+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
+lcc_proj4 <- sp::CRS("+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
 
 # project using LCC
-survey.area <- spTransform(survey.area, CRSobj=lcc_proj4)
+survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)
 
 # simplify the object
 survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords)
@@ -130,64 +158,28 @@ names(survey.area) <- c("x", "y")
 
 The below code generates this plot, which shows the survey area with the transect lines overlaid (using data from `segdata`).
 
-```{r areawithtransects, fig.cap="", fig.height=4}
-p <- qplot(data=survey.area, x=x, y=y, geom="polygon",fill=I("lightblue"),
-ylab="y", xlab="x", alpha=I(0.7))
-p <- p + coord_equal()
-p <- p + geom_line(aes(x,y,group=Transect.Label),data=segdata)
-p <- p + gg.opts
-
-print(p)
+```{r areawithtransects, fig.cap="", fig.dim=c(7,5)}
+segdata_sf <- st_as_sf(segdata, coords = c("x","y"))
+st_crs(segdata_sf) <- st_crs(area.sf.proj)
+# study area outline and segment centres
+plot_segments <- ggplot() +
+  geom_sf(data = area.sf.proj, fill="lightblue", color = "blue", linewidth=.1) +
+  geom_sf(data=segdata_sf, fill=NA, color="black", linewidth=.3) +
+  labs(title="1996 SE Fisheries Science Center Gulf of Mexico cruise",
+       subtitle = "Points are segment centres") +
+  scale_fill_viridis_c(option = "magma", guide = "none")
+plot_segments
 ```
 
-Also note that since we've projected our prediction grid, the "squares" don't look quite like squares. So for plotting we'll use the polygons that we've saved, these polygons (stored in `pred.polys`) are read from a shapefile created in GIS, the object itself is of class `SpatialPolygons` from the `sp` package. This plotting method makes plotting take a little longer, but avoids gaps and overplotting. The following plot compares using latitude/longitude (left) with a projection (right).
-
-```{r projection-compare, fig.cap="", fig.height=3}
-par(mfrow=c(1,2))
-
-# put pred.polys into lat/long
-pred_latlong <- spTransform(pred.polys,CRSobj=CRS("+proj=longlat +datum=WGS84"))
-
-# plot latlong
-plot(pred_latlong, xlab="Longitude", ylab="Latitude")
-axis(1); axis(2); box()
+Also note that since we have projected our prediction grid, the "squares" do not look quite like squares. So for plotting we will use the polygons that we have saved, these polygons (stored in `pred.polys`) are read from a shapefile created in GIS, the object itself is of class `SpatialPolygons` from the `sp` package. It needs project for proper plotting using functions in the `sf` package. predsf <- st_as_sf(pred.polys)
 
+```{r projection-compare, fig.cap="", fig.dim=c(7,5)}
+predsf <- st_as_sf(pred.polys)
 # plot as projected
-plot(pred.polys, xlab="Northing", ylab="Easting")
-axis(1); axis(2); box()
-```
-
-
-Tips on plotting polygons are available from [the `ggplot2` wiki](https://github.com/hadley/ggplot2/wiki/plotting-polygon-shapefiles).
-
-Here we define a convenience function to generate an appropriate data structure for `ggplot2` to plot:
-
-```{r ggpoly}
-# given the argument fill (the covariate vector to use as the fill) and a name,
-# return a geom_polygon object
-# fill must be in the same order as the polygon data
-grid_plot_obj <- function(fill, name, sp){
-
-  # what was the data supplied?
-  names(fill) <- NULL
-  row.names(fill) <- NULL
-  data <- data.frame(fill)
-  names(data) <- name
-
-  spdf <- SpatialPolygonsDataFrame(sp, data)
-  spdf@data$id <- rownames(spdf@data)
-  spdf.points <- fortify(spdf, region="id")
-  spdf.df <- join(spdf.points, spdf@data, by="id")
-
-  # seems to store the x/y even when projected as labelled as
-  # "long" and "lat"
-  spdf.df$x <- spdf.df$long
-  spdf.df$y <- spdf.df$lat
-
-  geom_polygon(aes_string(x="x",y="y",fill=name, group="group"), data=spdf.df)
-}
+plot(st_geometry(predsf), axes=TRUE)
 ```
 
+Tips on plotting polygons are available from [the `ggplot2` pkg_down](https://ggplot2.tidyverse.org/reference/ggsf.html?q=geom_sf#null).
 
 # Exploratory data analysis
 
@@ -197,7 +189,7 @@ The top panels of the EDA plots below show histograms of observed distances and
 
 The following code generates the EDA plots:
 
-```{r EDA-plots, fig.height=7, fig.width=7, fig.cap="", results="hide"}
+```{r EDA-plots, fig.height=6, fig.width=6, fig.cap="", results="hide"}
 par(mfrow=c(2,2))
 
 # histograms
@@ -219,26 +211,30 @@ plot(distdata$distance,distdata$beaufort, main="", xlab="Distance (m)",
 
 Top row, left to right: histograms of distance and cluster size; bottom row: plot of distance against cluster size and plot of distances against Beaufort sea state.
 
-
 ## Spatial data
 
 Looking separately at the spatial data without thinking about the distances, we can plot the observed group sizes in space (point size is proportional to the group size for each observation). Circle size indicates the size of the group in the observation. There are rather large areas with no observations, which might cause our variance estimates for abundance to be rather large. We also see the depth data which we will use depth later as an explanatory covariate in our spatial model.
 
-```{r spatialEDA, fig.cap=""}
-p <- ggplot() + grid_plot_obj(preddata$depth, "Depth", pred.polys) + coord_equal()
-p <- p + labs(fill="Depth",x="x",y="y",size="Group size")
-p <- p + geom_line(aes(x, y, group=Transect.Label), data=segdata)
-p <- p + geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.7))
-p <- p + gg.opts
-print(p)
-```
+```{r spatialEDA, fig.cap="", fig.dim=c(7,5)}
+prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
 
+depth <- ggplot() +
+         geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+         labs(title = "Spotted dolphins, Gulf of Mexico",
+              subtitle = "Depth in meters, size of detected dolphin groups") +
+         xlab("Longitude") + ylab("Latitude") +
+         geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
+         scale_fill_viridis_c(option = "viridis", direction = 1)
+depth
+```
 
 # Estimating the detection function
 
 We use the `ds` function in the package `Distance` to fit the detection function. (The `Distance` package is intended to make standard distance sampling in `R` relatively straightforward. For a more flexible but more complex alternative, see the function `ddf` in the `mrds` library.)
 
-
 First, loading the `Distance` library:
 
 ```{r loadDistance}
@@ -257,18 +253,15 @@ Calling `summary` gives us information about parameter estimates, probability of
 summary(detfc.hr.null)
 ```
 
-
 The following code generates a plot of the fitted detection function (left) and quantile-quantile plot (right):
 
-``` {r hr-detfct, fig.cap="", fig.width=9, fig.height=6, results="hide"}
-layout(matrix(c(1, 2), 1, 2), width=c(1.5, 1))
+``` {r hr-detfct, fig.cap="", fig.lim=c(6,9), fig.show="hold"}
 plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2)
 gof_ds(detfc.hr.null)
 ```
 
 The quantile-quantile plot show relatively good goodness of fit for the hazard-rate detection function.
 
-
 ## Adding covariates to the detection function
 
 It is common to include covariates in the detection function (so-called Multiple Covariate Distance Sampling or MCDS). In this dataset there are two covariates that were collected on each individual: Beaufort sea state and size. For brevity we fit only a hazard-rate detection functions with the sea state included as a factor covariate as follows:
@@ -288,12 +281,10 @@ Here the detection function with covariates does not give a lower AIC than the m
 
 For brevity, detection function model selection has been omitted here. In practise we would fit many different forms for the detection function (and select a model based on goodness of fit testing and AIC).
 
-
 # Fitting a DSM
 
 Before fitting a `dsm` model, the data must be segmented; this consists of chopping up the transects and attributing counts to each of the segments. As mentioned above, these data have already been segmented.
 
-
 ## A simple model
 
 We begin with a very simple model. We assume that the number of individuals in each segment are quasi-Poisson distributed and that they are a smooth function of their spatial coordinates (note that the formula is exactly as one would specify to `gam` in `mgcv`). By setting `group=TRUE`, the abundance of clusters/groups rather than individuals can be estimated (though we ignore this here). Note we set `method="REML"` to ensure that smooth terms are estimated reliably.
@@ -317,7 +308,7 @@ We can use the deviance explained to compare between models[^rsqoffset].
 
 We can also get a rough idea of what the smooth of space looks like using `vis.gam` (white/yellow indicates high values, red low indicates low values):
 
-```{r visgam1, fig.cap=''}
+```{r visgam1, fig.cap='', fig.dim=c(7,5)}
 vis.gam(dsm.xy, plot.type="contour", view=c("x","y"), asp=1, type="response", contour.col="black", n.grid=100)
 ```
 
@@ -338,7 +329,7 @@ Setting `select=TRUE` here (as an argument to `gam`) would impose extra shrinkag
 
 Simply calling `plot` on the model object allows us to look at the relationship between depth and the linear predictor:
 
-```{r dsm-xy-depth-depth, fig.cap=""}
+```{r dsm-xy-depth-depth, fig.cap="", fig.dim=c(7,5)}
 plot(dsm.xy.depth, select=2)
 ```
 
@@ -361,14 +352,13 @@ summary(dsm.est.xy)
 
 As is the resulting spatial smooth (though the resulting surface is somewhat "amplified"):
 
-```{r visgam5, fig.cap=""}
+```{r visgam5, fig.cap="", fig.dim=c(7,5)}
 vis.gam(dsm.est.xy, plot.type="contour", view=c("x","y"), asp=1, type="response", zlim=c(0, 300), contour.col="black", n.grid=100)
 ```
 
-
 ## Other response distributions
 
-Often the quasi-Poisson distribution doesn't give adequate flexibility and doesn't capture the overdispersion in the response data (see [Model checking] and [Model selection] below), so below we illustrate two additional distributions that can be used with count data.
+Often the quasi-Poisson distribution doesn't give adequate flexibility and does not capture the overdispersion in the response data (see [Model checking] and [Model selection] below), so below we illustrate two additional distributions that can be used with count data.
 
 For the models in this section, we'll move back to the `count` response, though the estimated abundance would also work.
 
@@ -381,8 +371,6 @@ dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw()
 summary(dsm.xy.tweedie)
 ```
 
-
-
 ### Negative binomial
 
 Though not used here there are, similarly, two options for the negative binomial distribution: `negbin` and `nb`. The former requires the user specification single parameter `theta` or a range of values for the parameter (specified as a vector), the latter estimates the value of `theta` during the model fitting process (and is generally faster). The latter is recommended for most users.
@@ -424,14 +412,13 @@ dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs="so", k=15, xt=list(bnd=list(survey.ar
 summary(dsm.xy.tweedie.soap)
 ```
 
-
 # Model checking
 
 Fitting models is all well and good, but we'd like to confirm that the models we have are reasonable; `dsm` provides some functions for model checking.
 
 We can use `gam.check` to generate diagnostic plots:
 
-```{r dsm.xy-check, fig.cap="", fig.width=6, fig.height=6}
+```{r dsm.xy-check, fig.cap="", fig.dim=c(7,5)}
 gam.check(dsm.xy)
 ```
 
@@ -448,7 +435,7 @@ The `?choose.k` manual page from `mgcv` gives further guidance and technical det
 
 We can look at the same model form but with a Tweedie distribution specified as the response:
 
-``` {r dsm.xy.tweedie-check, fig.cap="", fig.width=6, fig.height=6}
+``` {r dsm.xy.tweedie-check, fig.cap="", fig.dim=c(7,5)}
 gam.check(dsm.xy.tweedie)
 ```
 
@@ -462,20 +449,19 @@ In the top right panel of the above `gam.check` plots the residuals vs. linear p
 
 Randomised quantile residuals [@dunn_randomized_1996] avoid this issue by transforming the residuals to be exactly normally distributed. This makes the residuals vs. linear predictor plot much easier to interpret as it therefore doesn't include the artifacts generated by the link function. These plots can be produced using `rqgam.check` in `dsm`:
 
-``` {r dsm.xy.tweedie-rqcheck, fig.cap="", fig.width=6, fig.height=6}
-rqgam.check(dsm.xy.tweedie)
+``` {r dsm.xy.tweedie-rqcheck, fig.cap="", fig.dim=c(7,5)}
+rqgam_check(dsm.xy.tweedie)
 ```
 
 Here we can see that there is no issue with heteroskedasticity (no increase in spread in the residuals vs. linear predictor plot with increasing values of the linear predictor). One can also plot these residuals against covariate values to check for pattern in the residuals.
 
 Note that in general, plots other than "Resids vs. linear pred." should be interpreted with caution in the output of `rqgam.check` as the residuals generated are normal by construction (so for example the Q-Q plot and histogram of residuals will always look fine).
 
-
 # Model selection
 
 Assuming that models have "passed" the checks in `gam.check`, `rqgam.check` and are sufficiently flexible, we may be left with a choice of which model is "best". There are several methods for choosing the best model -- AIC, REML/GCV scores, deviance explained, full cross-validation with test data and so on.
 
-Though this document doesn't intend to be a full analysis of the pantropical dolphin data, we can create a results table to compare the various models that have been fitted so far in terms of their abundance estimates and associated uncertainties.
+Though this document does not intend to be a full analysis of the pantropical dolphin data, we can create a results table to compare the various models that have been fitted so far in terms of their abundance estimates and associated uncertainties.
 
 ```{r modelcomp, cache=TRUE}
 # make a data.frame to print out
@@ -509,13 +495,24 @@ Once a model has been checked and selected, we can make predictions over the gri
 dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area)
 ```
 
-We use the `grid_plot_obj` helper function to assign the predictions to grid cells (polygons):
+Given predicted abundance estimates for each cell in the prediction grid, these are connected to the prediction grid object for mapping:
 
-```{r dsm.xy-preds, fig.cap=""}
-p <- ggplot() + grid_plot_obj(dsm.xy.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
-p <- p + geom_path(aes(x=x, y=y),data=survey.area)
-p <- p + labs(fill="Abundance")
-print(p)
+```{r dsm.xy-preds, fig.cap="", fig.dim=c(7,5)}
+prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+
+preddata_sf$Prediction_xy <- dsm.xy.pred
+
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+
+pred <- ggplot() +
+  geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +
+  geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
+  labs(title="Spotted dolphins, Gulf of Mexico, abundance estimates",
+       subtitle = "Bivariate smooth of location, quasipoisson") +
+  scale_fill_viridis_c(option = "viridis", direction = 1)
+pred
 ```
 
 We can calculate abundance over the survey area by simply summing these predictions:
@@ -526,12 +523,21 @@ sum(dsm.xy.pred)
 
 We can compare this with a plot of the predictions from this `dsm.xy.depth`:
 
-```{r dsm.xy.depth-preds, fig.cap=""}
+```{r dsm.xy.depth-preds, fig.cap="", fig.dim=c(7,5)}
 dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)
-p <- ggplot() + grid_plot_obj(dsm.xy.depth.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
-p <- p + geom_path(aes(x=x, y=y), data=survey.area)
-p <- p + labs(fill="Abundance")
-print(p)
+
+preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred
+
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+
+pred <- ggplot() +
+  geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +
+  geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
+  labs(title="Spotted dolphins, Gulf of Mexico, abundance estimates",
+       subtitle = "Bivariate smooth of location, smooth of depth, quasipoisson") +
+  scale_fill_viridis_c(option = "viridis", direction = 1)
+pred
 ```
 
 We can see the inclusion of depth into the model has had a noticeable effect on the distribution (note the difference in legend scale between the two plots). We can again also look at the total abundance:
@@ -548,16 +554,16 @@ Obviously point estimates of abundance are important, but we should also calcula
 
 There are two approaches to estimating the uncertainty of an abundance estimate in `dsm`.
 
-- `dsm.var.gam` which assumes that the spatial model and detection function parts of the model are independent of each other. In this case the squared coefficients of variation for each model component are added.
-- `dsm.var.prop` which takes into account the fact that detection probability may be correlated with the spatial part of the model. It uses methods described in [@bravington_reliable_2018].
+- `dsm_var_gam` which assumes that the spatial model and detection function parts of the model are independent of each other. In this case the squared coefficients of variation for each model component are added.
+- `dsm_var_prop` which takes into account the fact that detection probability may be correlated with the spatial part of the model. It uses methods described in [@bravington_variance_2021].
 
-`dsm.var.prop` can only be applied when there is a covariate in the detection function that varies at the level of the segments, and is recorded at each segment (for example Beaufort). We don't have that situation here, so we opt for `dsm.var.gam`.
+`dsm_var_prop` can only be applied when there is a covariate in the detection function that varies at the level of the segments, and is recorded at each segment (for example Beaufort). We don't have that situation here, so we opt for `dsm_var_gam`.
 
- Both methods estimate the variance of the abundance for each element in the list provided in `pred.data`. In our case we wish to obtain an abundance for each of the prediction cells, so we use `split` to chop our data set into list elements to give to `dsm.var.gam` (or `dsm.var.prop`).
+ Both methods estimate the variance of the abundance for each element in the list provided in `pred.data`. In our case we wish to obtain an abundance for each of the prediction cells, so we use `split` to chop our data set into list elements to give to `dsm_var_gam` (or `dsm_var_prop`).
 
 ```{r dsm.xy-var, cache=TRUE}
 preddata.var <- split(preddata, 1:nrow(preddata))
-dsm.xy.var <- dsm.var.gam(dsm.xy, pred.data=preddata.var,
+dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,
                           off.set=preddata$area)
 ```
 
@@ -569,12 +575,19 @@ summary(dsm.xy.var)
 
 We can also make a plot of the CVs (with transect lines and observations overlaid) using the following code.
 
-```{r dsm.xyvarplot, fig.cap=""}
-p <- ggplot() + grid_plot_obj(sqrt(dsm.xy.var$pred.var)/unlist(dsm.xy.var$pred),
-                              "CV", pred.polys) + coord_equal() +gg.opts
-p <- p + geom_path(aes(x=x, y=y), data=survey.area)
-p <- p + geom_line(aes(x, y, group=Transect.Label), data=segdata)
-print(p)
+```{r dsm.xyvarplot, fig.cap="", fig.dim=c(7,5)}
+preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy
+
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+
+CV <- ggplot() +
+  geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +
+  geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
+  labs(title="Spotted dolphins, Gulf of Mexico, uncertainty (CV)",
+       subtitle = "Bivariate smooth of location, quasipoisson") +
+  scale_fill_viridis_c(option = "viridis", direction = 1)
+CV
 ```
 
 Note the increase in CV away from the transect lines.
@@ -582,7 +595,7 @@ Note the increase in CV away from the transect lines.
 We can revisit the model that included both depth and location smooths and observe that the coefficient of variation for that model is larger than that of the model with only the location smooth.
 
 ```{r dsm.xy.depth-varprop, cache=TRUE}
-dsm.xy.depth.var <- dsm.var.gam(dsm.xy.depth, pred.data=preddata.var,
+dsm.xy.depth.var <- dsm_var_gam(dsm.xy.depth, pred.data=preddata.var,
                                 off.set=preddata$area)
 summary(dsm.xy.depth.var)
 ```
@@ -592,13 +605,13 @@ summary(dsm.xy.depth.var)
 
 This document has outlined an analysis of spatially-explicit distance sampling data using the `dsm` package. Note that there are many possible models that can be fitted using `dsm` and that the aim here was to show just a few of the options. Results from the models can be rather different, so care must be taken in performing model selection, discrimination and criticism.
 
-
 # Software
 
  * `Distance` is available at [http://github.com/DistanceDevelopment/Distance](http://github.com/DistanceDevelopment/Distance) as well as on CRAN.
  * `dsm` is available at [http://github.com/DistanceDevelopment/dsm](http://github.com/DistanceDevelopment/dsm), as well as on CRAN.
 
-
 [^offsetcorrection]: An earlier version of this vignette incorrectly stated that the areas of the prediction cells were 444km$^2$. This has been corrected. Thanks to Phil Bouchet for pointing this out.
 [^MGET]: These operations can be performed in R using the `sp` and `rgeos` packages. It may, however, be easier to perform these operations in GIS such as ArcGIS -- in which case the MGET Toolbox may be useful.
 [^rsqoffset]: Note though that the adjusted $R^2$ for the model is defined as the proportion of variance explained but the "original" variance used for comparison doesn't include the offset (the area (or effective of the segments). It is therefore not recommended for one to directly interpret the $R^2$ value (see the `summary.gam` manual page for further details).
+
+# References
\ No newline at end of file
diff --git a/vignettes/lines_gomex/mexico-analysis_cache/html/__packages b/vignettes/lines_gomex/mexico-analysis_cache/html/__packages
new file mode 100644
index 0000000..ebf00a5
--- /dev/null
+++ b/vignettes/lines_gomex/mexico-analysis_cache/html/__packages
@@ -0,0 +1,10 @@
+knitr
+nlme
+mgcv
+mrds
+numDeriv
+dsm
+ggplot2
+sf
+plyr
+Distance
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diff --git a/vignettes/points_france/Contour_Rouillacais.shx b/vignettes/points_france/Contour_Rouillacais.shx
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HcmV?d00001

literal 108
zcmZQzQ0HR64$NLKGcd3M<>EV{l)rqEa7^#(;#zRr-tnV)@xS$7BpnqVefs;3%fS)5
Mb`((~1_tF00P^e-H~;_u

diff --git a/vignettes/points_france/Hare_data.csv b/vignettes/points_france/Hare_data.csv
deleted file mode 100644
index 2be7398..0000000
--- a/vignettes/points_france/Hare_data.csv
+++ /dev/null
@@ -1,576 +0,0 @@
-region;point_ID;Xcoord;Ycoord;distance;effort
-Rouillacais_2015;Rouillacais_2015_1;419607.8321;2094819.806;NA;3
-Rouillacais_2015;Rouillacais_2015_1;419607.8321;2094819.806;258;3
-Rouillacais_2015;Rouillacais_2015_1;419607.8321;2094819.806;307;3
-Rouillacais_2015;Rouillacais_2015_1;419607.8321;2094819.806;307;3
-Rouillacais_2015;Rouillacais_2015_1;419607.8321;2094819.806;236;3
-Rouillacais_2015;Rouillacais_2015_2;419341.9192;2096566.497;NA;3
-Rouillacais_2015;Rouillacais_2015_2;419341.9192;2096566.497;NA;3
-Rouillacais_2015;Rouillacais_2015_2;419341.9192;2096566.497;NA;3
-Rouillacais_2015;Rouillacais_2015_3;417947.761;2095566.784;131;3
-Rouillacais_2015;Rouillacais_2015_3;417947.761;2095566.784;158;3
-Rouillacais_2015;Rouillacais_2015_3;417947.761;2095566.784;148;3
-Rouillacais_2015;Rouillacais_2015_4;416528.8546;2095118.147;78;3
-Rouillacais_2015;Rouillacais_2015_4;416528.8546;2095118.147;NA;3
-Rouillacais_2015;Rouillacais_2015_4;416528.8546;2095118.147;132;3
-Rouillacais_2015;Rouillacais_2015_5;416465.0386;2096342.641;74;3
-Rouillacais_2015;Rouillacais_2015_5;416465.0386;2096342.641;246;3
-Rouillacais_2015;Rouillacais_2015_5;416465.0386;2096342.641;NA;3
-Rouillacais_2015;Rouillacais_2015_5;416465.0386;2096342.641;NA;3
-Rouillacais_2015;Rouillacais_2015_6;416411.0744;2098077.646;NA;3
-Rouillacais_2015;Rouillacais_2015_6;416411.0744;2098077.646;280;3
-Rouillacais_2015;Rouillacais_2015_6;416411.0744;2098077.646;NA;3
-Rouillacais_2015;Rouillacais_2015_7;418026.2351;2097482.576;142;3
-Rouillacais_2015;Rouillacais_2015_7;418026.2351;2097482.576;NA;3
-Rouillacais_2015;Rouillacais_2015_7;418026.2351;2097482.576;NA;3
-Rouillacais_2015;Rouillacais_2015_8;419467.022;2098266.903;176;3
-Rouillacais_2015;Rouillacais_2015_8;419467.022;2098266.903;131;3
-Rouillacais_2015;Rouillacais_2015_8;419467.022;2098266.903;91;3
-Rouillacais_2015;Rouillacais_2015_8;419467.022;2098266.903;92;3
-Rouillacais_2015;Rouillacais_2015_8;419467.022;2098266.903;107;3
-Rouillacais_2015;Rouillacais_2015_8;419467.022;2098266.903;90;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;82;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;83;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;79;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;91;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;91;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;114;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;94;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;167;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;138;3
-Rouillacais_2015;Rouillacais_2015_9;420878.0248;2097560.486;128;3
-Rouillacais_2015;Rouillacais_2015_10;420943.4383;2099187.798;NA;3
-Rouillacais_2015;Rouillacais_2015_10;420943.4383;2099187.798;214;3
-Rouillacais_2015;Rouillacais_2015_10;420943.4383;2099187.798;93;3
-Rouillacais_2015;Rouillacais_2015_10;420943.4383;2099187.798;93;3
-Rouillacais_2015;Rouillacais_2015_11;421213.5819;2100934.72;NA;3
-Rouillacais_2015;Rouillacais_2015_11;421213.5819;2100934.72;131;3
-Rouillacais_2015;Rouillacais_2015_11;421213.5819;2100934.72;131;3
-Rouillacais_2015;Rouillacais_2015_11;421213.5819;2100934.72;NA;3
-Rouillacais_2015;Rouillacais_2015_12;418065.2983;2099354.489;NA;3
-Rouillacais_2015;Rouillacais_2015_12;418065.2983;2099354.489;NA;3
-Rouillacais_2015;Rouillacais_2015_12;418065.2983;2099354.489;128;3
-Rouillacais_2015;Rouillacais_2015_12;418065.2983;2099354.489;128;3
-Rouillacais_2015;Rouillacais_2015_13;416453.5748;2100033.881;NA;3
-Rouillacais_2015;Rouillacais_2015_13;416453.5748;2100033.881;NA;3
-Rouillacais_2015;Rouillacais_2015_13;416453.5748;2100033.881;NA;3
-Rouillacais_2015;Rouillacais_2015_14;415021.2656;2099091.344;129;3
-Rouillacais_2015;Rouillacais_2015_14;415021.2656;2099091.344;220;3
-Rouillacais_2015;Rouillacais_2015_14;415021.2656;2099091.344;278;3
-Rouillacais_2015;Rouillacais_2015_14;415021.2656;2099091.344;329;3
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-Rouillacais_2015;Rouillacais_2015_14;415021.2656;2099091.344;126;3
-Rouillacais_2015;Rouillacais_2015_15;413657.1202;2099985.204;148;3
-Rouillacais_2015;Rouillacais_2015_15;413657.1202;2099985.204;231;3
-Rouillacais_2015;Rouillacais_2015_15;413657.1202;2099985.204;185;3
-Rouillacais_2015;Rouillacais_2015_16;414932.9316;2100863.24;183;3
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-Rouillacais_2015;Rouillacais_2015_16;414932.9316;2100863.24;139;3
-Rouillacais_2015;Rouillacais_2015_16;414932.9316;2100863.24;111;3
-Rouillacais_2015;Rouillacais_2015_17;416776.5344;2101798.901;142;3
-Rouillacais_2015;Rouillacais_2015_17;416776.5344;2101798.901;NA;3
-Rouillacais_2015;Rouillacais_2015_17;416776.5344;2101798.901;103;3
-Rouillacais_2015;Rouillacais_2015_17;416776.5344;2101798.901;150;3
-Rouillacais_2015;Rouillacais_2015_18;419406.899;2085661.307;NA;3
-Rouillacais_2015;Rouillacais_2015_18;419406.899;2085661.307;181;3
-Rouillacais_2015;Rouillacais_2015_18;419406.899;2085661.307;162;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;122;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;124;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;124;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;174;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;174;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;228;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;228;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;228;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;96;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;212;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;126;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;186;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;186;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;60;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;60;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;120;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;207;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;158;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;239;3
-Rouillacais_2015;Rouillacais_2015_19;420832.795;2085422.782;177;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;39;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;96;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;96;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;150;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;150;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;128;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;28;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;78;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;67;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;120;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;120;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;100;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;100;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;111;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;141;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;132;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;120;3
-Rouillacais_2015;Rouillacais_2015_20;420980.0112;2086649.681;103;3
-Rouillacais_2015;Rouillacais_2015_21;419571.8716;2087744.138;325;3
-Rouillacais_2015;Rouillacais_2015_21;419571.8716;2087744.138;156;3
-Rouillacais_2015;Rouillacais_2015_21;419571.8716;2087744.138;90;3
-Rouillacais_2015;Rouillacais_2015_21;419571.8716;2087744.138;137;3
-Rouillacais_2015;Rouillacais_2015_22;419392.6866;2089623.383;NA;3
-Rouillacais_2015;Rouillacais_2015_22;419392.6866;2089623.383;95;3
-Rouillacais_2015;Rouillacais_2015_22;419392.6866;2089623.383;95;3
-Rouillacais_2015;Rouillacais_2015_22;419392.6866;2089623.383;138;3
-Rouillacais_2015;Rouillacais_2015_22;419392.6866;2089623.383;208;3
-Rouillacais_2015;Rouillacais_2015_23;417837.5583;2090333.928;191;3
-Rouillacais_2015;Rouillacais_2015_23;417837.5583;2090333.928;197;3
-Rouillacais_2015;Rouillacais_2015_23;417837.5583;2090333.928;226;3
-Rouillacais_2015;Rouillacais_2015_23;417837.5583;2090333.928;207;3
-Rouillacais_2015;Rouillacais_2015_23;417837.5583;2090333.928;293;3
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-Rouillacais_2015;Rouillacais_2015_23;417837.5583;2090333.928;190;3
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-Rouillacais_2015;Rouillacais_2015_23;417837.5583;2090333.928;135;3
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-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;251;3
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-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;78;3
-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;148;3
-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;148;3
-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;148;3
-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;140;3
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-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;156;3
-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;232;3
-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;135;3
-Rouillacais_2015;Rouillacais_2015_24;419680.9333;2091459.093;155;3
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-Rouillacais_2015;Rouillacais_2015_25;417989.356;2092148.443;123;3
-Rouillacais_2015;Rouillacais_2015_25;417989.356;2092148.443;NA;3
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-Rouillacais_2015;Rouillacais_2015_26;416544.2106;2093028.441;211;3
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-Rouillacais_2015;Rouillacais_2015_26;416544.2106;2093028.441;NA;3
-Rouillacais_2015;Rouillacais_2015_27;418107.4706;2093813.447;NA;3
-Rouillacais_2015;Rouillacais_2015_27;418107.4706;2093813.447;374;3
-Rouillacais_2015;Rouillacais_2015_27;418107.4706;2093813.447;374;3
-Rouillacais_2015;Rouillacais_2015_27;418107.4706;2093813.447;NA;3
-Rouillacais_2015;Rouillacais_2015_28;419711.8623;2093189.859;112;3
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-Rouillacais_2015;Rouillacais_2015_29;421125.2726;2092100.671;90;3
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-Rouillacais_2015;Rouillacais_2015_31;424233.1264;2088564.333;NA;3
-Rouillacais_2015;Rouillacais_2015_31;424233.1264;2088564.333;NA;3
-Rouillacais_2015;Rouillacais_2015_31;424233.1264;2088564.333;NA;3
-Rouillacais_2015;Rouillacais_2015_32;422198.1964;2087515.811;NA;3
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-Rouillacais_2015;Rouillacais_2015_32;422198.1964;2087515.811;166;3
-Rouillacais_2015;Rouillacais_2015_33;421013.3446;2090217.645;NA;3
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-Rouillacais_2015;Rouillacais_2015_38;413563.8767;2091031.551;145;3
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-Rouillacais_2015;Rouillacais_2015_38;413563.8767;2091031.551;85;3
-Rouillacais_2015;Rouillacais_2015_38;413563.8767;2091031.551;112;3
-Rouillacais_2015;Rouillacais_2015_38;413563.8767;2091031.551;142;3
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-Rouillacais_2015;Rouillacais_2015_38;413563.8767;2091031.551;331;3
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-Rouillacais_2015;Rouillacais_2015_39;411892.9215;2091929.081;NA;3
-Rouillacais_2015;Rouillacais_2015_39;411892.9215;2091929.081;NA;3
-Rouillacais_2015;Rouillacais_2015_39;411892.9215;2091929.081;NA;3
-Rouillacais_2015;Rouillacais_2015_40;410219.4459;2091093.091;NA;3
-Rouillacais_2015;Rouillacais_2015_40;410219.4459;2091093.091;NA;3
-Rouillacais_2015;Rouillacais_2015_40;410219.4459;2091093.091;NA;3
-Rouillacais_2015;Rouillacais_2015_41;411766.6692;2090434.777;304;3
-Rouillacais_2015;Rouillacais_2015_41;411766.6692;2090434.777;324;3
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-Rouillacais_2015;Rouillacais_2015_41;411766.6692;2090434.777;347;3
-Rouillacais_2015;Rouillacais_2015_41;411766.6692;2090434.777;359;3
-Rouillacais_2015;Rouillacais_2015_41;411766.6692;2090434.777;359;3
-Rouillacais_2015;Rouillacais_2015_42;410636.9265;2089068.679;141;3
-Rouillacais_2015;Rouillacais_2015_42;410636.9265;2089068.679;223;3
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-Rouillacais_2015;Rouillacais_2015_42;410636.9265;2089068.679;215;3
-Rouillacais_2015;Rouillacais_2015_42;410636.9265;2089068.679;165;3
-Rouillacais_2015;Rouillacais_2015_42;410636.9265;2089068.679;243;3
-Rouillacais_2015;Rouillacais_2015_42;410636.9265;2089068.679;262;3
-Rouillacais_2015;Rouillacais_2015_42;410636.9265;2089068.679;262;3
-Rouillacais_2015;Rouillacais_2015_43;411941.9562;2088639.045;NA;3
-Rouillacais_2015;Rouillacais_2015_43;411941.9562;2088639.045;360;3
-Rouillacais_2015;Rouillacais_2015_43;411941.9562;2088639.045;366;3
-Rouillacais_2015;Rouillacais_2015_43;411941.9562;2088639.045;185;3
-Rouillacais_2015;Rouillacais_2015_44;411655.7683;2086820.172;NA;3
-Rouillacais_2015;Rouillacais_2015_44;411655.7683;2086820.172;NA;3
-Rouillacais_2015;Rouillacais_2015_44;411655.7683;2086820.172;112;3
-Rouillacais_2015;Rouillacais_2015_45;413412.5353;2087792.959;242;3
-Rouillacais_2015;Rouillacais_2015_45;413412.5353;2087792.959;84;3
-Rouillacais_2015;Rouillacais_2015_45;413412.5353;2087792.959;103;3
-Rouillacais_2015;Rouillacais_2015_45;413412.5353;2087792.959;270;3
-Rouillacais_2015;Rouillacais_2015_46;414888.2706;2086803.893;197;3
-Rouillacais_2015;Rouillacais_2015_46;414888.2706;2086803.893;NA;3
-Rouillacais_2015;Rouillacais_2015_46;414888.2706;2086803.893;NA;3
-Rouillacais_2015;Rouillacais_2015_47;414899.1531;2088502.698;NA;3
-Rouillacais_2015;Rouillacais_2015_47;414899.1531;2088502.698;57;3
-Rouillacais_2015;Rouillacais_2015_47;414899.1531;2088502.698;307;3
-Rouillacais_2015;Rouillacais_2015_47;414899.1531;2088502.698;307;3
-Rouillacais_2015;Rouillacais_2015_47;414899.1531;2088502.698;71;3
-Rouillacais_2015;Rouillacais_2015_47;414899.1531;2088502.698;78;3
-Rouillacais_2015;Rouillacais_2015_48;416429.1929;2087712.178;NA;3
-Rouillacais_2015;Rouillacais_2015_48;416429.1929;2087712.178;NA;3
-Rouillacais_2015;Rouillacais_2015_48;416429.1929;2087712.178;NA;3
-Rouillacais_2015;Rouillacais_2015_49;417954.9915;2088757.712;71;3
-Rouillacais_2015;Rouillacais_2015_49;417954.9915;2088757.712;NA;3
-Rouillacais_2015;Rouillacais_2015_49;417954.9915;2088757.712;432;3
-Rouillacais_2015;Rouillacais_2015_49;417954.9915;2088757.712;100;3
-Rouillacais_2015;Rouillacais_2015_50;416174.5092;2089383.945;NA;3
-Rouillacais_2015;Rouillacais_2015_50;416174.5092;2089383.945;127;3
-Rouillacais_2015;Rouillacais_2015_50;416174.5092;2089383.945;103;3
-Rouillacais_2016;Rouillacais_2016_1;419607.8321;2094819.806;233;3
-Rouillacais_2016;Rouillacais_2016_1;419607.8321;2094819.806;280;3
-Rouillacais_2016;Rouillacais_2016_1;419607.8321;2094819.806;307;3
-Rouillacais_2016;Rouillacais_2016_1;419607.8321;2094819.806;292;3
-Rouillacais_2016;Rouillacais_2016_1;419607.8321;2094819.806;292;3
-Rouillacais_2016;Rouillacais_2016_1;419607.8321;2094819.806;283;3
-Rouillacais_2016;Rouillacais_2016_2;419341.9192;2096566.497;NA;3
-Rouillacais_2016;Rouillacais_2016_2;419341.9192;2096566.497;NA;3
-Rouillacais_2016;Rouillacais_2016_2;419341.9192;2096566.497;NA;3
-Rouillacais_2016;Rouillacais_2016_3;417947.761;2095566.784;NA;3
-Rouillacais_2016;Rouillacais_2016_3;417947.761;2095566.784;111;3
-Rouillacais_2016;Rouillacais_2016_3;417947.761;2095566.784;140;3
-Rouillacais_2016;Rouillacais_2016_3;417947.761;2095566.784;140;3
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-Rouillacais_2016;Rouillacais_2016_3;417947.761;2095566.784;134;3
-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;273;3
-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;273;3
-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;141;3
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-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;146;3
-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;146;3
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-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;246;3
-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;246;3
-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;77;3
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-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;219;3
-Rouillacais_2016;Rouillacais_2016_4;416528.8546;2095118.147;222;3
-Rouillacais_2016;Rouillacais_2016_5;416465.0386;2096342.641;233;3
-Rouillacais_2016;Rouillacais_2016_5;416465.0386;2096342.641;338;3
-Rouillacais_2016;Rouillacais_2016_5;416465.0386;2096342.641;251;3
-Rouillacais_2016;Rouillacais_2016_5;416465.0386;2096342.641;251;3
-Rouillacais_2016;Rouillacais_2016_5;416465.0386;2096342.641;369;3
-Rouillacais_2016;Rouillacais_2016_5;416465.0386;2096342.641;95;3
-Rouillacais_2016;Rouillacais_2016_6;416411.0744;2098077.646;224;3
-Rouillacais_2016;Rouillacais_2016_6;416411.0744;2098077.646;210;3
-Rouillacais_2016;Rouillacais_2016_6;416411.0744;2098077.646;132;3
-Rouillacais_2016;Rouillacais_2016_6;416411.0744;2098077.646;NA;3
-Rouillacais_2016;Rouillacais_2016_7;418026.2351;2097482.576;277;3
-Rouillacais_2016;Rouillacais_2016_7;418026.2351;2097482.576;NA;3
-Rouillacais_2016;Rouillacais_2016_7;418026.2351;2097482.576;69;3
-Rouillacais_2016;Rouillacais_2016_7;418026.2351;2097482.576;272;3
-Rouillacais_2016;Rouillacais_2016_8;419467.022;2098266.903;NA;3
-Rouillacais_2016;Rouillacais_2016_8;419467.022;2098266.903;NA;3
-Rouillacais_2016;Rouillacais_2016_8;419467.022;2098266.903;109;3
-Rouillacais_2016;Rouillacais_2016_8;419467.022;2098266.903;109;3
-Rouillacais_2016;Rouillacais_2016_9;420878.0248;2097560.486;NA;3
-Rouillacais_2016;Rouillacais_2016_9;420878.0248;2097560.486;132;3
-Rouillacais_2016;Rouillacais_2016_9;420878.0248;2097560.486;NA;3
-Rouillacais_2016;Rouillacais_2016_10;420943.4383;2099187.798;129;3
-Rouillacais_2016;Rouillacais_2016_10;420943.4383;2099187.798;96;3
-Rouillacais_2016;Rouillacais_2016_10;420943.4383;2099187.798;171;3
-Rouillacais_2016;Rouillacais_2016_10;420943.4383;2099187.798;62;3
-Rouillacais_2016;Rouillacais_2016_10;420943.4383;2099187.798;277;3
-Rouillacais_2016;Rouillacais_2016_11;421213.5819;2100934.72;NA;3
-Rouillacais_2016;Rouillacais_2016_11;421213.5819;2100934.72;NA;3
-Rouillacais_2016;Rouillacais_2016_11;421213.5819;2100934.72;NA;3
-Rouillacais_2016;Rouillacais_2016_12;418065.2983;2099354.489;NA;3
-Rouillacais_2016;Rouillacais_2016_12;418065.2983;2099354.489;105;3
-Rouillacais_2016;Rouillacais_2016_12;418065.2983;2099354.489;105;3
-Rouillacais_2016;Rouillacais_2016_12;418065.2983;2099354.489;173;3
-Rouillacais_2016;Rouillacais_2016_13;416453.5748;2100033.881;90;3
-Rouillacais_2016;Rouillacais_2016_13;416453.5748;2100033.881;154;3
-Rouillacais_2016;Rouillacais_2016_13;416453.5748;2100033.881;153;3
-Rouillacais_2016;Rouillacais_2016_14;415021.2656;2099091.344;263;3
-Rouillacais_2016;Rouillacais_2016_14;415021.2656;2099091.344;362;3
-Rouillacais_2016;Rouillacais_2016_14;415021.2656;2099091.344;NA;3
-Rouillacais_2016;Rouillacais_2016_14;415021.2656;2099091.344;NA;3
-Rouillacais_2016;Rouillacais_2016_15;413657.1202;2099985.204;NA;3
-Rouillacais_2016;Rouillacais_2016_15;413657.1202;2099985.204;140;3
-Rouillacais_2016;Rouillacais_2016_15;413657.1202;2099985.204;199;3
-Rouillacais_2016;Rouillacais_2016_15;413657.1202;2099985.204;286;3
-Rouillacais_2016;Rouillacais_2016_15;413657.1202;2099985.204;136;3
-Rouillacais_2016;Rouillacais_2016_16;414932.9316;2100863.24;NA;3
-Rouillacais_2016;Rouillacais_2016_16;414932.9316;2100863.24;163;3
-Rouillacais_2016;Rouillacais_2016_16;414932.9316;2100863.24;219;3
-Rouillacais_2016;Rouillacais_2016_17;416776.5344;2101798.901;NA;3
-Rouillacais_2016;Rouillacais_2016_17;416776.5344;2101798.901;NA;3
-Rouillacais_2016;Rouillacais_2016_17;416776.5344;2101798.901;NA;3
-Rouillacais_2016;Rouillacais_2016_18;419406.899;2085661.307;225;3
-Rouillacais_2016;Rouillacais_2016_18;419406.899;2085661.307;NA;3
-Rouillacais_2016;Rouillacais_2016_18;419406.899;2085661.307;249;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;187;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;187;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;187;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;187;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;270;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;85;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;197;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;257;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;248;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;262;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;283;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;210;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;242;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;275;3
-Rouillacais_2016;Rouillacais_2016_19;420832.795;2085422.782;278;3
-Rouillacais_2016;Rouillacais_2016_20;420980.0112;2086649.681;NA;3
-Rouillacais_2016;Rouillacais_2016_20;420980.0112;2086649.681;NA;3
-Rouillacais_2016;Rouillacais_2016_20;420980.0112;2086649.681;NA;3
-Rouillacais_2016;Rouillacais_2016_21;419571.8716;2087744.138;280;3
-Rouillacais_2016;Rouillacais_2016_21;419571.8716;2087744.138;NA;3
-Rouillacais_2016;Rouillacais_2016_21;419571.8716;2087744.138;130;3
-Rouillacais_2016;Rouillacais_2016_22;419392.6866;2089623.383;256;3
-Rouillacais_2016;Rouillacais_2016_22;419392.6866;2089623.383;400;3
-Rouillacais_2016;Rouillacais_2016_22;419392.6866;2089623.383;237;3
-Rouillacais_2016;Rouillacais_2016_22;419392.6866;2089623.383;197;3
-Rouillacais_2016;Rouillacais_2016_22;419392.6866;2089623.383;211;3
-Rouillacais_2016;Rouillacais_2016_22;419392.6866;2089623.383;282;3
-Rouillacais_2016;Rouillacais_2016_23;417837.5583;2090333.928;349;3
-Rouillacais_2016;Rouillacais_2016_23;417837.5583;2090333.928;349;3
-Rouillacais_2016;Rouillacais_2016_23;417837.5583;2090333.928;349;3
-Rouillacais_2016;Rouillacais_2016_23;417837.5583;2090333.928;300;3
-Rouillacais_2016;Rouillacais_2016_23;417837.5583;2090333.928;300;3
-Rouillacais_2016;Rouillacais_2016_23;417837.5583;2090333.928;NA;3
-Rouillacais_2016;Rouillacais_2016_24;419680.9333;2091459.093;159;3
-Rouillacais_2016;Rouillacais_2016_24;419680.9333;2091459.093;159;3
-Rouillacais_2016;Rouillacais_2016_24;419680.9333;2091459.093;159;3
-Rouillacais_2016;Rouillacais_2016_24;419680.9333;2091459.093;135;3
-Rouillacais_2016;Rouillacais_2016_24;419680.9333;2091459.093;135;3
-Rouillacais_2016;Rouillacais_2016_24;419680.9333;2091459.093;109;3
-Rouillacais_2016;Rouillacais_2016_25;417989.356;2092148.443;NA;3
-Rouillacais_2016;Rouillacais_2016_25;417989.356;2092148.443;NA;3
-Rouillacais_2016;Rouillacais_2016_25;417989.356;2092148.443;NA;3
-Rouillacais_2016;Rouillacais_2016_26;416544.2106;2093028.441;124;3
-Rouillacais_2016;Rouillacais_2016_26;416544.2106;2093028.441;NA;3
-Rouillacais_2016;Rouillacais_2016_26;416544.2106;2093028.441;NA;3
-Rouillacais_2016;Rouillacais_2016_27;418107.4706;2093813.447;150;3
-Rouillacais_2016;Rouillacais_2016_27;418107.4706;2093813.447;164;3
-Rouillacais_2016;Rouillacais_2016_27;418107.4706;2093813.447;143;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;147;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;147;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;212;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;453;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;215;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;283;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;163;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;163;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;163;3
-Rouillacais_2016;Rouillacais_2016_28;419711.8623;2093189.859;154;3
-Rouillacais_2016;Rouillacais_2016_29;421125.2726;2092100.671;136;3
-Rouillacais_2016;Rouillacais_2016_29;421125.2726;2092100.671;136;3
-Rouillacais_2016;Rouillacais_2016_29;421125.2726;2092100.671;209;3
-Rouillacais_2016;Rouillacais_2016_29;421125.2726;2092100.671;267;3
-Rouillacais_2016;Rouillacais_2016_29;421125.2726;2092100.671;102;3
-Rouillacais_2016;Rouillacais_2016_29;421125.2726;2092100.671;102;3
-Rouillacais_2016;Rouillacais_2016_29;421125.2726;2092100.671;116;3
-Rouillacais_2016;Rouillacais_2016_29;421125.2726;2092100.671;198;3
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diff --git a/vignettes/points_france/hare_point_transect_dsm.Rmd b/vignettes/points_france/hare_point_transect_dsm.Rmd
deleted file mode 100644
index 77b4515..0000000
--- a/vignettes/points_france/hare_point_transect_dsm.Rmd
+++ /dev/null
@@ -1,289 +0,0 @@
----
-title: "Density surface model for hare point transect survey"
-description: |
-  Point transects for hares analysed with model-based inference to study area.
-author:
-  - name: Guillaume Souchay 
-    url: https://scholar.google.com/citations?user=mFKm9kAAAAAJ&hl=en
-    affiliation: French National Hunting and Wildlife Agency
-    affiliation_url: https://ofb.gouv.fr/
-date: "`r Sys.Date()`"
-output: 
-  distill::distill_article:
-    toc: true
-    toc_depth: 2
-bibliography: hares.bib
-csl: apa.csl
----
-
-```{r echo=FALSE}
-library(knitr)
-opts_chunk$set(message=FALSE, echo=TRUE, error=FALSE, warning=FALSE)
-```
-
-In France, the French National Agency for Hunting and Wildlife has recently created the Hare network [@Mauvy2017], a network of voluntary sites where hares and foxes are sampled every year in February using point transect sampling [@Peroux1997].
-
-As an illustration, we used the sampling data from Rouillacais for hare only, in western part of France. At this site, ~ 50 points were sampled during 3 successive nights in February of 2015 and 2016, distance from observers to hares and foxes were recorded. Presence of other species was also noted. Fieldwork was undertaken by skilled observers only. Data for the two years are pooled to produce a single estimate that represents the average density for the two years.
-
-*The material of this analysis is provided for illustration purpose only. For other use or questions regarding the Hare network, feel free to contact us at reseau.lievre@oncfs.gouv.fr*
-
-*Place all following files in same directory as .Rmd*
-
-- [Point transect survey csv](Hare_data.csv)
-- [Study area shapefile shp](Contour_Rouillacais.shp)
-- [Study area shapefile shx](Contour_Rouillacais.shx)
-- [Study area shapefile dbf](Contour_Rouillacais.dbf)
-- [Point station shapefile shp](Rouillacais_points.shp)
-- [Point station shapefile shx](Rouillacais_points.shx)
-- [Point station shapefile dbf](Rouillacais_points.dbf)
-
-
-
-## Observation data and covariates
-
-First, we load point transect survey data into `R` [@r_core_team_r_2019].
-
-```{r data}
-# load raw data
-data <- read.table("Hare_data.csv", header = TRUE, sep = ";", stringsAsFactors=FALSE)
-str(data)
-```
-
-Then, we build our dataset for the detection function.
-
-```{r Dataset}
-DSdata <- data.frame(Sample.Label = sub("Rouillacais_2016", "", data$point_ID),
-                     Point = data$point_ID,
-                     Xcoord = as.integer(data$Xcoord),
-                     Ycoord = as.integer(data$Ycoord),
-                     Area = 1)
-```
-
-
-Distance are given in metres, so density would be estimated as the number of individuals per square metre. To avoid this, we convert metres into kilometres.
-
-```{r tokm}
-DSdata$distance <- as.numeric(data$distance)/1000
-```
-
-Note that the `Effort` column contains `3`, as each point was visited 3 times.  It is worth noting that in our study, we are not interested in abundance but in density only.
-
-```{r effort}
-DSdata$Effort <- 3
-```
-
-### GIS data
-
-We now load and format the spatial data from shapefiles. Note there is a shape file for the study area (Contour_Rouillacais) and the point station locations (Rouillacais_points).  These shape files reside in the same directory as the point survey data and Rmarkdown file.
-
-```{r map_data}
-library("rgdal")
-library("maptools")
-library("ggplot2")
-library("plyr")
-
-# provide the correct projection for the data
-newproj <- "+proj=lcc +nadgrids=ntf_r93.gsb,null +a=6378249.2000 +rf=293.4660210000000  +pm=2.337229167 +lat_0=46.800000000 +lon_0=0.000000000 +k_0=0.99987742 +lat_1=46.800000000 +x_0=600000.000 +y_0=200000.000 +units=m +no_defs"
-# import shapefile for the survey area
-shape <- readShapeSpatial("Contour_Rouillacais.shp", proj4string = CRS(newproj),
-                          repair=TRUE, force_ring=T, verbose=TRUE)
-# import shapefile for the points
-EPP <- readShapeSpatial("Rouillacais_points.shp", proj4string = CRS(newproj),
-                        repair=TRUE, force_ring=T, verbose=TRUE)
-# make the object simpler
-survey.area <- data.frame(shape@polygons[[1]]@Polygons[[1]]@coords)
-names(survey.area) <- c("x","y")
-```
-
-We can find the area of the study area (in km$^2$):
-
-```{r area}
-shape@data$AREA/(1000^2)
-```
-
-We can then produce the map of the area with the sampled points (Figure \@ref(fig:map1)).
-
-```{r map1, fig.cap="Map with sampled points"}
-# produce a map of the survey area with all the point sampled
-p <- qplot(data=survey.area, x=x, y=y, geom="polygon",
-           fill=I("lightblue"), ylab="y", xlab="x", alpha=I(0.7)) +
-  geom_point(aes(x=Xcoord, y=Ycoord, group="Point"),
-             data=DSdata, colour="darkblue") +
-  coord_equal() +
-  theme_minimal()
-print(p)
-
-```
-
-Setting up the segment data, which in our case are the points that were visited...
-
-```{r segdata}
-# construct segment (point) data (x, y, Effort, Sample.Label)
-segdata <- as.data.frame(matrix(NA, ncol = 5, nrow=100))
-segdata <- DSdata[, c("Sample.Label", "Effort", "Point", "Xcoord", "Ycoord")]
-segdata <- segdata[!duplicated(segdata), ]
-colnames(segdata) <- c("Sample.Label", "Effort", "Segment.Label", "X", "Y")
-```
-
-Setting up the observation data, which links the observations with the segments (points):
-
-```{r obsdata}
-obsdata <- DSdata
-obsdata$size <- 1
-obsdata$object <- 1:nrow(obsdata)
-str(obsdata)
-```
-
-
-We then create the prediction grid (Figure \@ref(fig:projgrid)).
-
-```{r projgrid, fig.cap="Prediction grid"}
-# create a prediction grid
-# method from http://rfunctions.blogspot.co.uk/2014/12/how-to-create-grid-and-intersect-it.html
-library("raster")
-library("rgeos")
-library("dismo")
-
-# Create an empty raster
-grid <- raster(extent(shape))
-# Choose its resolution. 500 m in both X and Y (truncation distance)
-res(grid) <- 500
-# Make the grid have the same coordinate reference system (CRS) as the shapefile.
-proj4string(grid) <- proj4string(shape)
-# Transform this raster into a polygon and you will have a grid
-gridpolygon <- rasterToPolygons(grid)
-# Intersect our grid with shape
-pred.grid <- intersect(shape, gridpolygon)
-# Plot the intersected shape to check if everything is fine.
-plot(pred.grid)
-
-# create the data.frame for prediction
-preddata <- as.data.frame(matrix(NA, ncol=3, nrow=dim(pred.grid@data)[1]))
-colnames(preddata) <- c("X", "Y", "area")
-for (i in 1:dim(pred.grid@data)[1]){
-  preddata[i, c("X", "Y")] <- pred.grid@polygons[[i]]@labpt
-  preddata[i, c("area")] <- pred.grid@polygons[[i]]@area/(1000^2)
-}
-```
-
-The size of each cell in the prediction grid is 0.25 km$^2$ (created in metres and converted to kilometres).
-
-## Detection function
-
-Detection functions can be fitted using the `Distance` R package:
-
-```{r det_function}
-library("Distance")
-# define distance bins
-cut <- c(0, 0.180, 0.220, 0.280, 0.300)
-
-df_ht <- ds(DSdata, truncation=0.3, transect="point",
-            formula=~1, key="hn", adjustment=NULL, cutpoints=cut)
-```
-
-We can look at a plot of the detection function and results from goodness of fit testing (Figure \@ref(fig:gof)).
-
-```{r gof, fig.cap="Detection function:  half-normal."}
-plot(df_ht, pdf=TRUE)
-gof_ds(df_ht)
-```
-
-We can see from the above, the model fit seems adequate.
-
-We can now fit some DSMs [@Miller2013]:
-
-
-## Density surface modelling
-
-```{r dsmload}
-library("dsm")
-```
-
-Now fitting the DSMs, we model the count as a function of space, using a Tweedie (`tw()`) response distribution:
-
-```{r dsm-fitting}
-mod_tw <- dsm(count~s(X, Y), ddf.obj=df_ht, segment.data=segdata, 
-              observation.data=obsdata, family=tw(), transect="point")
-```
-
-Note that we need to specify `transect="point"` to ensure that the effort is calculated properly.
-
-We can then look at the `summary` and model checking output (Figure \@ref(fig:gamcheck)).
-
-```{r gamcheck, fig.cap="Results from `gam.check`", layout="l-body-outset"}
-summary(mod_tw)
-gam.check(mod_tw)
-```
-
-This model is fairly rudimentary, so the plot of response vs fitted values doesn't seem that great (note the difference in axis scales) but for illustration purposes here we accept what we have.
-
-## Making predictions
-
-We can now predict over the prediction grid.
-
-```{r makepred}
-mod_tw_pred <- predict(mod_tw, preddata, preddata$area)
-```
-
-Here we define a convenience function to generate an appropriate data structure for `ggplot2` to plot: given the argument `fill` (the covariate vector to use as the fill) and a name, return a `geom_polygon` object  (fill must be in the same order as the polygon data).
-
-
-```{r plotpred}
-grid_plot_obj <- function(shape,fill, name){
-  
-  # what data were supplied?
-  names(fill) <- NULL
-  row.names(fill) <- NULL
-  data <- data.frame(fill)
-  names(data) <- name
-  
-  # ! need to give the right name of the shapefile
-  sp <- shape
-  spdf <- SpatialPolygonsDataFrame(sp, data)
-  spdf@data$id <- rownames(spdf@data)
-  spdf.points <- fortify(spdf, region="id")
-  spdf.df <- join(spdf.points, spdf@data, by="id")
-  
-  # store the x/y even when projected and labelled as "long" and "lat"
-  spdf.df$x <- spdf.df$long
-  spdf.df$y <- spdf.df$lat
-  
-  geom_polygon(aes_string(x="x",y="y",fill=name, group="group"), data=spdf.df)
-}
-
-# make the plot
-pcount_tw <- ggplot() +
-  grid_plot_obj(pred.grid, mod_tw_pred, "Density") + 
-  scale_fill_gradient(low="white", high="chocolate4")  +
-  coord_equal() + theme_minimal() +
-  geom_path(aes(x=x, y=y), data=survey.area) +
-  geom_point(aes(x = Xcoord, y = Ycoord, group="Point"), data = DSdata, colour = "black") +
-  labs(fill="Density")
-```
-
-We can also estimate uncertainty in our abundance map in the form of a map of coefficients of variation. Note that since there are no covariates in the detection function, we use the `dsm.var.gam` function to estimate the variance (if there were covariates varying at the segment level, such as sea state or observer, we could use `dsm.var.prop`)  (Figure \@ref(fig:gridarr)).
-
-```{r cvpred}
-# data setup for plotting
-preddata.var <- split(preddata, 1:nrow(preddata))
-
-# estimate variance
-mod_tw_var <- dsm.var.gam(mod_tw, pred.data=preddata.var, off.set=preddata$area)
-summary(mod_tw_var)
-
-# plot
-pcount_cv_tw <- ggplot() + 
-  grid_plot_obj(pred.grid, sqrt(mod_tw_var$pred.var)/unlist(mod_tw_var$pred), "CV") + 
-  scale_fill_gradient(low = "white", high = "chocolate4") +
-  coord_equal() + theme_minimal() +
-  geom_path(aes(x=x, y=y),data=survey.area) +
-  geom_point(aes(x=Xcoord, y=Ycoord, group="Point"), data=DSdata, colour="black")
-```
-
-```{r, gridarr, fig.cap="Map of hare density (ind/km²) and of associated coefficient of variation in Rouillacais site from the DSM model with coordinates as covariates using Tweedie response. Black dots represent the sampled point.", layout="l-page"}
-library("gridExtra")
-grid.arrange(pcount_tw, pcount_cv_tw, ncol=2)
-```
-
-This is a small example of what can be done using DSM and the simplest covariates available: geographical coordinates.
-
diff --git a/vignettes/points_france/hares.bib b/vignettes/points_france/hares.bib
deleted file mode 100644
index 74c4d64..0000000
--- a/vignettes/points_france/hares.bib
+++ /dev/null
@@ -1,38 +0,0 @@
-% Encoding: UTF-8
-
-@InProceedings{Mauvy2017,
-  author    = {B. Mauvy and G. Souchay and J.-P. Arnauduc and J.-S. Guitton},
-  booktitle = {33rd IUBG Congress & 14th Perdix Symposium},
-  title     = {The French Hare Network : a tool to study European hare populations dynamics on a large scale},
-  year      = {2017},
-  url       = {http://iugb2017.com/wp-content/uploads/2017/08/abstract-book-FINAL-VERSION.pdf},
-}
-
-@Article{Peroux1997,
-  author  = {R. Peroux and B Mauvy and A. Lartiges and Y. Bray and E. Marboutin},
-  journal = {Game and Wildlife},
-  title   = {Point transects sampling: a new approach to estimate densities or abundances of European hare (Lepus europaeus) from spotlight counts},
-  year    = {1997},
-  pages   = {525-529},
-  volume  = {14},
-}
-
-
-@misc{r_core_team_r_2019,
-  address = {{Vienna Austria}},
-  title = {R: A Language and Environment for Statistical Computing},
-  howpublished = {R Foundation for Statistical Computing},
-  author = {{R Core Team}},
-  year = {2019}
-}
-
-@article{Miller2013,
-  author = {Miller, David L. and Burt, M. Louise and Rexstad, Eric A. and Thomas, Len},
-  title = {Spatial models for distance sampling data: recent developments and future directions},
-  journal = {Methods in Ecology and Evolution},
-  year = {2013},
-  volume = {4},
-  pages = {1001-1010},
-  url = {http://dx.doi.org/10.1111/2041-210X.12105},
-  doi = {10.1111/2041-210X.12105}
-}
\ No newline at end of file

From d79c70b452cd7a64c4e38de9d06ca8b2e6fc4f60 Mon Sep 17 00:00:00 2001
From: erex <ericincrail@gmail.com>
Date: Sun, 12 May 2024 15:57:34 +0100
Subject: [PATCH 3/6] add the docs directory this time

---
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 docs/deps/data-deps.txt                       |     4 +
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 docs/deps/jquery-3.6.0/jquery-3.6.0.min.js    |     2 +
 docs/deps/jquery-3.6.0/jquery-3.6.0.min.map   |     1 +
 docs/index.html                               |   148 +
 docs/link.svg                                 |    12 +
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 create mode 100644 docs/articles/lines_gomex/mexico-analysis.html
 create mode 100644 docs/articles/lines_gomex/mexico-analysis_files/figure-html/EDA-plots-1.png
 create mode 100644 docs/articles/lines_gomex/mexico-analysis_files/figure-html/areawithtransects-1.png
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 create mode 100644 docs/articles/lines_gomex/mexico-analysis_files/figure-html/dsm.xyvarplot-1.png
 create mode 100644 docs/articles/lines_gomex/mexico-analysis_files/figure-html/hr-detfct-1.png
 create mode 100644 docs/articles/lines_gomex/mexico-analysis_files/figure-html/hr-detfct-2.png
 create mode 100644 docs/articles/lines_gomex/mexico-analysis_files/figure-html/projection-compare-1.png
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 create mode 100644 docs/articles/lines_gomex/mexico-analysis_files/figure-html/visgam1-1.png
 create mode 100644 docs/articles/lines_gomex/mexico-analysis_files/figure-html/visgam5-1.png
 create mode 100644 docs/authors.html
 create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js
 create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map
 create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.min.css
 create mode 100644 docs/deps/data-deps.txt
 create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.js
 create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.min.js
 create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.min.map
 create mode 100644 docs/index.html
 create mode 100644 docs/link.svg
 create mode 100644 docs/news/index.html
 create mode 100644 docs/pkgdown.js
 create mode 100644 docs/pkgdown.yml
 create mode 100644 docs/reference/Rplot001.png
 create mode 100644 docs/reference/block.info.per.su.html
 create mode 100644 docs/reference/check.cols.html
 create mode 100644 docs/reference/dsm-data.html
 create mode 100644 docs/reference/dsm-package.html
 create mode 100644 docs/reference/dsm.cor.html
 create mode 100644 docs/reference/dsm.html
 create mode 100644 docs/reference/dsm.var.gam.html
 create mode 100644 docs/reference/dsm.var.movblk.html
 create mode 100644 docs/reference/dsm.var.prop.html
 create mode 100644 docs/reference/dsm_cor-1.png
 create mode 100644 docs/reference/dsm_cor.html
 create mode 100644 docs/reference/dsm_var_gam.html
 create mode 100644 docs/reference/dsm_var_movblk.html
 create mode 100644 docs/reference/dsm_var_prop.html
 create mode 100644 docs/reference/dsm_varprop.html
 create mode 100644 docs/reference/dummy_ddf.html
 create mode 100644 docs/reference/generate.ds.uncertainty.html
 create mode 100644 docs/reference/generate.mb.sample.html
 create mode 100644 docs/reference/index.html
 create mode 100644 docs/reference/latlong2km.html
 create mode 100644 docs/reference/make.soapgrid.html
 create mode 100644 docs/reference/mexdolphins.html
 create mode 100644 docs/reference/obs_exp.html
 create mode 100644 docs/reference/plot.dsm.html
 create mode 100644 docs/reference/plot.dsm.var.html
 create mode 100644 docs/reference/plot_pred_by_term.html
 create mode 100644 docs/reference/predict.dsm.html
 create mode 100644 docs/reference/predict.fake_ddf.html
 create mode 100644 docs/reference/print.dsm.html
 create mode 100644 docs/reference/print.dsm.var.html
 create mode 100644 docs/reference/print.dsm_varprop.html
 create mode 100644 docs/reference/print.summary.dsm.var.html
 create mode 100644 docs/reference/print.summary.dsm_varprop.html
 create mode 100644 docs/reference/rqgam.check.html
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 create mode 100644 docs/reference/rqgam_check.html
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diff --git a/.gitignore b/.gitignore
index 7ae87a9..4b65d5a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,2 @@
 *.Rproj
 .Rproj.user
-docs
diff --git a/docs/404.html b/docs/404.html
new file mode 100644
index 0000000..3d990f5
--- /dev/null
+++ b/docs/404.html
@@ -0,0 +1,100 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
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+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
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diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
new file mode 100644
index 0000000..d9502b5
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+++ b/docs/LICENSE-text.html
@@ -0,0 +1,753 @@
+<!DOCTYPE html>
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+<pre>                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+
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+    Copyright (C) &lt;year&gt;  &lt;name of author&gt;
+
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+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
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+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see &lt;http://www.gnu.org/licenses/&gt;.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    &lt;program&gt;  Copyright (C) &lt;year&gt;  &lt;name of author&gt;
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+&lt;http://www.gnu.org/licenses/&gt;.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
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+Public License instead of this License.  But first, please read
+&lt;http://www.gnu.org/philosophy/why-not-lgpl.html&gt;.
+</pre>
+
+  </main></div>
+
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diff --git a/docs/articles/index.html b/docs/articles/index.html
new file mode 100644
index 0000000..fea592b
--- /dev/null
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@@ -0,0 +1,83 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+  <a class="nav-link" href="../reference/index.html">Reference</a>
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+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
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+    <div class="section ">
+      <h3>All vignettes</h3>
+      <div class="section-desc"></div>
+
+      <dl><dt><a href="lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></dt>
+        <dd><p>Example spatial analysis of pantropical spotted dolphins.</p>
+      </dd></dl></div>
+  </main></div>
+
+
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+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
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diff --git a/docs/articles/lines_gomex/mexico-analysis.html b/docs/articles/lines_gomex/mexico-analysis.html
new file mode 100644
index 0000000..e6c21c8
--- /dev/null
+++ b/docs/articles/lines_gomex/mexico-analysis.html
@@ -0,0 +1,898 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="description" content="Example spatial analysis of pantropical spotted dolphins.
+">
+<title>Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico • dsm</title>
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+      <img src="" class="logo" alt=""><h1>Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</h1>
+                        <h4 data-toc-skip class="author">David L Miller</h4>
+            <address class="author_afil">
+      CREEM, Univ of St Andrews<br><h4 data-toc-skip class="date">May 2024</h4>
+      
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/vignettes/lines_gomex/mexico-analysis.Rmd" class="external-link"><code>vignettes/lines_gomex/mexico-analysis.Rmd</code></a></small>
+      <div class="d-none name"><code>mexico-analysis.Rmd</code></div>
+    </address>
+</div>
+
+    
+    
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+<p>The analysis is based on a dataset of observations of pantropical dolphins in the Gulf of Mexico (shipped with Distance 6.0 and later). For convenience the data are bundled in an <code>R</code>-friendly format, although all of the code necessary for creating the data from the Distance project files is available <a href="http://github.com/dill/mexico-data" class="external-link">on github</a>. The OBIS-SEAMAP page for the data may be found at the <a href="http://seamap.env.duke.edu/dataset/25" class="external-link">SEFSC GoMex Oceanic 1996</a> survey page.</p>
+<p>The intention here is to highlight the features of the <code>dsm</code> package, rather than perform a full analysis of the data. For that reason, some important steps are not fully explored. Some familiarity with density surface modelling <span class="citation">(Miller, Burt, Rexstad, &amp; Thomas, 2013)</span> <span class="citation">(Hedley &amp; Buckland, 2004)</span> is assumed.</p>
+</div>
+<div class="section level2">
+<h2 id="preamble">Preamble<a class="anchor" aria-label="anchor" href="#preamble"></a>
+</h2>
+<p>Before we start, we load the <code>dsm</code> package (and its dependencies) and set some options:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># plotting options</span></span>
+<span><span class="va">gg.opts</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html" class="external-link">theme</a></span><span class="op">(</span>panel.grid.major<span class="op">=</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/element.html" class="external-link">element_blank</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>                 panel.grid.minor<span class="op">=</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/element.html" class="external-link">element_blank</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>                 panel.background<span class="op">=</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/element.html" class="external-link">element_blank</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<p>In order to run this vignette, you’ll need to install a few R packages. This can be done via the following call to <code>install.packages</code>:</p>
+<pre><code><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"dsm"</span>, <span class="st">"Distance"</span>, <span class="st">"knitr"</span>, <span class="st">"distill"</span>, <span class="st">"ggplot2"</span>, <span class="st">"rgdal"</span>,</span>
+<span>                   <span class="st">"maptools"</span>, <span class="st">"plyr"</span>, <span class="st">"tweedie"</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="section level2">
+<h2 id="the-data">The data<a class="anchor" aria-label="anchor" href="#the-data"></a>
+</h2>
+<p>Most of the data we need is included in the <code>dsm</code> package, but two additional objects needed for plotting are required and can be downloaded <a href="https://examples.distancesampling.org/dsm-line-dolphins/mexdolphins-extra.rda" class="external-link">here</a> and should be put into the same directory as this file. The data can then be loaded into R using the following code:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/load.html" class="external-link">load</a></span><span class="op">(</span><span class="st">"mexdolphins-extra.rda"</span><span class="op">)</span></span></code></pre></div>
+<p>This should add the objects <code>survey.area</code> and <code>pred.polys</code> to your environment.</p>
+<div class="section level3">
+<h3 id="observation-and-segment-data">Observation and segment data<a class="anchor" aria-label="anchor" href="#observation-and-segment-data"></a>
+</h3>
+<p>All of the data for this analysis has been nicely pre-formatted and is shipped with <code>dsm</code>. Loading that data, we can see that we have four <code>data.frame</code>s, the first few lines of each are shown:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></code></pre></div>
+<p><code>segdata</code> holds the segment data: the transects have already been “chopped” into segments.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">segdata</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##   longitude latitude        x        y Effort Transect.Label Sample.Label depth</span></span>
+<span><span class="co">## 1 -86.92712 29.94378 836105.9 -1011416  13800       19960417   19960417-1 135.0</span></span>
+<span><span class="co">## 2 -86.83176 29.84030 846012.9 -1021407  14000       19960417   19960417-2 147.7</span></span>
+<span><span class="co">## 3 -86.74445 29.75279 855022.6 -1029785  14000       19960417   19960417-3 152.1</span></span>
+<span><span class="co">## 4 -86.65230 29.65522 864610.3 -1039168  13900       19960417   19960417-4 163.8</span></span>
+<span><span class="co">## 5 -86.56648 29.56088 873598.1 -1048266  13800       19960417   19960417-5 179.7</span></span>
+<span><span class="co">## 6 -86.49290 29.49000 881203.7 -1055004  13800       19960417   19960417-6 188.5</span></span></code></pre>
+<p><code>distdata</code> holds the distance sampling data that will be used to fit the detection function.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##     object size  distance Effort detected beaufort latitude longitude</span></span>
+<span><span class="co">## 45      45   21 3296.6363  36300        1        4 27.72872 -86.00159</span></span>
+<span><span class="co">## 61      61  150  929.1937  17800        1        4 25.99896 -87.62712</span></span>
+<span><span class="co">## 63      63  125 6051.0009  21000        1        2 26.00693 -87.94881</span></span>
+<span><span class="co">## 85      85   75 5499.6971  21800        1        1 27.50344 -90.44891</span></span>
+<span><span class="co">## 114    114   50 7258.9837  13400        1        3 27.40568 -94.99483</span></span>
+<span><span class="co">## 120    120   45 1454.7962  20900        1        5 26.01765 -95.97449</span></span>
+<span><span class="co">##              x        y</span></span>
+<span><span class="co">## 45  948000.065 -1236192</span></span>
+<span><span class="co">## 61  812161.653 -1436899</span></span>
+<span><span class="co">## 63  780969.520 -1438985</span></span>
+<span><span class="co">## 85  528656.807 -1297833</span></span>
+<span><span class="co">## 114  95910.149 -1324562</span></span>
+<span><span class="co">## 120   2477.665 -1473909</span></span></code></pre>
+<p><code>obsdata</code> links the distance data to the segments.</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">obsdata</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##     object Sample.Label size  distance Effort</span></span>
+<span><span class="co">## 45      45   19960421-9   21 3296.6363  36300</span></span>
+<span><span class="co">## 61      61   19960423-7  150  929.1937  17800</span></span>
+<span><span class="co">## 63      63   19960423-9  125 6051.0009  21000</span></span>
+<span><span class="co">## 85      85   19960427-1   75 5499.6971  21800</span></span>
+<span><span class="co">## 114    114   19960430-8   50 7258.9837  13400</span></span>
+<span><span class="co">## 120    120   19960501-5   45 1454.7962  20900</span></span></code></pre>
+<p><code>preddata</code> holds the prediction grid (which includes all the necessary covariates).</p>
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">preddata</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##   latitude longitude        x          y depth      area</span></span>
+<span><span class="co">## 1 30.08333 -87.58333 774402.9 -1002759.1    35 271236913</span></span>
+<span><span class="co">## 2 30.08333 -87.41667 789688.6 -1001264.5    30 271236913</span></span>
+<span><span class="co">## 3 30.08333 -87.25000 804971.3  -999740.6    27 271236913</span></span>
+<span><span class="co">## 4 30.08333 -87.08333 820251.1  -998187.5    22 271236913</span></span>
+<span><span class="co">## 5 30.08333 -86.91667 835528.0  -996605.2    46 271236913</span></span>
+<span><span class="co">## 6 29.91667 -87.75000 760783.1 -1021810.3    14 271236913</span></span></code></pre>
+<p>Typically (i.e. for other datasets) it will be necessary divide the transects into segments, and allocate observations to the correct segments using a GIS or other similar package<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content="&lt;p&gt;These operations can be performed in R using the &lt;code&gt;sp&lt;/code&gt; and &lt;code&gt;rgeos&lt;/code&gt; packages. It may, however, be easier to perform these operations in GIS such as ArcGIS – in which case the MGET Toolbox may be useful.&lt;/p&gt;"><sup>1</sup></a>, before starting an analysis using <code>dsm</code>.</p>
+</div>
+<div class="section level3">
+<h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class="anchor" aria-label="anchor" href="#shapefiles-and-converting-units"></a>
+</h3>
+<p>Often data in a spatial analysis comes from many different sources. It is important to ensure that the measurements to be used in the analysis are in compatible units, otherwise the resulting estimates will be incorrect or hard to interpret. Having all of our measurements in SI units from the outset removes the need for conversion later, making life much easier.</p>
+<p>The data are already in the appropriate units (Northings and Eastings: kilometres from a centroid, projected using the <a href="https://en.wikipedia.org/wiki/Lambert_conformal_conic_projection" class="external-link">North American Lambert Conformal Conic projection</a>).</p>
+<p>There is extensive literature about when particular projections of latitude and longitude are appropriate and we highly recommend the reader review this for their particular study area; <span class="citation">(Bivand, Pebesma, &amp; Gómez-Rubio, 2008)</span> is a good starting point. The other data frames have already had their measurements appropriately converted. By convention the directions are named <code>x</code> and <code>y</code>.</p>
+<p>Using latitude and longitude when performing spatial smoothing can be problematic when certain smoother bases are used. In particular when bivariate isotropic bases are used the non-isotropic nature of latitude and longitude is inconsistent (moving one degree in one direction is not the same as moving one degree in the other).</p>
+<p>We give an example of projecting the polygon that defines the survey area (which as simply been read into R using <code>readShapeSpatial</code> from a shapefile produced by GIS).</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://had.co.nz/plyr" class="external-link">plyr</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># tell R that the survey.area object is currently in lat/long</span></span>
+<span><span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/is.projected.html" class="external-link">proj4string</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/CRS-class.html" class="external-link">CRS</a></span><span class="op">(</span><span class="st">"+proj=longlat +datum=WGS84"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">predsf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">pred.polys</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">area.sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">area.sf</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="st">"WGS84"</span></span>
+<span><span class="va">area.sf.proj</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">area.sf</span>, crs <span class="op">=</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">predsf</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Convert preddata to a spatial object</span></span>
+<span><span class="va">preddata_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">preddata</span>, coords<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>, <span class="st">"y"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">preddata_sf</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">area.sf.proj</span><span class="op">)</span></span>
+<span><span class="co"># Perform the intersection</span></span>
+<span><span class="va">preddata_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">preddata_sf</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
+<span><span class="co">## all geometries</span></span></code></pre>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">coords_preddata</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">preddata_sf</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">coords_preddata</span><span class="op">$</span><span class="va">X</span></span>
+<span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">y</span> <span class="op">&lt;-</span> <span class="va">coords_preddata</span><span class="op">$</span><span class="va">Y</span> </span></code></pre></div>
+<p>Next bit of code works with the <code>study.area</code> outline, massaging it for use with soap film smoother. If you will not be using soap film smoothers, the code is extraneous.</p>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># proj 4 string</span></span>
+<span><span class="co"># using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/</span></span>
+<span><span class="va">lcc_proj4</span> <span class="op">&lt;-</span> <span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/CRS-class.html" class="external-link">CRS</a></span><span class="op">(</span><span class="st">"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs "</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># project using LCC</span></span>
+<span><span class="va">survey.area</span> <span class="op">&lt;-</span> <span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/spTransform.html" class="external-link">spTransform</a></span><span class="op">(</span><span class="va">survey.area</span>, CRSobj<span class="op">=</span><span class="va">lcc_proj4</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># simplify the object</span></span>
+<span><span class="va">survey.area</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">@</span><span class="va">polygons</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">@</span><span class="va">Polygons</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">@</span><span class="va">coords</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>, <span class="st">"y"</span><span class="op">)</span></span></code></pre></div>
+<p>The below code generates this plot, which shows the survey area with the transect lines overlaid (using data from <code>segdata</code>).</p>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">segdata_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">segdata</span>, coords <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">segdata_sf</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">area.sf.proj</span><span class="op">)</span></span>
+<span><span class="co"># study area outline and segment centres</span></span>
+<span><span class="va">plot_segments</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">area.sf.proj</span>, fill<span class="op">=</span><span class="st">"lightblue"</span>, color <span class="op">=</span> <span class="st">"blue"</span>, linewidth<span class="op">=</span><span class="fl">.1</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"black"</span>, linewidth<span class="op">=</span><span class="fl">.3</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title<span class="op">=</span><span class="st">"1996 SE Fisheries Science Center Gulf of Mexico cruise"</span>,</span>
+<span>       subtitle <span class="op">=</span> <span class="st">"Points are segment centres"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_fill_viridis_c</a></span><span class="op">(</span>option <span class="op">=</span> <span class="st">"magma"</span>, guide <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
+<span><span class="va">plot_segments</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/areawithtransects-1.png"><!-- --></p>
+<p>Also note that since we have projected our prediction grid, the “squares” do not look quite like squares. So for plotting we will use the polygons that we have saved, these polygons (stored in <code>pred.polys</code>) are read from a shapefile created in GIS, the object itself is of class <code>SpatialPolygons</code> from the <code>sp</code> package. It needs project for proper plotting using functions in the <code>sf</code> package. predsf &lt;- st_as_sf(pred.polys)</p>
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">predsf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">pred.polys</span><span class="op">)</span></span>
+<span><span class="co"># plot as projected</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_geometry</a></span><span class="op">(</span><span class="va">predsf</span><span class="op">)</span>, axes<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/projection-compare-1.png"><!-- --></p>
+<p>Tips on plotting polygons are available from <a href="https://ggplot2.tidyverse.org/reference/ggsf.html?q=geom_sf#null" class="external-link">the <code>ggplot2</code> pkg_down</a>.</p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="exploratory-data-analysis">Exploratory data analysis<a class="anchor" aria-label="anchor" href="#exploratory-data-analysis"></a>
+</h2>
+<div class="section level3">
+<h3 id="distance-data">Distance data<a class="anchor" aria-label="anchor" href="#distance-data"></a>
+</h3>
+<p>The top panels of the EDA plots below show histograms of observed distances and cluster size, while the bottom panels show the relationship between observed distance and observed cluster size, and the relationship between observed distance and Beaufort sea state. The plots show that there is some relationship between cluster size and observed distance (fewer smaller clusters seem to be seen at larger distances).</p>
+<p>The following code generates the EDA plots:</p>
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># histograms</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span>,main<span class="op">=</span><span class="st">""</span>,xlab<span class="op">=</span><span class="st">"Distance (m)"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">size</span>,main<span class="op">=</span><span class="st">""</span>,xlab<span class="op">=</span><span class="st">"Cluster size"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># plots of distance vs. cluster size</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span>, <span class="va">distdata</span><span class="op">$</span><span class="va">size</span>, main<span class="op">=</span><span class="st">""</span>, xlab<span class="op">=</span><span class="st">"Distance (m)"</span>,</span>
+<span>     ylab<span class="op">=</span><span class="st">"Group size"</span>, pch<span class="op">=</span><span class="fl">19</span>, cex<span class="op">=</span><span class="fl">0.5</span>, col<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/grDevices/gray.html" class="external-link">gray</a></span><span class="op">(</span><span class="fl">0.7</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># lm fit</span></span>
+<span><span class="va">l.dat</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>distance<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">8000</span>,len<span class="op">=</span><span class="fl">1000</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">lo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">size</span><span class="op">~</span><span class="va">distance</span>, data<span class="op">=</span><span class="va">distdata</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/lines.html" class="external-link">lines</a></span><span class="op">(</span><span class="va">l.dat</span><span class="op">$</span><span class="va">distance</span>, <span class="fu"><a href="https://rdrr.io/r/base/vector.html" class="external-link">as.vector</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">lo</span>,<span class="va">l.dat</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span>,<span class="va">distdata</span><span class="op">$</span><span class="va">beaufort</span>, main<span class="op">=</span><span class="st">""</span>, xlab<span class="op">=</span><span class="st">"Distance (m)"</span>,</span>
+<span>     ylab<span class="op">=</span><span class="st">"Beaufort sea state"</span>, pch<span class="op">=</span><span class="fl">19</span>, cex<span class="op">=</span><span class="fl">0.5</span>, col<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/grDevices/gray.html" class="external-link">gray</a></span><span class="op">(</span><span class="fl">0.7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/EDA-plots-1.png"><!-- --></p>
+<p>Top row, left to right: histograms of distance and cluster size; bottom row: plot of distance against cluster size and plot of distances against Beaufort sea state.</p>
+</div>
+<div class="section level3">
+<h3 id="spatial-data">Spatial data<a class="anchor" aria-label="anchor" href="#spatial-data"></a>
+</h3>
+<p>Looking separately at the spatial data without thinking about the distances, we can plot the observed group sizes in space (point size is proportional to the group size for each observation). Circle size indicates the size of the group in the observation. There are rather large areas with no observations, which might cause our variance estimates for abundance to be rather large. We also see the depth data which we will use depth later as an explanatory covariate in our spatial model.</p>
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">prediction_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_make_grid.html" class="external-link">st_make_grid</a></span><span class="op">(</span><span class="va">area.sf.proj</span>, cellsize <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">9000</span>,<span class="fl">9000</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">prediction_grid_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/sf.html" class="external-link">st_sf</a></span><span class="op">(</span>geometry <span class="op">=</span> <span class="va">prediction_grid</span><span class="op">)</span></span>
+<span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">prediction_grid_sf</span>, <span class="va">preddata_sf</span>, join <span class="op">=</span> <span class="va">st_nearest_feature</span><span class="op">)</span></span>
+<span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
+<span><span class="co">## all geometries</span></span></code></pre>
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">depth</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill<span class="op">=</span><span class="va">depth</span><span class="op">)</span>, color<span class="op">=</span><span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
+<span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Spotted dolphins, Gulf of Mexico"</span>,</span>
+<span>              subtitle <span class="op">=</span> <span class="st">"Depth in meters, size of detected dolphin groups"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">xlab</a></span><span class="op">(</span><span class="st">"Longitude"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">ylab</a></span><span class="op">(</span><span class="st">"Latitude"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, size<span class="op">=</span><span class="va">size</span><span class="op">)</span>, data<span class="op">=</span><span class="va">distdata</span>, colour<span class="op">=</span><span class="st">"red"</span>,alpha<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/AsIs.html" class="external-link">I</a></span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_fill_viridis_c</a></span><span class="op">(</span>option <span class="op">=</span> <span class="st">"viridis"</span>, direction <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span><span class="va">depth</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/spatialEDA-1.png"><!-- --></p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="estimating-the-detection-function">Estimating the detection function<a class="anchor" aria-label="anchor" href="#estimating-the-detection-function"></a>
+</h2>
+<p>We use the <code>ds</code> function in the package <code>Distance</code> to fit the detection function. (The <code>Distance</code> package is intended to make standard distance sampling in <code>R</code> relatively straightforward. For a more flexible but more complex alternative, see the function <code>ddf</code> in the <code>mrds</code> library.)</p>
+<p>First, loading the <code>Distance</code> library:</p>
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></code></pre></div>
+<p>We can then fit a detection function with hazard-rate key with no adjustment terms:</p>
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">detfc.hr.null</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span><span class="op">)</span>, key<span class="op">=</span><span class="st">"hr"</span>, adjustment<span class="op">=</span><span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, :</span></span>
+<span><span class="co">## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible</span></span>
+<span><span class="co">## problem in data (e.g., spike near zero distance).</span></span></code></pre>
+<p>Calling <code>summary</code> gives us information about parameter estimates, probability of detection, AIC, etc:</p>
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">detfc.hr.null</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Summary for distance analysis </span></span>
+<span><span class="co">## Number of observations :  47 </span></span>
+<span><span class="co">## Distance range         :  0  -  7847.467 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model       : Hazard-rate key function </span></span>
+<span><span class="co">## AIC         :  841.1841 </span></span>
+<span><span class="co">## Optimisation:  mrds (nlminb) </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Detection function parameters</span></span>
+<span><span class="co">## Scale coefficient(s):  </span></span>
+<span><span class="co">##             estimate        se</span></span>
+<span><span class="co">## (Intercept) 7.986391 0.5239151</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Shape coefficient(s):  </span></span>
+<span><span class="co">##                estimate         se</span></span>
+<span><span class="co">## (Intercept) 0.000237466 0.02958901</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##                       Estimate         SE        CV</span></span>
+<span><span class="co">## Average p            0.5918795  0.1667006 0.2816462</span></span>
+<span><span class="co">## N in covered region 79.4080596 23.5573051 0.2966614</span></span></code></pre>
+<p>The following code generates a plot of the fitted detection function (left) and quantile-quantile plot (right):</p>
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">detfc.hr.null</span>, showpoints<span class="op">=</span><span class="cn">FALSE</span>, pl.den<span class="op">=</span><span class="fl">0</span>, lwd<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/Distance/man/gof_ds.html" class="external-link">gof_ds</a></span><span class="op">(</span><span class="va">detfc.hr.null</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Goodness of fit results for ddf object</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Distance sampling Cramer-von Mises test (unweighted)</span></span>
+<span><span class="co">## Test statistic = 0.0976846 p-value = 0.596345</span></span></code></pre>
+<p><img src="mexico-analysis_files/figure-html/hr-detfct-1.png"><img src="mexico-analysis_files/figure-html/hr-detfct-2.png"></p>
+<p>The quantile-quantile plot show relatively good goodness of fit for the hazard-rate detection function.</p>
+<div class="section level3">
+<h3 id="adding-covariates-to-the-detection-function">Adding covariates to the detection function<a class="anchor" aria-label="anchor" href="#adding-covariates-to-the-detection-function"></a>
+</h3>
+<p>It is common to include covariates in the detection function (so-called Multiple Covariate Distance Sampling or MCDS). In this dataset there are two covariates that were collected on each individual: Beaufort sea state and size. For brevity we fit only a hazard-rate detection functions with the sea state included as a factor covariate as follows:</p>
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">detfc.hr.beau</span><span class="op">&lt;-</span><span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span><span class="op">)</span>, formula<span class="op">=</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">beaufort</span><span class="op">)</span>,</span>
+<span>                  key<span class="op">=</span><span class="st">"hr"</span>, adjustment<span class="op">=</span><span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
+<p>Again looking at the <code>summary</code>,</p>
+<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">detfc.hr.beau</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Summary for distance analysis </span></span>
+<span><span class="co">## Number of observations :  47 </span></span>
+<span><span class="co">## Distance range         :  0  -  7847.467 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model       : Hazard-rate key function </span></span>
+<span><span class="co">## AIC         :  843.7115 </span></span>
+<span><span class="co">## Optimisation:  mrds (nlminb) </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Detection function parameters</span></span>
+<span><span class="co">## Scale coefficient(s):  </span></span>
+<span><span class="co">##                         estimate        se</span></span>
+<span><span class="co">## (Intercept)           7.66291936  1.078972</span></span>
+<span><span class="co">## as.factor(beaufort)2  2.24809809 14.754677</span></span>
+<span><span class="co">## as.factor(beaufort)3  0.28581820  1.020809</span></span>
+<span><span class="co">## as.factor(beaufort)4  0.07133776  1.225360</span></span>
+<span><span class="co">## as.factor(beaufort)5 -0.36421115  1.540324</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Shape coefficient(s):  </span></span>
+<span><span class="co">##              estimate        se</span></span>
+<span><span class="co">## (Intercept) 0.2994644 0.5184204</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##                       Estimate         SE        CV</span></span>
+<span><span class="co">## Average p            0.5420677  0.1753555 0.3234937</span></span>
+<span><span class="co">## N in covered region 86.7050383 29.4646397 0.3398262</span></span></code></pre>
+<p>Here the detection function with covariates does not give a lower AIC than the model without covariates (843.71 vs. 841.18 for the hazard-rate model without covariates). Looking back to the bottom-right panel of the EDA plots, we can see there is not a discernible pattern in the plot of Beaufort vs distance.</p>
+<p>For brevity, detection function model selection has been omitted here. In practise we would fit many different forms for the detection function (and select a model based on goodness of fit testing and AIC).</p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="fitting-a-dsm">Fitting a DSM<a class="anchor" aria-label="anchor" href="#fitting-a-dsm"></a>
+</h2>
+<p>Before fitting a <code>dsm</code> model, the data must be segmented; this consists of chopping up the transects and attributing counts to each of the segments. As mentioned above, these data have already been segmented.</p>
+<div class="section level3">
+<h3 id="a-simple-model">A simple model<a class="anchor" aria-label="anchor" href="#a-simple-model"></a>
+</h3>
+<p>We begin with a very simple model. We assume that the number of individuals in each segment are quasi-Poisson distributed and that they are a smooth function of their spatial coordinates (note that the formula is exactly as one would specify to <code>gam</code> in <code>mgcv</code>). By setting <code>group=TRUE</code>, the abundance of clusters/groups rather than individuals can be estimated (though we ignore this here). Note we set <code>method="REML"</code> to ensure that smooth terms are estimated reliably.</p>
+<p>Running the model:</p>
+<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">dsm.xy</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span></code></pre></div>
+<p>We can then obtain a summary of the fitted model:</p>
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Family: quasipoisson </span></span>
+<span><span class="co">## Link function: log </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Formula:</span></span>
+<span><span class="co">## count ~ s(x, y) + offset(off.set)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parametric coefficients:</span></span>
+<span><span class="co">##             Estimate Std. Error t value Pr(&gt;|t|)    </span></span>
+<span><span class="co">## (Intercept)   -18.20       0.53  -34.34   &lt;2e-16 ***</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Approximate significance of smooth terms:</span></span>
+<span><span class="co">##         edf Ref.df     F  p-value    </span></span>
+<span><span class="co">## s(x,y) 24.8  27.49 2.354 0.000733 ***</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## R-sq.(adj) =  0.121   Deviance explained = 43.4%</span></span>
+<span><span class="co">## -REML = 936.04  Scale est. = 94.367    n = 387</span></span></code></pre>
+<p>The exact interpretation of the model summary results can be found in <span class="citation">(S. N. Wood, 2017)</span>; here we can see various information about the smooth components fitted and general model statistics.</p>
+<p>We can use the deviance explained to compare between models<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content='&lt;p&gt;Note though that the adjusted &lt;span class="math inline"&gt;\(R^2\)&lt;/span&gt; for the model is defined as the proportion of variance explained but the “original” variance used for comparison doesn’t include the offset (the area (or effective of the segments). It is therefore not recommended for one to directly interpret the &lt;span class="math inline"&gt;\(R^2\)&lt;/span&gt; value (see the &lt;code&gt;summary.gam&lt;/code&gt; manual page for further details).&lt;/p&gt;'><sup>2</sup></a>.</p>
+<p>We can also get a rough idea of what the smooth of space looks like using <code>vis.gam</code> (white/yellow indicates high values, red low indicates low values):</p>
+<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/vis.gam.html" class="external-link">vis.gam</a></span><span class="op">(</span><span class="va">dsm.xy</span>, plot.type<span class="op">=</span><span class="st">"contour"</span>, view<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span>, asp<span class="op">=</span><span class="fl">1</span>, type<span class="op">=</span><span class="st">"response"</span>, contour.col<span class="op">=</span><span class="st">"black"</span>, n.grid<span class="op">=</span><span class="fl">100</span><span class="op">)</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/visgam1-1.png"><!-- --></p>
+<p>The <code>type="response"</code> argument ensures that the plot is on the scale of abundance but the values are relative (as the offsets are set to be their median values). This means that the plot is useful to get an idea of the general shape of the smooth but cannot be interpreted directly.</p>
+</div>
+<div class="section level3">
+<h3 id="adding-another-environmental-covariate-to-the-spatial-model">Adding another environmental covariate to the spatial model<a class="anchor" aria-label="anchor" href="#adding-another-environmental-covariate-to-the-spatial-model"></a>
+</h3>
+<p>The data set also contains a <code>depth</code> covariate (which we plotted above). We can include in the model very simply:</p>
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">dsm.xy.depth</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span>,k<span class="op">=</span><span class="fl">10</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">depth</span>,k<span class="op">=</span><span class="fl">20</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Family: quasipoisson </span></span>
+<span><span class="co">## Link function: log </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Formula:</span></span>
+<span><span class="co">## count ~ s(x, y, k = 10) + s(depth, k = 20) + offset(off.set)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parametric coefficients:</span></span>
+<span><span class="co">##             Estimate Std. Error t value Pr(&gt;|t|)    </span></span>
+<span><span class="co">## (Intercept)  -18.741      1.236  -15.16   &lt;2e-16 ***</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Approximate significance of smooth terms:</span></span>
+<span><span class="co">##            edf Ref.df     F p-value  </span></span>
+<span><span class="co">## s(x,y)   6.062  7.371 0.923  0.5038  </span></span>
+<span><span class="co">## s(depth) 9.443 11.466 1.585  0.0824 .</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## R-sq.(adj) =  0.0909   Deviance explained = 34.3%</span></span>
+<span><span class="co">## -REML = 939.52  Scale est. = 124.9     n = 387</span></span></code></pre>
+<p>Here we see a drop in deviance explained, so perhaps this model is not as useful as the first. We discuss setting the <code>k</code> parameter in <a href="#model-checking">Model checking</a>, below.</p>
+<p>Setting <code>select=TRUE</code> here (as an argument to <code>gam</code>) would impose extra shrinkage terms on each smooth in the model (allowing smooth terms to be removed from the model during fitting; see <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">?gam</a></code> for more information). This is not particularly useful here, so we do not include it. However when there are many environmental predictors is in the model this can be a good way (along with looking at <span class="math inline">\(p\)</span>-values) to perform term selection.</p>
+<p>Simply calling <code>plot</code> on the model object allows us to look at the relationship between depth and the linear predictor:</p>
+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, select<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/dsm-xy-depth-depth-1.png"><!-- --></p>
+<p>Omitting the argument <code>select</code> in the call to <code>plot</code> will plot each of the smooth terms, one at a time.</p>
+</div>
+<div class="section level3">
+<h3 id="spatial-models-when-there-are-covariates-in-the-detection-function">Spatial models when there are covariates in the detection function<a class="anchor" aria-label="anchor" href="#spatial-models-when-there-are-covariates-in-the-detection-function"></a>
+</h3>
+<p>The code to fit the DSM when there are covariates in the detection function is similar to the other models, above. However since the detection function has observation-level covariates, we must estimate the abundance per segment using a Horvitz-Thompson-like estimator before modelling, so we change the response to be <code>abundance.est</code>:</p>
+<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">dsm.est.xy</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">abundance.est</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.beau</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span></code></pre></div>
+<p>As we can see, the <code>summary</code> results are rather similar:</p>
+<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.est.xy</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Family: quasipoisson </span></span>
+<span><span class="co">## Link function: log </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Formula:</span></span>
+<span><span class="co">## abundance.est ~ s(x, y) + offset(off.set)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parametric coefficients:</span></span>
+<span><span class="co">##             Estimate Std. Error t value Pr(&gt;|t|)    </span></span>
+<span><span class="co">## (Intercept) -18.0233     0.4739  -38.03   &lt;2e-16 ***</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Approximate significance of smooth terms:</span></span>
+<span><span class="co">##          edf Ref.df     F p-value    </span></span>
+<span><span class="co">## s(x,y) 24.64  27.44 2.349 0.00029 ***</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## R-sq.(adj) =  0.129   Deviance explained = 42.2%</span></span>
+<span><span class="co">## -REML = 1043.6  Scale est. = 174.1     n = 387</span></span></code></pre>
+<p>As is the resulting spatial smooth (though the resulting surface is somewhat “amplified”):</p>
+<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/vis.gam.html" class="external-link">vis.gam</a></span><span class="op">(</span><span class="va">dsm.est.xy</span>, plot.type<span class="op">=</span><span class="st">"contour"</span>, view<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span>, asp<span class="op">=</span><span class="fl">1</span>, type<span class="op">=</span><span class="st">"response"</span>, zlim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">300</span><span class="op">)</span>, contour.col<span class="op">=</span><span class="st">"black"</span>, n.grid<span class="op">=</span><span class="fl">100</span><span class="op">)</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/visgam5-1.png"><!-- --></p>
+</div>
+<div class="section level3">
+<h3 id="other-response-distributions">Other response distributions<a class="anchor" aria-label="anchor" href="#other-response-distributions"></a>
+</h3>
+<p>Often the quasi-Poisson distribution doesn’t give adequate flexibility and does not capture the overdispersion in the response data (see <a href="#model-checking">Model checking</a> and <a href="#model-selection">Model selection</a> below), so below we illustrate two additional distributions that can be used with count data.</p>
+<p>For the models in this section, we’ll move back to the <code>count</code> response, though the estimated abundance would also work.</p>
+<div class="section level4">
+<h4 id="tweedie">Tweedie<a class="anchor" aria-label="anchor" href="#tweedie"></a>
+</h4>
+<p>Response distributions other than the quasi-Poisson can be used, for example the Tweedie distribution. The Tweedie distribution is available in <code>dsm</code> by setting <code>family=tw()</code>.</p>
+<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">dsm.xy.tweedie</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, family<span class="op">=</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></span><span class="op">(</span><span class="op">)</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Family: Tweedie(p=1.347) </span></span>
+<span><span class="co">## Link function: log </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Formula:</span></span>
+<span><span class="co">## count ~ s(x, y) + offset(off.set)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parametric coefficients:</span></span>
+<span><span class="co">##             Estimate Std. Error t value Pr(&gt;|t|)    </span></span>
+<span><span class="co">## (Intercept) -17.2651     0.2363  -73.08   &lt;2e-16 ***</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Approximate significance of smooth terms:</span></span>
+<span><span class="co">##          edf Ref.df     F p-value  </span></span>
+<span><span class="co">## s(x,y) 12.82  17.13 1.628  0.0562 .</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## R-sq.(adj) =  0.0684   Deviance explained = 26.8%</span></span>
+<span><span class="co">## -REML = 351.49  Scale est. = 60.26     n = 387</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="negative-binomial">Negative binomial<a class="anchor" aria-label="anchor" href="#negative-binomial"></a>
+</h4>
+<p>Though not used here there are, similarly, two options for the negative binomial distribution: <code>negbin</code> and <code>nb</code>. The former requires the user specification single parameter <code>theta</code> or a range of values for the parameter (specified as a vector), the latter estimates the value of <code>theta</code> during the model fitting process (and is generally faster). The latter is recommended for most users.</p>
+</div>
+</div>
+<div class="section level3">
+<h3 id="other-spatial-modelling-options">Other spatial modelling options<a class="anchor" aria-label="anchor" href="#other-spatial-modelling-options"></a>
+</h3>
+<p>There is a large literature on spatial modelling using GAMs, much of which can be harnessed in a DSM context. Here are a few highlights.</p>
+<div class="section level4">
+<h4 id="soap-film-smoothing">Soap film smoothing<a class="anchor" aria-label="anchor" href="#soap-film-smoothing"></a>
+</h4>
+<p>To account for a complex region (e.g., a region that includes peninsulae) we can use the soap film smoother <span class="citation">(Simon N. Wood, Bravington, &amp; Hedley, 2008)</span>.</p>
+<p>To use a soap film smoother for the spatial part of the model we must create a set of knots for the smoother to use. This is easily done using the <code><a href="../../reference/make.soapgrid.html">make.soapgrid()</a></code> function in <code>dsm</code>:</p>
+<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">soap.knots</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/make.soapgrid.html">make.soapgrid</a></span><span class="op">(</span><span class="va">survey.area</span>,<span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">15</span>,<span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<p>where the second argument specifies the number of points (in each direction) in the grid that will be used to create the knots (knots in the grid outside of <code>survey.area</code> are removed).</p>
+<p>As we saw in the exploratory analysis, some of the transect lines are outside of the survey area. These will cause the soap film smoother to fail, so we remove them:</p>
+<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">segdata</span><span class="op">$</span><span class="va">x</span>; <span class="va">y</span><span class="op">&lt;-</span><span class="va">segdata</span><span class="op">$</span><span class="va">y</span></span>
+<span><span class="va">onoff</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/inSide.html" class="external-link">inSide</a></span><span class="op">(</span>x<span class="op">=</span><span class="va">x</span>,y<span class="op">=</span><span class="va">y</span>, bnd<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">as.list</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/rm.html" class="external-link">rm</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span></span>
+<span><span class="va">segdata.soap</span> <span class="op">&lt;-</span> <span class="va">segdata</span><span class="op">[</span><span class="va">onoff</span>,<span class="op">]</span></span></code></pre></div>
+<p>Note that the <a href="https://github.com/dill/soap_checker" class="external-link"><code>soap_checker</code> script available here</a> can be useful in ensuring that the boundary, data and knots are in the correct format to use with the soap film smoother.</p>
+<p>We can run a model with both the <code>depth</code> covariate along with a spatial (soap film) smooth. Note that the <code>k</code> argument now refers to the complexity of the boundary smooth in the soap film, and the complexity of the film is controlled by the knots given in the <code>xt</code> argument.</p>
+<div class="sourceCode" id="cb48"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">dsm.xy.tweedie.soap</span><span class="op">&lt;-</span><span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, bs<span class="op">=</span><span class="st">"so"</span>, k<span class="op">=</span><span class="fl">15</span>, xt<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>bnd<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>            <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">depth</span><span class="op">)</span>,</span>
+<span>          family<span class="op">=</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></span><span class="op">(</span><span class="op">)</span>, method<span class="op">=</span><span class="st">"REML"</span>,</span>
+<span>          <span class="va">detfc.hr.null</span>, <span class="va">segdata.soap</span>, <span class="va">obsdata</span>, knots<span class="op">=</span><span class="va">soap.knots</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie.soap</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Family: Tweedie(p=1.356) </span></span>
+<span><span class="co">## Link function: log </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Formula:</span></span>
+<span><span class="co">## count ~ s(x, y, bs = "so", k = 15, xt = list(bnd = list(survey.area))) + </span></span>
+<span><span class="co">##     s(depth) + offset(off.set)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parametric coefficients:</span></span>
+<span><span class="co">##             Estimate Std. Error t value Pr(&gt;|t|)    </span></span>
+<span><span class="co">## (Intercept)  -18.018      0.405  -44.49   &lt;2e-16 ***</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Approximate significance of smooth terms:</span></span>
+<span><span class="co">##            edf Ref.df     F  p-value    </span></span>
+<span><span class="co">## s(x,y)   3.050 72.000 0.064 0.114944    </span></span>
+<span><span class="co">## s(depth) 5.135  6.196 3.895 0.000793 ***</span></span>
+<span><span class="co">## ---</span></span>
+<span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## R-sq.(adj) =  0.0653   Deviance explained = 33.1%</span></span>
+<span><span class="co">## -REML = 339.26  Scale est. = 54.022    n = 365</span></span></code></pre>
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="model-checking">Model checking<a class="anchor" aria-label="anchor" href="#model-checking"></a>
+</h2>
+<p>Fitting models is all well and good, but we’d like to confirm that the models we have are reasonable; <code>dsm</code> provides some functions for model checking.</p>
+<p>We can use <code>gam.check</code> to generate diagnostic plots:</p>
+<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></span><span class="op">(</span><span class="va">dsm.xy</span><span class="op">)</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/dsm.xy-check-1.png"><!-- --></p>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Method: REML   Optimizer: outer newton</span></span>
+<span><span class="co">## full convergence after 6 iterations.</span></span>
+<span><span class="co">## Gradient range [-7.105947e-08,-9.826417e-09]</span></span>
+<span><span class="co">## (score 936.0363 &amp; scale 94.3674).</span></span>
+<span><span class="co">## Hessian positive definite, eigenvalue range [5.800328,192.5304].</span></span>
+<span><span class="co">## Model rank =  30 / 30 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Basis dimension (k) checking results. Low p-value (k-index&lt;1) may</span></span>
+<span><span class="co">## indicate that k is too low, especially if edf is close to k'.</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##          k'  edf k-index p-value</span></span>
+<span><span class="co">## s(x,y) 29.0 24.8    1.01    0.99</span></span></code></pre>
+<p>These show that there is some deviation in the Q-Q plot. The “line” of points in the plot of the residuals vs. linear predictor plot corresponds to the zeros in the data.</p>
+<p>Note that as well as the plots, <code>gam.check</code> also produces information about the model fitting. Of particular interest to us is the last few lines that tell us about the basis size.</p>
+<p>The <code>k</code> parameter provided to <code>s</code> (and <code>te</code>) terms in <code>dsm</code> controls the complexity of the smooths in the model.</p>
+<p>By setting the <code>k</code> parameter we specify the largest complexity for that smooth term in the model; as long as this is high enough, we can be sure that there is enough flexibility. In the output from <code>gam.check</code> above, we can see that there is a “p-value” calculated for the size of the basis, this can be a good guide as to whether the basis size needs to be increased.</p>
+<p>The <code><a href="https://rdrr.io/pkg/mgcv/man/choose.k.html" class="external-link">?choose.k</a></code> manual page from <code>mgcv</code> gives further guidance and technical details on this matter.</p>
+<p>We can look at the same model form but with a Tweedie distribution specified as the response:</p>
+<div class="sourceCode" id="cb52"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/dsm.xy.tweedie-check-1.png"><!-- --></p>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Method: REML   Optimizer: outer newton</span></span>
+<span><span class="co">## full convergence after 8 iterations.</span></span>
+<span><span class="co">## Gradient range [-1.513175e-06,3.981813e-06]</span></span>
+<span><span class="co">## (score 351.486 &amp; scale 60.26034).</span></span>
+<span><span class="co">## Hessian positive definite, eigenvalue range [0.6230615,103.5094].</span></span>
+<span><span class="co">## Model rank =  30 / 30 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Basis dimension (k) checking results. Low p-value (k-index&lt;1) may</span></span>
+<span><span class="co">## indicate that k is too low, especially if edf is close to k'.</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##          k'  edf k-index p-value</span></span>
+<span><span class="co">## s(x,y) 29.0 12.8    0.73    0.34</span></span></code></pre>
+<p>The Q-Q plot now seems much better (closer to the <span class="math inline">\(y=x\)</span> line). In both plots the histogram of residuals is rather hard to interpret due to the large number of zeros in the data.</p>
+<p>Further guidance on interpreting <code>gam.check</code> output can be found in <span class="citation">(S. N. Wood, 2017)</span>.</p>
+<div class="section level4">
+<h4 id="randomised-quantile-residuals">Randomised quantile residuals<a class="anchor" aria-label="anchor" href="#randomised-quantile-residuals"></a>
+</h4>
+<p>In the top right panel of the above <code>gam.check</code> plots the residuals vs. linear predictor plot includes a odd line of predictions. These are an artifact of the link function, showing the exact zeros in the data. These can be misleading and distracting, making it difficult to see whether residuals show heteroskedasticity.</p>
+<p>Randomised quantile residuals <span class="citation">(Dunn &amp; Smyth, 1996)</span> avoid this issue by transforming the residuals to be exactly normally distributed. This makes the residuals vs. linear predictor plot much easier to interpret as it therefore doesn’t include the artifacts generated by the link function. These plots can be produced using <code>rqgam.check</code> in <code>dsm</code>:</p>
+<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../../reference/rqgam_check.html">rqgam_check</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/dsm.xy.tweedie-rqcheck-1.png"><!-- --></p>
+<p>Here we can see that there is no issue with heteroskedasticity (no increase in spread in the residuals vs. linear predictor plot with increasing values of the linear predictor). One can also plot these residuals against covariate values to check for pattern in the residuals.</p>
+<p>Note that in general, plots other than “Resids vs. linear pred.” should be interpreted with caution in the output of <code>rqgam.check</code> as the residuals generated are normal by construction (so for example the Q-Q plot and histogram of residuals will always look fine).</p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="model-selection">Model selection<a class="anchor" aria-label="anchor" href="#model-selection"></a>
+</h2>
+<p>Assuming that models have “passed” the checks in <code>gam.check</code>, <code>rqgam.check</code> and are sufficiently flexible, we may be left with a choice of which model is “best”. There are several methods for choosing the best model – AIC, REML/GCV scores, deviance explained, full cross-validation with test data and so on.</p>
+<p>Though this document does not intend to be a full analysis of the pantropical dolphin data, we can create a results table to compare the various models that have been fitted so far in terms of their abundance estimates and associated uncertainties.</p>
+<div class="sourceCode" id="cb55"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># make a data.frame to print out</span></span>
+<span><span class="va">mod_results</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="st">"Model name"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"`dsm.xy`"</span>, <span class="st">"`dsm.xy.depth`"</span>, <span class="st">"`dsm.xy.tweedie`"</span>, <span class="st">"`dsm.xy.tweedie.soap`"</span>,</span>
+<span>                                           <span class="st">"`dsm.est.xy`"</span><span class="op">)</span>,</span>
+<span>                          <span class="st">"Description"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Bivariate smooth of location, quasipoisson"</span>,</span>
+<span>                                            <span class="st">"Bivariate smooth of location, smooth of depth, quasipoisson"</span>,</span>
+<span>                                            <span class="st">"Bivariate smooth of location, smooth of depth, Tweedie"</span>,</span>
+<span>                                            <span class="st">"Soap film smooth of location, smooth of depth, Tweedie"</span>,</span>
+<span>                                            <span class="st">"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function"</span><span class="op">)</span>,</span>
+<span>                          <span class="st">"Deviance explained"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/unlist.html" class="external-link">unlist</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">dsm.xy</span>,</span>
+<span>                                                                      <span class="va">dsm.xy.depth</span>,</span>
+<span>                                                                      <span class="va">dsm.xy.tweedie</span>,</span>
+<span>                                                                      <span class="va">dsm.xy.tweedie.soap</span><span class="op">)</span>,</span>
+<span>                 <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">{</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">$</span><span class="va">dev.expl</span><span class="op">*</span><span class="fl">100</span>,<span class="fl">2</span><span class="op">)</span>,<span class="st">"%"</span><span class="op">)</span><span class="op">}</span><span class="op">)</span><span class="op">)</span>,<span class="cn">NA</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<p>We can then use the resulting <code>data.frame</code> to build a table of results using the <code>kable</code> function:</p>
+<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">kable</span><span class="op">(</span><span class="va">mod_results</span>, col.names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Model name"</span>, <span class="st">"Description"</span>, <span class="st">"Deviance explained"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="15%">
+<col width="70%">
+<col width="13%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Model name</th>
+<th align="left">Description</th>
+<th align="left">Deviance explained</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left"><code>dsm.xy</code></td>
+<td align="left">Bivariate smooth of location, quasipoisson</td>
+<td align="left">43.38%</td>
+</tr>
+<tr class="even">
+<td align="left"><code>dsm.xy.depth</code></td>
+<td align="left">Bivariate smooth of location, smooth of depth, quasipoisson</td>
+<td align="left">34.32%</td>
+</tr>
+<tr class="odd">
+<td align="left"><code>dsm.xy.tweedie</code></td>
+<td align="left">Bivariate smooth of location, smooth of depth, Tweedie</td>
+<td align="left">26.78%</td>
+</tr>
+<tr class="even">
+<td align="left"><code>dsm.xy.tweedie.soap</code></td>
+<td align="left">Soap film smooth of location, smooth of depth, Tweedie</td>
+<td align="left">33.05%</td>
+</tr>
+<tr class="odd">
+<td align="left"><code>dsm.est.xy</code></td>
+<td align="left">Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function</td>
+<td align="left">NA</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section level2">
+<h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="anchor" href="#abundance-estimation"></a>
+</h2>
+<p>Once a model has been checked and selected, we can make predictions over the grid and calculate abundance. The offset is stored in the <code>area</code> column<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content='&lt;p&gt;An earlier version of this vignette incorrectly stated that the areas of the prediction cells were 444km&lt;span class="math inline"&gt;\(^2\)&lt;/span&gt;. This has been corrected. Thanks to Phil Bouchet for pointing this out.&lt;/p&gt;'><sup>3</sup></a>.</p>
+<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">dsm.xy.pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">dsm.xy</span>, <span class="va">preddata</span>, <span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></code></pre></div>
+<p>Given predicted abundance estimates for each cell in the prediction grid, these are connected to the prediction grid object for mapping:</p>
+<div class="sourceCode" id="cb58"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">prediction_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_make_grid.html" class="external-link">st_make_grid</a></span><span class="op">(</span><span class="va">area.sf.proj</span>, cellsize <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">9000</span>,<span class="fl">9000</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">prediction_grid_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/sf.html" class="external-link">st_sf</a></span><span class="op">(</span>geometry <span class="op">=</span> <span class="va">prediction_grid</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">Prediction_xy</span> <span class="op">&lt;-</span> <span class="va">dsm.xy.pred</span></span>
+<span></span>
+<span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">prediction_grid_sf</span>, <span class="va">preddata_sf</span>, join <span class="op">=</span> <span class="va">st_nearest_feature</span><span class="op">)</span></span>
+<span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
+<span><span class="co">## all geometries</span></span></code></pre>
+<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">Prediction_xy</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title<span class="op">=</span><span class="st">"Spotted dolphins, Gulf of Mexico, abundance estimates"</span>,</span>
+<span>       subtitle <span class="op">=</span> <span class="st">"Bivariate smooth of location, quasipoisson"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_fill_viridis_c</a></span><span class="op">(</span>option <span class="op">=</span> <span class="st">"viridis"</span>, direction <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span><span class="va">pred</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/dsm.xy-preds-1.png"><!-- --></p>
+<p>We can calculate abundance over the survey area by simply summing these predictions:</p>
+<div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">dsm.xy.pred</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] 28430.76</span></span></code></pre>
+<p>We can compare this with a plot of the predictions from this <code>dsm.xy.depth</code>:</p>
+<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">dsm.xy.depth.pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, <span class="va">preddata</span>, <span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">Prediction_xy_depth</span> <span class="op">&lt;-</span> <span class="va">dsm.xy.depth.pred</span></span>
+<span></span>
+<span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">prediction_grid_sf</span>, <span class="va">preddata_sf</span>, join <span class="op">=</span> <span class="va">st_nearest_feature</span><span class="op">)</span></span>
+<span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
+<span><span class="co">## all geometries</span></span></code></pre>
+<div class="sourceCode" id="cb65"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">Prediction_xy_depth</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title<span class="op">=</span><span class="st">"Spotted dolphins, Gulf of Mexico, abundance estimates"</span>,</span>
+<span>       subtitle <span class="op">=</span> <span class="st">"Bivariate smooth of location, smooth of depth, quasipoisson"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_fill_viridis_c</a></span><span class="op">(</span>option <span class="op">=</span> <span class="st">"viridis"</span>, direction <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span><span class="va">pred</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/dsm.xy.depth-preds-1.png"><!-- --></p>
+<p>We can see the inclusion of depth into the model has had a noticeable effect on the distribution (note the difference in legend scale between the two plots). We can again also look at the total abundance:</p>
+<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">dsm.xy.depth.pred</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] 27054.6</span></span></code></pre>
+<p>Here we see that there is not much of a change in the abundance, so in terms of abundance alone there isn’t much between the two models. Next we’ll go on to look at variance next where we can see bigger differences between the models.</p>
+</div>
+<div class="section level2">
+<h2 id="variance-estimation">Variance estimation<a class="anchor" aria-label="anchor" href="#variance-estimation"></a>
+</h2>
+<p>Obviously point estimates of abundance are important, but we should also calculate uncertainty around these abundance estimates. Fortunately <code>dsm</code> provides functions to perform these calculations and display the resulting uncertainty estimates.</p>
+<p>There are two approaches to estimating the uncertainty of an abundance estimate in <code>dsm</code>.</p>
+<ul>
+<li>
+<code>dsm_var_gam</code> which assumes that the spatial model and detection function parts of the model are independent of each other. In this case the squared coefficients of variation for each model component are added.</li>
+<li>
+<code>dsm_var_prop</code> which takes into account the fact that detection probability may be correlated with the spatial part of the model. It uses methods described in <span class="citation">(Bravington, Miller, &amp; Hedley, 2021)</span>.</li>
+</ul>
+<p><code>dsm_var_prop</code> can only be applied when there is a covariate in the detection function that varies at the level of the segments, and is recorded at each segment (for example Beaufort). We don’t have that situation here, so we opt for <code>dsm_var_gam</code>.</p>
+<p>Both methods estimate the variance of the abundance for each element in the list provided in <code>pred.data</code>. In our case we wish to obtain an abundance for each of the prediction cells, so we use <code>split</code> to chop our data set into list elements to give to <code>dsm_var_gam</code> (or <code>dsm_var_prop</code>).</p>
+<div class="sourceCode" id="cb68"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">preddata.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/split.html" class="external-link">split</a></span><span class="op">(</span><span class="va">preddata</span>, <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">preddata</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">dsm.xy.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm_var_gam.html">dsm_var_gam</a></span><span class="op">(</span><span class="va">dsm.xy</span>, pred.data<span class="op">=</span><span class="va">preddata.var</span>,</span>
+<span>                          off.set<span class="op">=</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></code></pre></div>
+<p>Calling <code>summary</code> will give some information about uncertainty estimation:</p>
+<div class="sourceCode" id="cb69"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.var</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Summary of uncertainty in a density surface model calculated</span></span>
+<span><span class="co">##  analytically for GAM, with delta method</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Approximate asymptotic confidence interval:</span></span>
+<span><span class="co">##     2.5%     Mean    97.5% </span></span>
+<span><span class="co">## 15252.81 28430.76 52994.04 </span></span>
+<span><span class="co">## (Using log-Normal approximation)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Point estimate                 : 28430.76 </span></span>
+<span><span class="co">## CV of detection function       : 0.2816462 </span></span>
+<span><span class="co">## CV from GAM                    : 0.164 </span></span>
+<span><span class="co">## Total standard error           : 9265.663 </span></span>
+<span><span class="co">## Total coefficient of variation : 0.3259</span></span></code></pre>
+<p>We can also make a plot of the CVs (with transect lines and observations overlaid) using the following code.</p>
+<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">CV</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">dsm.xy.var</span><span class="op">$</span><span class="va">pred.var</span><span class="op">)</span><span class="op">/</span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">Prediction_xy</span></span>
+<span></span>
+<span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">prediction_grid_sf</span>, <span class="va">preddata_sf</span>, join <span class="op">=</span> <span class="va">st_nearest_feature</span><span class="op">)</span></span>
+<span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
+<span><span class="co">## all geometries</span></span></code></pre>
+<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">CV</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">CV</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title<span class="op">=</span><span class="st">"Spotted dolphins, Gulf of Mexico, uncertainty (CV)"</span>,</span>
+<span>       subtitle <span class="op">=</span> <span class="st">"Bivariate smooth of location, quasipoisson"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_fill_viridis_c</a></span><span class="op">(</span>option <span class="op">=</span> <span class="st">"viridis"</span>, direction <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span><span class="va">CV</span></span></code></pre></div>
+<p><img src="mexico-analysis_files/figure-html/dsm.xyvarplot-1.png"><!-- --></p>
+<p>Note the increase in CV away from the transect lines.</p>
+<p>We can revisit the model that included both depth and location smooths and observe that the coefficient of variation for that model is larger than that of the model with only the location smooth.</p>
+<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">dsm.xy.depth.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm_var_gam.html">dsm_var_gam</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, pred.data<span class="op">=</span><span class="va">preddata.var</span>,</span>
+<span>                                off.set<span class="op">=</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.depth.var</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Summary of uncertainty in a density surface model calculated</span></span>
+<span><span class="co">##  analytically for GAM, with delta method</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Approximate asymptotic confidence interval:</span></span>
+<span><span class="co">##     2.5%     Mean    97.5% </span></span>
+<span><span class="co">## 14377.57 27054.60 50909.23 </span></span>
+<span><span class="co">## (Using log-Normal approximation)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Point estimate                 : 27054.6 </span></span>
+<span><span class="co">## CV of detection function       : 0.2816462 </span></span>
+<span><span class="co">## CV from GAM                    : 0.1741 </span></span>
+<span><span class="co">## Total standard error           : 8958.444 </span></span>
+<span><span class="co">## Total coefficient of variation : 0.3311</span></span></code></pre>
+</div>
+<div class="section level2">
+<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
+</h2>
+<p>This document has outlined an analysis of spatially-explicit distance sampling data using the <code>dsm</code> package. Note that there are many possible models that can be fitted using <code>dsm</code> and that the aim here was to show just a few of the options. Results from the models can be rather different, so care must be taken in performing model selection, discrimination and criticism.</p>
+</div>
+<div class="section level2">
+<h2 id="software">Software<a class="anchor" aria-label="anchor" href="#software"></a>
+</h2>
+<ul>
+<li>
+<code>Distance</code> is available at <a href="http://github.com/DistanceDevelopment/Distance" class="external-link">http://github.com/DistanceDevelopment/Distance</a> as well as on CRAN.</li>
+<li>
+<code>dsm</code> is available at <a href="http://github.com/DistanceDevelopment/dsm" class="external-link">http://github.com/DistanceDevelopment/dsm</a>, as well as on CRAN.</li>
+</ul>
+</div>
+<div class="section level2">
+<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
+</h2>
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0" line-spacing="2">
+<div id="ref-bivand_applied_2008" class="csl-entry">
+Bivand, R. S., Pebesma, E. J., &amp; Gómez-Rubio, V. (2008). <em>Applied spatial data analysis with <span>R</span></em>. Retrieved from <a href="https://books.google.co.uk/books?id=4gg8yx_lcj0C" class="external-link">https://books.google.co.uk/books?id=4gg8yx_lcj0C</a>
+</div>
+<div id="ref-bravington_variance_2021" class="csl-entry">
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+</div>
+</div>
+</div>
+
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diff --git a/docs/articles/lines_gomex/mexico-analysis_files/figure-html/dsm.xy-preds-1.png b/docs/articles/lines_gomex/mexico-analysis_files/figure-html/dsm.xy-preds-1.png
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diff --git a/docs/authors.html b/docs/authors.html
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • dsm</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item">
+  <a class="nav-link" href="reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
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+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-citation-authors">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Authors and Citation</h1>
+    </div>
+
+    <div class="section level2 citation">
+      <h2>Authors</h2>
+      
+      <ul class="list-unstyled"><li>
+          <p><strong>David L. Miller</strong>. Author. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Eric Rexstad</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Louise Burt</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Mark V. Bravington</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Sharon Hedley</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Megan Ferguson</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Natalie Kelly</strong>. Contributor. 
+          </p>
+        </li>
+      </ul></div>
+
+    <div class="section level2 citation">
+      <h2 id="citation">Citation</h2>
+      <p><small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
+
+      <p>Miller DL (2024).
+<em>dsm: Density Surface Modelling of Distance Sampling Data</em>.
+R package version 2.3.3.9001, <a href="https://github.com/DistanceDevelopment/dsm" class="external-link">https://github.com/DistanceDevelopment/dsm</a>. 
+</p>
+      <pre>@Manual{,
+  title = {dsm: Density Surface Modelling of Distance Sampling Data},
+  author = {David L. Miller},
+  year = {2024},
+  note = {R package version 2.3.3.9001},
+  url = {https://github.com/DistanceDevelopment/dsm},
+}</pre>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
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+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
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+
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js b/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js
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+  * Bootstrap v5.3.1 (https://getbootstrap.com/)
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W{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=Y.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}N.on(document,"click.bs.button.data-api",X,(t=>{t.preventDefault();const e=t.target.closest(X);Y.getOrCreateInstance(e).toggle()})),m(Y);const U=".bs.swipe",G=`touchstart${U}`,J=`touchmove${U}`,Z=`touchend${U}`,tt=`pointerdown${U}`,et=`pointerup${U}`,it={endCallback:null,leftCallback:null,rightCallback:null},nt={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class st extends H{constructor(t,e){super(),this._element=t,t&&st.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return it}static get DefaultType(){return nt}static get 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t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&N.on(this._element,ft,(t=>this._keydown(t))),"hover"===this._config.pause&&(N.on(this._element,pt,(()=>this.pause())),N.on(this._element,mt,(()=>this._maybeEnableCycle()))),this._config.touch&&st.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of z.find(".carousel-item img",this._element))N.on(t,gt,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ct)),rightCallback:()=>this._slide(this._directionToOrder(ht)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new st(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=Tt[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const 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s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof 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vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const 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diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map b/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map
new file mode 100644
index 0000000..3863da8
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object[0] : object\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object))\n  }\n\n  return null\n}\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])')\n\n  if (!closedDetails) {\n    return elementIsVisible\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary')\n    if (summary && summary.parentNode !== closedDetails) {\n      return false\n    }\n\n    if (summary === null) {\n      return false\n    }\n  }\n\n  return elementIsVisible\n}\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode()\n    return root instanceof ShadowRoot ? root : null\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null\n  }\n\n  return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery\n  }\n\n  return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback()\n        }\n      })\n    }\n\n    DOMContentLoadedCallbacks.push(callback)\n  } else {\n    callback()\n  }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery()\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME\n      const JQUERY_NO_CONFLICT = $.fn[name]\n      $.fn[name] = plugin.jQueryInterface\n      $.fn[name].Constructor = plugin\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT\n        return plugin.jQueryInterface\n      }\n    }\n  })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback)\n    return\n  }\n\n  const durationPadding = 5\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n  let called = false\n\n  const handler = ({ target }) => {\n    if (target !== transitionElement) {\n      return\n    }\n\n    called = true\n    transitionElement.removeEventListener(TRANSITION_END, handler)\n    execute(callback)\n  }\n\n  transitionElement.addEventListener(TRANSITION_END, handler)\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement)\n    }\n  }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length\n  let index = list.indexOf(activeElement)\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n  }\n\n  index += shouldGetNext ? 1 : -1\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n  defineJQueryPlugin,\n  execute,\n  executeAfterTransition,\n  findShadowRoot,\n  getElement,\n  getjQuery,\n  getNextActiveElement,\n  getTransitionDurationFromElement,\n  getUID,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop,\n  onDOMContentLoaded,\n  parseSelector,\n  reflow,\n  triggerTransitionEnd,\n  toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n  'click',\n  'dblclick',\n  'mouseup',\n  'mousedown',\n  'contextmenu',\n  'mousewheel',\n  'DOMMouseScroll',\n  'mouseover',\n  'mouseout',\n  'mousemove',\n  'selectstart',\n  'selectend',\n  'keydown',\n  'keypress',\n  'keyup',\n  'orientationchange',\n  'touchstart',\n  'touchmove',\n  'touchend',\n  'touchcancel',\n  'pointerdown',\n  'pointermove',\n  'pointerup',\n  'pointerleave',\n  'pointercancel',\n  'gesturestart',\n  'gesturechange',\n  'gestureend',\n  'focus',\n  'blur',\n  'change',\n  'reset',\n  'select',\n  'submit',\n  'focusin',\n  'focusout',\n  'load',\n  'unload',\n  'beforeunload',\n  'resize',\n  'move',\n  'DOMContentLoaded',\n  'readystatechange',\n  'error',\n  'abort',\n  'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element)\n\n  element.uidEvent = uid\n  eventRegistry[uid] = eventRegistry[uid] || {}\n\n  return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, { delegateTarget: element })\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn)\n    }\n\n    return fn.apply(element, [event])\n  }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector)\n\n    for (let { target } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue\n        }\n\n        hydrateObj(event, { delegateTarget: target })\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn)\n        }\n\n        return fn.apply(target, [event])\n      }\n    }\n  }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events)\n    .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n  let typeEvent = getTypeEvent(originalTypeEvent)\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent\n  }\n\n  return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n          return fn.call(this, event)\n        }\n      }\n    }\n\n    callable = wrapFunction(callable)\n  }\n\n  const events = getElementEvents(element)\n  const handlers = events[typeEvent] || (events[typeEvent] = {})\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n    return\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n  const fn = isDelegated ?\n    bootstrapDelegationHandler(element, handler, callable) :\n    bootstrapHandler(element, callable)\n\n  fn.delegationSelector = isDelegated ? handler : null\n  fn.callable = callable\n  fn.oneOff = oneOff\n  fn.uidEvent = uid\n  handlers[uid] = fn\n\n  element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n  if (!fn) {\n    return\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n  delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {}\n\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '')\n  return customEvents[event] || event\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false)\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true)\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n    const inNamespace = typeEvent !== originalTypeEvent\n    const events = getElementEvents(element)\n    const storeElementEvent = events[typeEvent] || {}\n    const isNamespace = originalTypeEvent.startsWith('.')\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n      return\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n      }\n    }\n\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null\n    }\n\n    const $ = getjQuery()\n    const typeEvent = getTypeEvent(event)\n    const inNamespace = event !== typeEvent\n\n    let jQueryEvent = null\n    let bubbles = true\n    let nativeDispatch = true\n    let defaultPrevented = false\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args)\n\n      $(element).trigger(jQueryEvent)\n      bubbles = !jQueryEvent.isPropagationStopped()\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n      defaultPrevented = jQueryEvent.isDefaultPrevented()\n    }\n\n    const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n    if (defaultPrevented) {\n      evt.preventDefault()\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt)\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault()\n    }\n\n    return evt\n  }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value\n    } catch {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value\n        }\n      })\n    }\n  }\n\n  return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true\n  }\n\n  if (value === 'false') {\n    return false\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value)\n  }\n\n  if (value === '' || value === 'null') {\n    return null\n  }\n\n  if (typeof value !== 'string') {\n    return value\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value))\n  } catch {\n    return value\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {}\n    }\n\n    const attributes = {}\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '')\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n      attributes[pureKey] = normalizeData(element.dataset[key])\n    }\n\n    return attributes\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n  }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {}\n  }\n\n  static get DefaultType() {\n    return {}\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!')\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    return config\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    }\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property]\n      const valueType = isElement(value) ? 'element' : toType(value)\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(\n          `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n        )\n      }\n    }\n  }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super()\n\n    element = getElement(element)\n    if (!element) {\n      return\n    }\n\n    this._element = element\n    this._config = this._getConfig(config)\n\n    Data.set(this._element, this.constructor.DATA_KEY, this)\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY)\n    EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated)\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY)\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n  }\n\n  static get VERSION() {\n    return VERSION\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`\n  }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target')\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href')\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n      return null\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n  }\n\n  return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector)\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector))\n  },\n\n  parents(element, selector) {\n    const parents = []\n    let ancestor = element.parentNode.closest(selector)\n\n    while (ancestor) {\n      parents.push(ancestor)\n      ancestor = ancestor.parentNode.closest(selector)\n    }\n\n    return parents\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous]\n      }\n\n      previous = previous.previousElementSibling\n    }\n\n    return []\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next]\n      }\n\n      next = next.nextElementSibling\n    }\n\n    return []\n  },\n\n  focusableChildren(element) {\n    const focusables = [\n      'a',\n      'button',\n      'input',\n      'textarea',\n      'select',\n      'details',\n      '[tabindex]',\n      '[contenteditable=\"true\"]'\n    ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n  },\n\n  getSelectorFromElement(element) {\n    const selector = getSelector(element)\n\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null\n    }\n\n    return null\n  },\n\n  getElementFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.findOne(selector) : null\n  },\n\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.find(selector) : []\n  }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`\n  const name = component.NAME\n\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault()\n    }\n\n    if (isDisabled(this)) {\n      return\n    }\n\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n    const instance = component.getOrCreateInstance(target)\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]()\n  })\n}\n\nexport {\n  enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n    if (closeEvent.defaultPrevented) {\n      return\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove()\n    EventHandler.trigger(this._element, EVENT_CLOSED)\n    this.dispose()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this)\n\n      if (config === 'toggle') {\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n  event.preventDefault()\n\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n  const data = Button.getOrCreateInstance(button)\n\n  data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n}\n\nconst DefaultType = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super()\n    this._element = element\n\n    if (!element || !Swipe.isSupported()) {\n      return\n    }\n\n    this._config = this._getConfig(config)\n    this._deltaX = 0\n    this._supportPointerEvents = Boolean(window.PointerEvent)\n    this._initEvents()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY)\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX\n\n      return\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX\n    }\n\n    this._handleSwipe()\n    execute(this._config.endCallback)\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ?\n      0 :\n      event.touches[0].clientX - this._deltaX\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX)\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return\n    }\n\n    const direction = absDeltaX / this._deltaX\n\n    this._deltaX = 0\n\n    if (!direction) {\n      return\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n  }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getNextActiveElement,\n  isRTL,\n  isVisible,\n  reflow,\n  triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n}\n\nconst DefaultType = {\n  interval: '(number|boolean)', // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._interval = null\n    this._activeElement = null\n    this._isSliding = false\n    this.touchTimeout = null\n    this._swipeHelper = null\n\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n    this._addEventListeners()\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT)\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next()\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV)\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element)\n    }\n\n    this._clearInterval()\n  }\n\n  cycle() {\n    this._clearInterval()\n    this._updateInterval()\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n      return\n    }\n\n    this.cycle()\n  }\n\n  to(index) {\n    const items = this._getItems()\n    if (index > items.length - 1 || index < 0) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n      return\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive())\n    if (activeIndex === index) {\n      return\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n    this._slide(order, items[index])\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose()\n    }\n\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval\n    return config\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n      EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners()\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause()\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout)\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n    }\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    }\n\n    this._swipeHelper = new Swipe(this._element, swipeConfig)\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key]\n    if (direction) {\n      event.preventDefault()\n      this._slide(this._directionToOrder(direction))\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element)\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n    activeIndicator.removeAttribute('aria-current')\n\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n      newActiveIndicator.setAttribute('aria-current', 'true')\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive()\n\n    if (!element) {\n      return\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n    this._config.interval = elementInterval || this._config.defaultInterval\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return\n    }\n\n    const activeElement = this._getActive()\n    const isNext = order === ORDER_NEXT\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n    if (nextElement === activeElement) {\n      return\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement)\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      })\n    }\n\n    const slideEvent = triggerEvent(EVENT_SLIDE)\n\n    if (slideEvent.defaultPrevented) {\n      return\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return\n    }\n\n    const isCycling = Boolean(this._interval)\n    this.pause()\n\n    this._isSliding = true\n\n    this._setActiveIndicatorElement(nextElementIndex)\n    this._activeElement = nextElement\n\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n    nextElement.classList.add(orderClassName)\n\n    reflow(nextElement)\n\n    activeElement.classList.add(directionalClassName)\n    nextElement.classList.add(directionalClassName)\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName)\n      nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n      activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n      this._isSliding = false\n\n      triggerEvent(EVENT_SLID)\n    }\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n    if (isCycling) {\n      this.cycle()\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE)\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element)\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval)\n      this._interval = null\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config)\n\n      if (typeof config === 'number') {\n        data.to(config)\n        return\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return\n  }\n\n  event.preventDefault()\n\n  const carousel = Carousel.getOrCreateInstance(target)\n  const slideIndex = this.getAttribute('data-bs-slide-to')\n\n  if (slideIndex) {\n    carousel.to(slideIndex)\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next()\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  carousel.prev()\n  carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getElement,\n  reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n  parent: null,\n  toggle: true\n}\n\nconst DefaultType = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isTransitioning = false\n    this._triggerArray = []\n\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem)\n      const filterElement = SelectorEngine.find(selector)\n        .filter(foundElement => foundElement === this._element)\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem)\n      }\n    }\n\n    this._initializeChildren()\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n    }\n\n    if (this._config.toggle) {\n      this.toggle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide()\n    } else {\n      this.show()\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return\n    }\n\n    let activeChildren = []\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n        .filter(element => element !== this._element)\n        .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide()\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE)\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n    this._element.style[dimension] = 0\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true)\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n      this._element.style[dimension] = ''\n\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n    }\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n    const scrollSize = `scroll${capitalizedDimension}`\n\n    this._queueCallback(complete, this._element, true)\n    this._element.style[dimension] = `${this._element[scrollSize]}px`\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger)\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false)\n      }\n    }\n\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.style[dimension] = ''\n\n    this._queueCallback(complete, this._element, true)\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle) // Coerce string values\n    config.parent = getElement(config.parent)\n    return config\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element)\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected))\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n      element.setAttribute('aria-expanded', isOpen)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {}\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n    event.preventDefault()\n  }\n\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  execute,\n  getElement,\n  getNextActiveElement,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n}\n\nconst DefaultType = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._popper = null\n    this._parent = this._element.parentNode // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n    this._inNavbar = this._detectNavbar()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show()\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._createPopper()\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    this._element.focus()\n    this._element.setAttribute('aria-expanded', true)\n\n    this._menu.classList.add(CLASS_NAME_SHOW)\n    this._element.classList.add(CLASS_NAME_SHOW)\n    EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    this._completeHide(relatedTarget)\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    super.dispose()\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar()\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW)\n    this._element.classList.remove(CLASS_NAME_SHOW)\n    this._element.setAttribute('aria-expanded', 'false')\n    Manipulator.removeDataAttribute(this._menu, 'popper')\n    EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config)\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) &&\n      typeof config.reference.getBoundingClientRect !== 'function'\n    ) {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n    }\n\n    return config\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n    }\n\n    let referenceElement = this._element\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference)\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference\n    }\n\n    const popperConfig = this._getPopperConfig()\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      },\n      {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _selectMenuItem({ key, target }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n    if (!items.length) {\n      return\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n      return\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle)\n      if (!context || context._config.autoClose === false) {\n        continue\n      }\n\n      const composedPath = event.composedPath()\n      const isMenuTarget = composedPath.includes(context._menu)\n      if (\n        composedPath.includes(context._element) ||\n        (context._config.autoClose === 'inside' && !isMenuTarget) ||\n        (context._config.autoClose === 'outside' && isMenuTarget)\n      ) {\n        continue\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue\n      }\n\n      const relatedTarget = { relatedTarget: context._element }\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event\n      }\n\n      context._completeHide(relatedTarget)\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName)\n    const isEscapeEvent = event.key === ESCAPE_KEY\n    const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return\n    }\n\n    event.preventDefault()\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n      this :\n      (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n    const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation()\n      instance.show()\n      instance._selectMenuItem(event)\n      return\n    }\n\n    if (instance._isShown()) { // else is escape and we check if it is shown\n      event.stopPropagation()\n      instance.hide()\n      getToggleButton.focus()\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  event.preventDefault()\n  Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isAppended = false\n    this._element = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._append()\n\n    const element = this._getElement()\n    if (this._config.isAnimated) {\n      reflow(element)\n    }\n\n    element.classList.add(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      execute(callback)\n    })\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      this.dispose()\n      execute(callback)\n    })\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n    this._element.remove()\n    this._isAppended = false\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div')\n      backdrop.className = this._config.className\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE)\n      }\n\n      this._element = backdrop\n    }\n\n    return this._element\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement)\n    return config\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return\n    }\n\n    const element = this._getElement()\n    this._config.rootElement.append(element)\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback)\n    })\n\n    this._isAppended = true\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n  }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isActive = false\n    this._lastTabNavDirection = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus()\n    }\n\n    EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n    this._isActive = true\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return\n    }\n\n    this._isActive = false\n    EventHandler.off(document, EVENT_KEY)\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const { trapElement } = this._config\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement)\n\n    if (elements.length === 0) {\n      trapElement.focus()\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus()\n    } else {\n      elements[0].focus()\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n  }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth\n    return Math.abs(window.innerWidth - documentWidth)\n  }\n\n  hide() {\n    const width = this.getWidth()\n    this._disableOverFlow()\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow')\n    this._resetElementAttributes(this._element, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow')\n    this._element.style.overflow = 'hidden'\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth()\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return\n      }\n\n      this._saveInitialAttribute(element, styleProperty)\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty)\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue)\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty)\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty)\n        return\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty)\n      element.style.setProperty(styleProperty, value)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector)\n      return\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel)\n    }\n  }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._isShown = false\n    this._isTransitioning = false\n    this._scrollBar = new ScrollBarHelper()\n\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n      relatedTarget\n    })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._isTransitioning = true\n\n    this._scrollBar.hide()\n\n    document.body.classList.add(CLASS_NAME_OPEN)\n\n    this._adjustDialog()\n\n    this._backdrop.show(() => this._showElement(relatedTarget))\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = false\n    this._isTransitioning = true\n    this._focustrap.deactivate()\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n  }\n\n  dispose() {\n    EventHandler.off(window, EVENT_KEY)\n    EventHandler.off(this._dialog, EVENT_KEY)\n\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n\n    super.dispose()\n  }\n\n  handleUpdate() {\n    this._adjustDialog()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element)\n    }\n\n    this._element.style.display = 'block'\n    this._element.removeAttribute('aria-hidden')\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.scrollTop = 0\n\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n    if (modalBody) {\n      modalBody.scrollTop = 0\n    }\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_SHOW)\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate()\n      }\n\n      this._isTransitioning = false\n      EventHandler.trigger(this._element, EVENT_SHOWN, {\n        relatedTarget\n      })\n    }\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      this._triggerBackdropTransition()\n    })\n\n    EventHandler.on(window, EVENT_RESIZE, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog()\n      }\n    })\n\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition()\n          return\n        }\n\n        if (this._config.backdrop) {\n          this.hide()\n        }\n      })\n    })\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none'\n    this._element.setAttribute('aria-hidden', true)\n    this._element.removeAttribute('aria-modal')\n    this._element.removeAttribute('role')\n    this._isTransitioning = false\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN)\n      this._resetAdjustments()\n      this._scrollBar.reset()\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    })\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE)\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const initialOverflowY = this._element.style.overflowY\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden'\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC)\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC)\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY\n      }, this._dialog)\n    }, this._dialog)\n\n    this._element.focus()\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const scrollbarWidth = this._scrollBar.getWidth()\n    const isBodyOverflowing = scrollbarWidth > 0\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = ''\n    this._element.style.paddingRight = ''\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](relatedTarget)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  EventHandler.one(target, EVENT_SHOW, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN, () => {\n      if (isVisible(this)) {\n        this.focus()\n      }\n    })\n  })\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Modal.getOrCreateInstance(target)\n\n  data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n  defineJQueryPlugin,\n  isDisabled,\n  isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isShown = false\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._backdrop.show()\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide()\n    }\n\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.classList.add(CLASS_NAME_SHOWING)\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate()\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW)\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n    }\n\n    this._queueCallback(completeCallBack, this._element, true)\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._focustrap.deactivate()\n    this._element.blur()\n    this._isShown = false\n    this._element.classList.add(CLASS_NAME_HIDING)\n    this._backdrop.hide()\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n      this._element.removeAttribute('aria-modal')\n      this._element.removeAttribute('role')\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset()\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._queueCallback(completeCallback, this._element, true)\n  }\n\n  dispose() {\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n    super.dispose()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n        return\n      }\n\n      this.hide()\n    }\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop)\n\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    })\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus()\n    }\n  })\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target)\n  data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show()\n  }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide()\n    }\n  }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n  'background',\n  'cite',\n  'href',\n  'itemtype',\n  'longdesc',\n  'poster',\n  'src',\n  'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase()\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n    }\n\n    return true\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n    .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml)\n  }\n\n  const domParser = new window.DOMParser()\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase()\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove()\n      continue\n    }\n\n    const attributeList = [].concat(...element.attributes)\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName)\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  content: {}, // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n}\n\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content)\n      .map(config => this._resolvePossibleFunction(config))\n      .filter(Boolean)\n  }\n\n  hasContent() {\n    return this.getContent().length > 0\n  }\n\n  changeContent(content) {\n    this._checkContent(content)\n    this._config.content = { ...this._config.content, ...content }\n    return this\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div')\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector)\n    }\n\n    const template = templateWrapper.children[0]\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '))\n    }\n\n    return template\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config)\n    this._checkContent(config.content)\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template)\n\n    if (!templateElement) {\n      return\n    }\n\n    content = this._resolvePossibleFunction(content)\n\n    if (!content) {\n      templateElement.remove()\n      return\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement)\n      return\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content)\n      return\n    }\n\n    templateElement.textContent = content\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this])\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = ''\n      templateElement.append(element)\n      return\n    }\n\n    templateElement.textContent = element.textContent\n  }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' +\n            '<div class=\"tooltip-arrow\"></div>' +\n            '<div class=\"tooltip-inner\"></div>' +\n            '</div>',\n  title: '',\n  trigger: 'hover focus'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n    }\n\n    super(element, config)\n\n    // Private\n    this._isEnabled = true\n    this._timeout = 0\n    this._isHovered = null\n    this._activeTrigger = {}\n    this._popper = null\n    this._templateFactory = null\n    this._newContent = null\n\n    // Protected\n    this.tip = null\n\n    this._setListeners()\n\n    if (!this._config.selector) {\n      this._fixTitle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true\n  }\n\n  disable() {\n    this._isEnabled = false\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click\n    if (this._isShown()) {\n      this._leave()\n      return\n    }\n\n    this._enter()\n  }\n\n  dispose() {\n    clearTimeout(this._timeout)\n\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n    }\n\n    this._disposePopper()\n    super.dispose()\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements')\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n    const shadowRoot = findShadowRoot(this._element)\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper()\n\n    const tip = this._getTipElement()\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n    const { container } = this._config\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip)\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n    }\n\n    this._popper = this._createPopper(tip)\n\n    tip.classList.add(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n      if (this._isHovered === false) {\n        this._leave()\n      }\n\n      this._isHovered = false\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const tip = this._getTipElement()\n    tip.classList.remove(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false\n    this._activeTrigger[TRIGGER_FOCUS] = false\n    this._activeTrigger[TRIGGER_HOVER] = false\n    this._isHovered = null // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper()\n      }\n\n      this._element.removeAttribute('aria-describedby')\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle())\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n    }\n\n    return this.tip\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml()\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n    const tipId = getUID(this.constructor.NAME).toString()\n\n    tip.setAttribute('id', tipId)\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE)\n    }\n\n    return tip\n  }\n\n  setContent(content) {\n    this._newContent = content\n    if (this._isShown()) {\n      this._disposePopper()\n      this.show()\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content)\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      })\n    }\n\n    return this._templateFactory\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    }\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n  }\n\n  _isAnimated() {\n    return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element])\n    const attachment = AttachmentMap[placement.toUpperCase()]\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element])\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [\n        {\n          name: 'flip',\n          options: {\n            fallbackPlacements: this._config.fallbackPlacements\n          }\n        },\n        {\n          name: 'offset',\n          options: {\n            offset: this._getOffset()\n          }\n        },\n        {\n          name: 'preventOverflow',\n          options: {\n            boundary: this._config.boundary\n          }\n        },\n        {\n          name: 'arrow',\n          options: {\n            element: `.${this.constructor.NAME}-arrow`\n          }\n        },\n        {\n          name: 'preSetPlacement',\n          enabled: true,\n          phase: 'beforeMain',\n          fn: data => {\n            // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n            // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n            this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n          }\n        }\n      ]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ')\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context.toggle()\n        })\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSEENTER) :\n          this.constructor.eventName(EVENT_FOCUSIN)\n        const eventOut = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSELEAVE) :\n          this.constructor.eventName(EVENT_FOCUSOUT)\n\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n          context._enter()\n        })\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n            context._element.contains(event.relatedTarget)\n\n          context._leave()\n        })\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide()\n      }\n    }\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title')\n\n    if (!title) {\n      return\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title)\n    }\n\n    this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title')\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true\n      return\n    }\n\n    this._isHovered = true\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show()\n      }\n    }, this._config.delay.show)\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return\n    }\n\n    this._isHovered = false\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide()\n      }\n    }, this._config.delay.hide)\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout)\n    this._timeout = setTimeout(handler, timeout)\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true)\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute]\n      }\n    }\n\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    }\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container)\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      }\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString()\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString()\n    }\n\n    return config\n  }\n\n  _getDelegateConfig() {\n    const config = {}\n\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value\n      }\n    }\n\n    config.selector = false\n    config.trigger = 'manual'\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy()\n      this._popper = null\n    }\n\n    if (this.tip) {\n      this.tip.remove()\n      this.tip = null\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' +\n    '<div class=\"popover-arrow\"></div>' +\n    '<h3 class=\"popover-header\"></h3>' +\n    '<div class=\"popover-body\"></div>' +\n    '</div>',\n  trigger: 'click'\n}\n\nconst DefaultType = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent()\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    }\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content)\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n  offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n  offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n    this._activeTarget = null\n    this._observer = null\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    }\n    this.refresh() // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables()\n    this._maybeEnableSmoothScroll()\n\n    if (this._observer) {\n      this._observer.disconnect()\n    } else {\n      this._observer = this._getNewObserver()\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section)\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect()\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n    }\n\n    return config\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK)\n\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash)\n      if (observableSection) {\n        event.preventDefault()\n        const root = this._rootElement || window\n        const height = observableSection.offsetTop - this._element.offsetTop\n        if (root.scrollTo) {\n          root.scrollTo({ top: height, behavior: 'smooth' })\n          return\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height\n      }\n    })\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    }\n\n    return new IntersectionObserver(entries => this._observerCallback(entries), options)\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n      this._process(targetElement(entry))\n    }\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n    this._previousScrollData.parentScrollTop = parentScrollTop\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null\n        this._clearActiveClass(targetElement(entry))\n\n        continue\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry)\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return\n        }\n\n        continue\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry)\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue\n      }\n\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor)\n        this._observableSections.set(anchor.hash, observableSection)\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return\n    }\n\n    this._clearActiveClass(this._config.target)\n    this._activeTarget = target\n    target.classList.add(CLASS_NAME_ACTIVE)\n    this._activateParents(target)\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n        .classList.add(CLASS_NAME_ACTIVE)\n      return\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE)\n      }\n    }\n  }\n\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE)\n\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE}`, parent)\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy)\n\nexport default ScrollSpy\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getNextActiveElement, isDisabled } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tab'\nconst DATA_KEY = 'bs.tab'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}`\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst HOME_KEY = 'Home'\nconst END_KEY = 'End'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_DROPDOWN = 'dropdown'\n\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu'\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = ':not(.dropdown-toggle)'\n\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]'\nconst SELECTOR_OUTER = '.nav-item, .list-group-item'\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]' // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`\n\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element)\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL)\n\n    if (!this._parent) {\n      return\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren())\n\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() { // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element\n    if (this._elemIsActive(innerElem)) {\n      return\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem()\n\n    const hideEvent = active ?\n      EventHandler.trigger(active, EVENT_HIDE, { relatedTarget: innerElem }) :\n      null\n\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW, { relatedTarget: active })\n\n    if (showEvent.defaultPrevented || (hideEvent && hideEvent.defaultPrevented)) {\n      return\n    }\n\n    this._deactivate(active, innerElem)\n    this._activate(innerElem, active)\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.add(CLASS_NAME_ACTIVE)\n\n    this._activate(SelectorEngine.getElementFromSelector(element)) // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.removeAttribute('tabindex')\n      element.setAttribute('aria-selected', true)\n      this._toggleDropDown(element, true)\n      EventHandler.trigger(element, EVENT_SHOWN, {\n        relatedTarget: relatedElem\n      })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.remove(CLASS_NAME_ACTIVE)\n    element.blur()\n\n    this._deactivate(SelectorEngine.getElementFromSelector(element)) // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.setAttribute('aria-selected', false)\n      element.setAttribute('tabindex', '-1')\n      this._toggleDropDown(element, false)\n      EventHandler.trigger(element, EVENT_HIDDEN, { relatedTarget: relatedElem })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _keydown(event) {\n    if (!([ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key))) {\n      return\n    }\n\n    event.stopPropagation()// stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault()\n\n    const children = this._getChildren().filter(element => !isDisabled(element))\n    let nextActiveElement\n\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1]\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key)\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true)\n    }\n\n    if (nextActiveElement) {\n      nextActiveElement.focus({ preventScroll: true })\n      Tab.getOrCreateInstance(nextActiveElement).show()\n    }\n  }\n\n  _getChildren() { // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent)\n  }\n\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null\n  }\n\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist')\n\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child)\n    }\n  }\n\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child)\n    const isActive = this._elemIsActive(child)\n    const outerElem = this._getOuterElement(child)\n    child.setAttribute('aria-selected', isActive)\n\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation')\n    }\n\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1')\n    }\n\n    this._setAttributeIfNotExists(child, 'role', 'tab')\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child)\n  }\n\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child)\n\n    if (!target) {\n      return\n    }\n\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel')\n\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`)\n    }\n  }\n\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element)\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return\n    }\n\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem)\n      if (element) {\n        element.classList.toggle(className, open)\n      }\n    }\n\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE)\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW)\n    outerElem.setAttribute('aria-expanded', open)\n  }\n\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value)\n    }\n  }\n\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE)\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem)\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  Tab.getOrCreateInstance(this).show()\n})\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element)\n  }\n})\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab)\n\nexport default Tab\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin, reflow } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'toast'\nconst DATA_KEY = 'bs.toast'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_HIDE = 'hide' // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\n\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n}\n\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n}\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._timeout = null\n    this._hasMouseInteraction = false\n    this._hasKeyboardInteraction = false\n    this._setListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._clearTimeout()\n\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE)\n    }\n\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n\n      this._maybeScheduleHide()\n    }\n\n    this._element.classList.remove(CLASS_NAME_HIDE) // @deprecated\n    reflow(this._element)\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING)\n\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  hide() {\n    if (!this.isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE) // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.classList.add(CLASS_NAME_SHOWING)\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  dispose() {\n    this._clearTimeout()\n\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW)\n    }\n\n    super.dispose()\n  }\n\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return\n    }\n\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return\n    }\n\n    this._timeout = setTimeout(() => {\n      this.hide()\n    }, this._config.delay)\n  }\n\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout': {\n        this._hasMouseInteraction = isInteracting\n        break\n      }\n\n      case 'focusin':\n      case 'focusout': {\n        this._hasKeyboardInteraction = isInteracting\n        break\n      }\n\n      default: {\n        break\n      }\n    }\n\n    if (isInteracting) {\n      this._clearTimeout()\n      return\n    }\n\n    const nextElement = event.relatedTarget\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return\n    }\n\n    this._maybeScheduleHide()\n  }\n\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false))\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false))\n  }\n\n  _clearTimeout() {\n    clearTimeout(this._timeout)\n    this._timeout = null\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config](this)\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast)\n\nexport default Toast\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap index.umd.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Alert from './src/alert.js'\nimport Button from './src/button.js'\nimport Carousel from './src/carousel.js'\nimport Collapse from './src/collapse.js'\nimport Dropdown from './src/dropdown.js'\nimport Modal from './src/modal.js'\nimport Offcanvas from './src/offcanvas.js'\nimport Popover from './src/popover.js'\nimport ScrollSpy from './src/scrollspy.js'\nimport Tab from './src/tab.js'\nimport Toast from './src/toast.js'\nimport Tooltip from 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\ No newline at end of file
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.min.css b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
new file mode 100644
index 0000000..8db4478
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
@@ -0,0 +1,5 @@
+:root{--bslib-bootstrap-version: 5;--bslib-preset-name: ;--bslib-preset-type: }/*!
+   * Bootstrap  v5.3.1 (https://getbootstrap.com/)
+   * Copyright 2011-2023 The Bootstrap Authors
+   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+   */:root,[data-bs-theme="light"]{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #0dcaf0;--bs-black: #000;--bs-white: #FCFAF2;--bs-gray: #656461;--bs-gray-dark: #323230;--bs-gray-100: #E3E1DA;--bs-gray-200: #CAC8C2;--bs-gray-300: #B0AFA9;--bs-gray-400: #979691;--bs-gray-500: #7E7D79;--bs-gray-600: #656461;--bs-gray-700: #4C4B49;--bs-gray-800: #323230;--bs-gray-900: #191918;--bs-default: #B0AFA9;--bs-primary: #0542A3;--bs-secondary: #656461;--bs-success: #198754;--bs-info: #0dcaf0;--bs-warning: #ffc107;--bs-danger: #dc3545;--bs-light: #E3E1DA;--bs-dark: #191918;--bs-default-rgb: 176,175,169;--bs-primary-rgb: 5,66,163;--bs-secondary-rgb: 101,100,97;--bs-success-rgb: 25,135,84;--bs-info-rgb: 13,202,240;--bs-warning-rgb: 255,193,7;--bs-danger-rgb: 220,53,69;--bs-light-rgb: 227,225,218;--bs-dark-rgb: 25,25,24;--bs-primary-text-emphasis: 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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #B0AFA9;--bs-dropdown-bg: #323230;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #B0AFA9;--bs-dropdown-link-hover-color: #FCFAF2;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(252,250,242,0.15);--bs-dropdown-link-active-color: #FCFAF2;--bs-dropdown-link-active-bg: #0542A3;--bs-dropdown-link-disabled-color: #7E7D79;--bs-dropdown-header-color: #7E7D79}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(5,66,163,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: var(--bs-border-color);--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * var(--bs-nav-tabs-border-width));border-top-left-radius:0;border-top-right-radius:0}.nav-pills{--bs-nav-pills-border-radius: var(--bs-border-radius);--bs-nav-pills-link-active-color: #FCFAF2;--bs-nav-pills-link-active-bg: #0542A3}.nav-pills .nav-link{border-radius:var(--bs-nav-pills-border-radius)}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: .125rem;--bs-nav-underline-link-active-color: var(--bs-emphasis-color);gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid transparent}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2825,25,24,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23021a41'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #82a1d1;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(5,66,163,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23698ec8'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23698ec8'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(5,66,163,0.25);--bs-pagination-active-color: #FCFAF2;--bs-pagination-active-bg: #0542A3;--bs-pagination-active-border-color: #0542A3;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #FCFAF2;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #FCFAF2;--bs-progress-bar-bg: #0542A3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(252,250,242,0.15) 25%, transparent 25%, transparent 50%, rgba(252,250,242,0.15) 50%, rgba(252,250,242,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #FCFAF2;--bs-list-group-active-bg: #0542A3;--bs-list-group-active-border-color: #0542A3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(5,66,163,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: .5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y) * .5) calc(var(--bs-modal-header-padding-x) * .5);margin:calc(-.5 * var(--bs-modal-header-padding-y)) calc(-.5 * var(--bs-modal-header-padding-x)) calc(-.5 * var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap) * .5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap) * .5)}@media (min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media (min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media (min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media (max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media (max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media (max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: .5rem;--bs-tooltip-padding-y: .25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:.875rem;--bs-tooltip-color: var(--bs-body-bg);--bs-tooltip-bg: var(--bs-emphasis-color);--bs-tooltip-border-radius: var(--bs-border-radius);--bs-tooltip-opacity: .9;--bs-tooltip-arrow-width: .8rem;--bs-tooltip-arrow-height: .4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:transparent;border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow{bottom:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * .5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="right"] .tooltip-arrow{left:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="right"] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * .5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="bottom"] .tooltip-arrow{top:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="bottom"] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="left"] .tooltip-arrow{right:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="left"] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) 0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:.875rem;--bs-popover-bg: var(--bs-body-bg);--bs-popover-border-width: var(--bs-border-width);--bs-popover-border-color: var(--bs-border-color-translucent);--bs-popover-border-radius: var(--bs-border-radius-lg);--bs-popover-inner-border-radius: calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: .5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: var(--bs-secondary-bg);--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: var(--bs-body-color);--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: .5rem;--bs-popover-arrow-border: 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#000;--bslib-color-bg: #a3aa26;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ffc107 var(--bg-gradient-start, 36%), #198754 var(--bg-gradient-end, 180%)) #a3aa26;color:#000}.bg-gradient-yellow-teal{--bslib-color-fg: #000;--bslib-color-bg: #a6c441;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ffc107 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6c441;color:#000}.bg-gradient-yellow-cyan{--bslib-color-fg: #000;--bslib-color-bg: #9ec564;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ffc107 var(--bg-gradient-start, 36%), #0dcaf0 var(--bg-gradient-end, 180%)) #9ec564;color:#000}.bg-gradient-green-blue{--bslib-color-fg: #FCFAF2;--bslib-color-bg: #147d98;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #0d6efd var(--bg-gradient-end, 180%)) #147d98;color:#FCFAF2}.bg-gradient-green-indigo{--bslib-color-fg: #FCFAF2;--bslib-color-bg: #385793;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #385793;color:#FCFAF2}.bg-gradient-green-purple{--bslib-color-fg: #FCFAF2;--bslib-color-bg: #3b6b80;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #3b6b80;color:#FCFAF2}.bg-gradient-green-pink{--bslib-color-fg: #FCFAF2;--bslib-color-bg: #656567;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #d63384 var(--bg-gradient-end, 180%)) #656567;color:#FCFAF2}.bg-gradient-green-red{--bslib-color-fg: #FCFAF2;--bslib-color-bg: #67664e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #dc3545 var(--bg-gradient-end, 180%)) #67664e;color:#FCFAF2}.bg-gradient-green-orange{--bslib-color-fg: #000;--bslib-color-bg: #74833a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #74833a;color:#000}.bg-gradient-green-yellow{--bslib-color-fg: #000;--bslib-color-bg: #759e35;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #ffc107 var(--bg-gradient-end, 180%)) #759e35;color:#000}.bg-gradient-green-teal{--bslib-color-fg: #000;--bslib-color-bg: #1ca16f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #1ca16f;color:#000}.bg-gradient-green-cyan{--bslib-color-fg: #000;--bslib-color-bg: #14a292;background:linear-gradient(var(--bg-gradient-deg, 140deg), #198754 var(--bg-gradient-start, 36%), #0dcaf0 var(--bg-gradient-end, 180%)) #14a292;color:#000}.bg-gradient-teal-blue{--bslib-color-fg: #000;--bslib-color-bg: #18a5c0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #0d6efd var(--bg-gradient-end, 180%)) #18a5c0;color:#000}.bg-gradient-teal-indigo{--bslib-color-fg: #000;--bslib-color-bg: #3c7fbb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #3c7fbb;color:#000}.bg-gradient-teal-purple{--bslib-color-fg: #000;--bslib-color-bg: #4093a8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #4093a8;color:#000}.bg-gradient-teal-pink{--bslib-color-fg: #000;--bslib-color-bg: #698d8f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #d63384 var(--bg-gradient-end, 180%)) #698d8f;color:#000}.bg-gradient-teal-red{--bslib-color-fg: #000;--bslib-color-bg: #6b8e76;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #dc3545 var(--bg-gradient-end, 180%)) #6b8e76;color:#000}.bg-gradient-teal-orange{--bslib-color-fg: #000;--bslib-color-bg: #78ab63;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #78ab63;color:#000}.bg-gradient-teal-yellow{--bslib-color-fg: #000;--bslib-color-bg: #79c65d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ffc107 var(--bg-gradient-end, 180%)) #79c65d;color:#000}.bg-gradient-teal-green{--bslib-color-fg: #000;--bslib-color-bg: #1daf7c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #198754 var(--bg-gradient-end, 180%)) #1daf7c;color:#000}.bg-gradient-teal-cyan{--bslib-color-fg: #000;--bslib-color-bg: #18c9bb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #0dcaf0 var(--bg-gradient-end, 180%)) #18c9bb;color:#000}.bg-gradient-cyan-blue{--bslib-color-fg: #000;--bslib-color-bg: #0da5f5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #0d6efd var(--bg-gradient-end, 180%)) #0da5f5;color:#000}.bg-gradient-cyan-indigo{--bslib-color-fg: #000;--bslib-color-bg: #3180f1;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #3180f1;color:#000}.bg-gradient-cyan-purple{--bslib-color-fg: #000;--bslib-color-bg: #3494dd;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #3494dd;color:#000}.bg-gradient-cyan-pink{--bslib-color-fg: #000;--bslib-color-bg: #5d8ec5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #d63384 var(--bg-gradient-end, 180%)) #5d8ec5;color:#000}.bg-gradient-cyan-red{--bslib-color-fg: #000;--bslib-color-bg: #608eac;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #dc3545 var(--bg-gradient-end, 180%)) #608eac;color:#000}.bg-gradient-cyan-orange{--bslib-color-fg: #000;--bslib-color-bg: #6dac98;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #6dac98;color:#000}.bg-gradient-cyan-yellow{--bslib-color-fg: #000;--bslib-color-bg: #6ec693;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #ffc107 var(--bg-gradient-end, 180%)) #6ec693;color:#000}.bg-gradient-cyan-green{--bslib-color-fg: #000;--bslib-color-bg: #12afb2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #198754 var(--bg-gradient-end, 180%)) #12afb2;color:#000}.bg-gradient-cyan-teal{--bslib-color-fg: #000;--bslib-color-bg: #15cacc;background:linear-gradient(var(--bg-gradient-deg, 140deg), #0dcaf0 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #15cacc;color:#000}.row>main{max-width:50rem}@media (min-width: 1200px) and (max-width: 1399.98px){.container .row{justify-content:space-evenly}}@media (min-width: 1400px){body{font-size:18px}.col-md-3{margin-left:5rem}}.navbar-nav .nav-item>.nav-link{border-radius:.375rem;padding:0.5rem}.navbar>.container{align-items:baseline;-webkit-align-items:baseline}.navbar-light .navbar-nav .active>.nav-link{background:#CAC8C2;color:#191918}.navbar-dark .navbar-nav .active>.nav-link{background:#323230;color:#FCFAF2}.navbar-dark .navbar-nav .nav-item>.nav-link:hover,.navbar-light .navbar-nav .nav-item>.nav-link:hover{background:rgba(5,66,163,0.1)}.navbar-dark input[type="search"]{border-color:#656461;background-color:#191918;color:#CAC8C2}input[type="search"]{border-color:#B0AFA9;width:12rem}.headroom{will-change:transform;transition:transform 400ms ease}.headroom--pinned{transform:translateY(0%)}.headroom--unpinned{transform:translateY(-100%)}.row>main,.row>aside{margin-top:56px}html,body{scroll-padding:56px}@media (min-width: 576px){#toc{position:sticky;top:56px;max-height:calc(100vh - 56px - 1rem);overflow-y:auto}}aside h2,aside .h2{margin-top:1.5rem;font-size:1.25rem}aside .roles{color:#464644}aside .list-unstyled li{margin-bottom:0.5rem}aside .dev-status .list-unstyled li{margin-bottom:0.1rem}@media (max-width: 575.98px){aside{margin:0.5rem;width:calc(100vw - 1rem);background-color:#E3E1DA;border-color:#B0AFA9;border-radius:.375rem}aside h2:first-child,aside .h2:first-child{margin-top:1rem}}body{position:relative}#toc>.nav{margin-bottom:1rem}#toc>.nav a.nav-link{color:inherit;padding:0.25rem 0.5rem;margin-bottom:2px;border-radius:.375rem;border:0 solid #B0AFA9}#toc>.nav a.nav-link:hover,#toc>.nav a.nav-link:focus{background-color:rgba(5,66,163,0.1);color:#000}#toc>.nav a.nav-link.active{background-color:#e5e4dc;color:#000}#toc>.nav .nav a.nav-link{margin-left:0.5rem}#toc>.nav .nav{display:none !important}#toc>.nav a.active+.nav{display:flex !important}footer{margin:1rem 0 1rem 0;padding-top:1rem;font-size:.875em;border-top:1px solid #B0AFA9;background:rgba(0,0,0,0);color:#464644;display:flex;column-gap:1rem}@media (max-width: 575.98px){footer{flex-direction:column}}@media (min-width: 576px){footer .pkgdown-footer-right{text-align:right}}footer div{flex:1 1 auto}html,body{height:100%}body>.container{min-height:100%;display:flex;flex-direction:column}body>.container .row{flex:1 0 auto}::selection{background-color:#cbd5e2}main img{max-width:100%;height:auto}main table{display:block;overflow:auto}body{font-display:fallback}.page-header{border-bottom:1px solid #B0AFA9;padding-bottom:0.5rem;margin-bottom:0.5rem;margin-top:1.5rem}dd{margin-left:1.5rem}summary{margin-bottom:0.5rem}details{margin-bottom:1rem}.html-widget{margin-bottom:1rem}a.anchor{display:none;margin-left:5px;width:Min(0.9em, 20px);height:Min(0.9em, 20px);background-image:url(../../link.svg);background-repeat:no-repeat;background-size:Min(0.9em, 20px) Min(0.9em, 20px);background-position:center center}h2:hover .anchor,.h2:hover .anchor,h3:hover .anchor,.h3:hover .anchor,h4:hover .anchor,.h4:hover .anchor,h5:hover .anchor,.h5:hover .anchor,h6:hover .anchor,.h6:hover .anchor{display:inline-block}.orcid{color:#A6CE39;margin-right:4px}.fab{font-family:"Font Awesome 5 Brands" !important}img.logo{float:right;width:100px;margin-left:30px}.template-home img.logo{width:120px}@media (max-width: 575.98px){img.logo{width:80px}}@media (min-width: 576px){.page-header{min-height:88px}.template-home .page-header{min-height:104px}}.line-block{margin-bottom:1rem}.template-reference-index dt{font-weight:normal}.template-reference-index code{word-wrap:normal}.icon{float:right}.icon img{width:40px}a[href='#main']{position:absolute;margin:4px;padding:0.75rem;background-color:#FCFAF2;text-decoration:none;z-index:2000}a.footnote-ref{cursor:pointer}.popover{width:Min(100vw, 32rem);font-size:0.9rem;box-shadow:4px 4px 8px rgba(0,0,0,0.3)}.popover-body{padding:0.75rem}.popover-body p:last-child{margin-bottom:0}.tab-content{padding:1rem}.tabset-pills .tab-content{border:solid 1px #e5e5e5}.tab-content{display:flex}.tab-content>.tab-pane{display:block;visibility:hidden;margin-right:-100%;width:100%}.tab-content>.active{visibility:visible}div.csl-entry{clear:both}.hanging-indent div.csl-entry{margin-left:2em;text-indent:-2em}div.csl-left-margin{min-width:2em;float:left}div.csl-right-inline{margin-left:2em;padding-left:1em}div.csl-indent{margin-left:2em}pre,pre code{word-wrap:normal}code{overflow-wrap:break-word}.hasCopyButton{position:relative}.btn-copy-ex{position:absolute;right:5px;top:5px;visibility:hidden}.hasCopyButton:hover button.btn-copy-ex{visibility:visible}pre{padding:1rem 0.5rem}@media (max-width: 575.98px){div>div>pre{margin-left:calc(var(--bs-gutter-x) * -.5);margin-right:calc(var(--bs-gutter-x) * -.5);border-radius:0;padding-left:1rem;padding-right:1rem}.btn-copy-ex{right:calc(var(--bs-gutter-x) * -.5 + 5px)}}code a:any-link{color:inherit;text-decoration-color:#656461}pre code{padding:0;background:transparent}pre code .error,pre code .warning{font-weight:bolder}pre .img img,pre .r-plt img{margin:5px 0;background-color:#fff}@media print{code a:link:after,code a:visited:after{content:""}}a.sourceLine:hover{text-decoration:none}mark,.mark{background:linear-gradient(-100deg, rgba(13,202,240,0.2), rgba(13,202,240,0.7) 95%, rgba(13,202,240,0.1))}.algolia-autocomplete .aa-hint{color:#191918}.algolia-autocomplete .aa-dropdown-menu{width:Max(100%, 20rem);background-color:#FCFAF2;border:1px solid var(--bs-border-color);margin-top:2px;max-height:50vh;overflow-y:auto}.algolia-autocomplete .aa-dropdown-menu .aa-suggestion{cursor:pointer;padding:5px 4px;border-bottom:1px #CAC8C2 solid;font-size:0.9rem;color:#191918}.search-details{font-size:0.9rem;color:#0542A3;display:inline;font-weight:bolder}.algolia-autocomplete .aa-dropdown-menu 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diff --git a/docs/deps/data-deps.txt b/docs/deps/data-deps.txt
new file mode 100644
index 0000000..693b82d
--- /dev/null
+++ b/docs/deps/data-deps.txt
@@ -0,0 +1,4 @@
+<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script>
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
+<link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.js
new file mode 100644
index 0000000..fc6c299
--- /dev/null
+++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	}
+);
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js
new file mode 100644
index 0000000..c4c6022
--- /dev/null
+++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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diff --git a/docs/index.html b/docs/index.html
new file mode 100644
index 0000000..abb3d3b
--- /dev/null
+++ b/docs/index.html
@@ -0,0 +1,148 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
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+<meta name="description" content="Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.">
+<title>Density Surface Modelling of Distance Sampling Data • dsm</title>
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+<meta property="og:description" content="Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.">
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+<h2 id="dsm---density-surface-modelling">
+<code>dsm</code> - Density surface modelling<a class="anchor" aria-label="anchor" href="#dsm---density-surface-modelling"></a>
+</h2>
+<p><a href="https://travis-ci.org/DistanceDevelopment/dsm" class="external-link"><img src="https://travis-ci.org/DistanceDevelopment/dsm.svg?branch=master" alt="Build Status"></a> <a href="https://www.r-pkg.org:443/pkg/dsm" class="external-link"><img src="http://cranlogs.r-pkg.org/badges/dsm" alt="CRAN (RStudio Mirror) Downloads"></a> <a href="https://www.r-pkg.org:443/pkg/dsm" class="external-link"><img src="http://www.r-pkg.org/badges/version/dsm" alt="CRAN Version"></a></p>
+</div>
+<div class="section level1">
+<div class="page-header"><h1 id="what-is-dsm">What is <code>dsm</code>?<a class="anchor" aria-label="anchor" href="#what-is-dsm"></a>
+</h1></div>
+<p><code>dsm</code> provides a Generalized Additive Model (GAM)-based approach to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.</p>
+<p>Open Access paper describing the methods: <a href="https://doi.org/10.1111/2041-210X.12105" class="external-link">Miller, DL, ML Burt, EA Rexstad and L Thomas (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution.</a></p>
+</div>
+<div class="section level1">
+<h1 id="using-dsm">Using <code>dsm</code>
+<a class="anchor" aria-label="anchor" href="#using-dsm"></a>
+</h1>
+<p>Examples of how to use <code>dsm</code> and other distance sampling R packages are available at <a href="http://examples.distancesampling.org" class="external-link">examples.distancesampling.org</a>.</p>
+</div>
+
+  </main><aside class="col-md-3"><div class="links">
+<h2 data-toc-skip>Links</h2>
+<ul class="list-unstyled">
+<li><a href="https://cloud.r-project.org/package=dsm" class="external-link">View on CRAN</a></li>
+<li><a href="https://github.com/DistanceDevelopment/dsm/" class="external-link">Browse source code</a></li>
+<li><a href="https://github.com/DistanceDevelopment/dsm/issues" class="external-link">Report a bug</a></li>
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+<h2 data-toc-skip>License</h2>
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+<li>GPL (&gt;= 2)</li>
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+<h2 data-toc-skip>Developers</h2>
+<ul class="list-unstyled">
+<li>David L. Miller <br><small class="roles"> Author </small>  </li>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+      <img src="" class="logo" alt=""><h1>Changelog</h1>
+      <small>Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
+    </div>
+
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.3.4" id="dsm-234">dsm 2.3.4<a class="anchor" aria-label="anchor" href="#dsm-234"></a></h2>
+<ul><li>fix bug where if predictions were NA (e.g., predictions outside the soap boundary), all variances were NA due to propagation (<a href="https://github.com/DistanceDevelopment/dsm/issues/4" class="external-link">#4</a>)</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.3.3" id="dsm-233">dsm 2.3.3<a class="anchor" aria-label="anchor" href="#dsm-233"></a></h2><p class="text-muted">CRAN release: 2022-08-20</p>
+<ul><li>To improve consistency in functions in the package, some functions have changed from . separation to <em>. For function arguments some separators have been switched from </em> to .. An error is now thrown when the “old” arguments/functions using . are used. This error will be removed in dsm 2.3.4.
+<ul><li>dsm.var.prop -&gt; dsm_var_prop</li>
+<li>dsm.var.gam -&gt; dsm_var_gam</li>
+<li>dsm.var.movblk -&gt; dsm_var_movblk</li>
+<li>dsm.cor -&gt; dsm_cor</li>
+<li>rqgam.check -&gt; rqgam_check</li>
+<li>vis.concurvity -&gt; vis_concurvity</li>
+<li>dsm_varprop
+<ul><li>var_type -&gt; var.type</li>
+</ul></li>
+</ul></li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.3.2" id="dsm-232">dsm 2.3.2<a class="anchor" aria-label="anchor" href="#dsm-232"></a></h2><p class="text-muted">CRAN release: 2022-03-17</p>
+<ul><li><p>fixed bug in offset calculation in dsm_varprop se estimation where offsets were logged twice. Thanks to Megan Ferguson for spotting this.</p></li>
+<li><p>documentation now in rmarkdown format</p></li>
+<li><p>dsm 2.3.1</p></li>
+<li><p>transect type (line or point) is now determined from the detection function rather than using the transect= argument</p></li>
+<li><p>multiple detection functions are now possible, use a list() of fitted detection functions supplied to ddf.obj= argument to dsm(). dsm.var.gam, dsm.var.prop and dsm_varprop are compatible. Vignette coming soon. Thanks to Dave Fifield and Ewan Wakefield for extensive testing and suggestions. See ?“dsm-data” for more information on how to set up your data.</p></li>
+<li><p>strip transects now handled using the dummy_ddf() function.</p></li>
+<li><p>presence~ response type removed</p></li>
+<li><p>Response options “D”, “density”, “Dhat”, “N”, “Nhat” and “n” deprecated. Now only “count”, “abundance.est” and “density.est” are allowed.</p></li>
+<li><p>Make predict.dsm work with tibbles when off.set is not a column in newdata (error is thrown in previous code’s check of this). Thanks to Dave Fifield for reporting this issue.</p></li>
+<li><p>Fix bug with inconsistent predictions when density.est is used as the response. Code in predict.dsm has been simplified and documentation updated. Thanks to Dave Fifield for reporting this issue.</p></li>
+<li><p>mrds independent observer (io) and trial models are allowed for the detection function.Variance can be estimated via dsm_varprop (with the usual limitation that covariates may only vary at the segment level) or dsm.var.gam. This feature is currently experimental. Thanks to Doug Sigourney and Megan Ferguson for their help and suggestions. See ?“dsm-data” for more information on how to set up your data.</p></li>
+<li><p>An example of multiddf and mrds analysis in dsm is at <a href="https://github.com/densitymodelling/nefsc_fin_mrds_dsm" class="external-link uri">https://github.com/densitymodelling/nefsc_fin_mrds_dsm</a></p></li>
+<li><p>availability= argument now works differently for count models. availability must now be the same length as number of segments (nrow(segment.data)) and will multiply the offset (effective area) rather than the number of animals observed to be more coherent with how detectability is handled in this case.</p></li>
+<li><p>dsm_varprop no longer requies newdata to be supplied. If newdata isn’t present just the refitted and original models are returned, with other entries NA.</p></li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.3.0" id="dsm-230">dsm 2.3.0<a class="anchor" aria-label="anchor" href="#dsm-230"></a></h2><p class="text-muted">CRAN release: 2020-04-22</p>
+<ul><li>fixed dsm_varprop (gam.fixed.priors) for response distributions with fixed scale parameters. Negative binomial results before now may have been incorrect!</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.18" id="dsm-2218">dsm 2.2.18<a class="anchor" aria-label="anchor" href="#dsm-2218"></a></h2>
+<ul><li>Clarification of the predict.dsm return documentation. Thanks to Dave Fifield for the suggestion.</li>
+<li>off.set=NULL lead to predictions of 0 when density was used. Now just returns density. Thanks to Dave Fifield for the catch.</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.17" id="dsm-2217">dsm 2.2.17<a class="anchor" aria-label="anchor" href="#dsm-2217"></a></h2><p class="text-muted">CRAN release: 2019-01-19</p>
+<ul><li>Now Suggests package “sp” (needed for some parts of the mexdolphin example data)</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.16" id="dsm-2216">dsm 2.2.16<a class="anchor" aria-label="anchor" href="#dsm-2216"></a></h2><p class="text-muted">CRAN release: 2018-06-27</p>
+<ul><li>check that any Sample.Labels match up, if none do then throw an error. Thanks to Madhura Davate for bringing this issue to my attention.</li>
+<li>gamma option removed, since it now is not ignored by gam(). If using method=“GCV.Cp” then gamma=1.4 should still be used (but generally we advise using REML rather than GCV for smoothing parameter selection)</li>
+<li>dsm now requires mgcv version 1.8-23 or higher.</li>
+<li>dsm.var.prop (and dsm_varprop) now throw an error when there are no covariates in the detection function as it can’t do anything useful</li>
+<li>new function obs_exp is a diagnostic function to compare observed and expected counts at different covariate aggregations</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.15" id="dsm-2215">dsm 2.2.15<a class="anchor" aria-label="anchor" href="#dsm-2215"></a></h2><p class="text-muted">CRAN release: 2017-07-03</p>
+<ul><li>when mexdolphins is loaded using data(), the data no longer need to be attach()ed</li>
+<li>dsm.var.prop throws an error when count or N are not used as the response.</li>
+<li>dsm.var.prop now calls dsm_varprop, which keeps the scale parameter constant when refitting models for variance propagation. dsm_varprop provides a lower-level interface to variance propagation and is probably faster and more flexible.</li>
+<li>add vis.concurvity to visually assess concurvity (colinearity extended to smooth terms) in the model</li>
+<li>new model diagnostic function: plot_pred_by_term – plots the effect of each smooth in the model spatially.</li>
+<li>remove “abundance” option for the response, use “count”</li>
+<li>dsm.var.prop and dsm_varprop have better diagnostics for checking the variance model hasn’t done something weird</li>
+<li>dsm.var.movblk discards replicates where the model didn’t converge</li>
+<li>fix potential issue with negative binomial response is used with variance propagation (due to scale parameterisations). dsm_varprop now refits nb() models as negbin() but we advise users to look into this more deeply. Thanks to Eric Rexstad for spotting this.</li>
+<li>fix bug in predict.dsm() where predictions for “density” response were not including the offset. Thanks to Len Thomas for spotting this.</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.14" id="dsm-2214">dsm 2.2.14<a class="anchor" aria-label="anchor" href="#dsm-2214"></a></h2><p class="text-muted">CRAN release: 2017-01-26</p>
+<ul><li>nasty bug in predict.dsm fixed: was logging the offset twice when newdata was not provided, leading to very odd checking plots. This also effected the results of predict() when newdata= was not specified. Thanks to Megan Ferguson for alerting us to this.</li>
+<li>add support for point transects(!)</li>
+<li>rqgam.check() now produced ONLY the linear predictor vs. randomised quantile residuals plot to avoid confusion in interpreting the other plots</li>
+<li>REML is now default method for smoothing parameter selection</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.13" id="dsm-2213">dsm 2.2.13<a class="anchor" aria-label="anchor" href="#dsm-2213"></a></h2><p class="text-muted">CRAN release: 2016-10-03</p>
+<ul><li>dsm.var.gam now has an example</li>
+<li>dsm.var.prop now checks that the detection function model does not have covariates</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.12" id="dsm-2212">dsm 2.2.12<a class="anchor" aria-label="anchor" href="#dsm-2212"></a></h2><p class="text-muted">CRAN release: 2016-08-10</p>
+<ul><li>The alpha argument now works for analytical variance reported using dsm.var.prop and dsm.var.gam print.summary and summary methods. Thanks to Jason Roberts and Eric Rexstad for spotting this issue.</li>
+<li>dsm now uses numDeriv::grad to calculate derivatives for variance estimation – this should be much faster. Thanks to Jason Roberts for this fix.</li>
+<li>dsm now requires mrds version 2.1.15 or higher, due to internal changes</li>
+<li>fixed issue where Effort column was required even when segment.area was specified. Thanks to Greg Lollback for spotting this issue.</li>
+<li>ensured that ordering of segments matched the ordering of the observations so that aggregation of observations are correct. Thanks to Dave Fifield for reporting this bug and suggesting a fix.</li>
+<li>check.cols no longer checks to see if there is a column called “distance” when strip transects are being used</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.11" id="dsm-2211">dsm 2.2.11<a class="anchor" aria-label="anchor" href="#dsm-2211"></a></h2>
+<ul><li>dsm.var.prop uses update() rather than messing around with eval()</li>
+<li>print.summary.dsm.var didn’t work for GAM variance when no detection function was specified, now fixed (thanks to Natalia Dellabianca for spotting this)</li>
+<li>Removed non-working plot() code in variance examples – see the Mexico pantropical spotted dolphins for a proper treatment of plotting using projections (<a href="http://distancesampling.org/R/vignettes/mexico-analysis.html" class="external-link uri">http://distancesampling.org/R/vignettes/mexico-analysis.html</a>)</li>
+<li>plot.var.gam no longer crashes by default when distance data doesn’t have columns “x” and “y”</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.10" id="dsm-2210">dsm 2.2.10<a class="anchor" aria-label="anchor" href="#dsm-2210"></a></h2>
+<ul><li>corrected offsets in examples in dsm, dsm.var.prop, dsm.var.movblk</li>
+<li>updated mexdolphins data with lat/long reversal in prediction data and non-list storage</li>
+<li>stopped use of rqgam.check from being used with non-negbin/Tweedie responses (thanks to Phil Bouchet for highlighting this issue).</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.9" id="dsm-229">dsm 2.2.9<a class="anchor" aria-label="anchor" href="#dsm-229"></a></h2><p class="text-muted">CRAN release: 2015-07-29</p>
+<ul><li>Moving block bootstrap now works when there is no detection function (suggested by Laura Williamson)</li>
+<li>Mexico dolphin data has had the areas of the prediction grids and segments re-projected. Thanks to Phil Bouchet for pointing out this error. Context: <a href="https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ" class="external-link uri">https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ</a>
+</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.8" id="dsm-228">dsm 2.2.8<a class="anchor" aria-label="anchor" href="#dsm-228"></a></h2>
+<ul><li>Speed up and reduced memory usage in variance calculations, thanks to Filipe Dias for asking about this.</li>
+<li>Better error when no observations are matched to the detection function. Thanks to Ricardo Lima for highlighting this.</li>
+<li>Check for missing columns had incorrect logic, now corrected and with tests. Thanks to Julia Migné for spotting this.</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.7" id="dsm-227">dsm 2.2.7<a class="anchor" aria-label="anchor" href="#dsm-227"></a></h2>
+<ul><li>Plotting check plots using random quantile residuals (rqgam.check) didn’t work for Tweedie or tw family models, now fixed. Thanks to Laura Mannocci and Jason Roberts for pointing this out.</li>
+<li>Plotting CV used matrix() to coerce data into a data.frame – thus converting all elements to the simplest type. Errors ensued. Thanks to Adrian Schiavini for finding this bug!</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.6" id="dsm-226">dsm 2.2.6<a class="anchor" aria-label="anchor" href="#dsm-226"></a></h2>
+<ul><li>dsm.var.prop and dsm.var.gam now allow you to supply a single number as an offset, then copy it as many times as you need. Thanks to Filipe Dias for suggesting this.</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.5" id="dsm-225">dsm 2.2.5<a class="anchor" aria-label="anchor" href="#dsm-225"></a></h2><p class="text-muted">CRAN release: 2014-09-16</p>
+<ul><li>dsm.var.prop will now throw an error if there is no detection function in the dsm that it’s trying to calculate the variance for (thanks to Adrián Schiavini for finding this)</li>
+<li>variance/CV plotting threw an error when no detection function was supplied but observation=TRUE. Now error is sidestepped (again, thanks to Adrián Schiavini for finding this).</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.4" id="dsm-224">dsm 2.2.4<a class="anchor" aria-label="anchor" href="#dsm-224"></a></h2><p class="text-muted">CRAN release: 2014-06-17</p>
+<ul><li>changed stop() to warning() when data not in the detection function are included in the observation data frame, as they are automatically excluded later, bug found by Eric Rexstad and Anna Cucknell</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.3" id="dsm-223">dsm 2.2.3<a class="anchor" aria-label="anchor" href="#dsm-223"></a></h2><p class="text-muted">CRAN release: 2014-04-23</p>
+<ul><li>moving block boostrap was using observed abundances as the residuals and therefore gave wrong answers (Eric Rexstad found this bug)</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.2" id="dsm-222">dsm 2.2.2<a class="anchor" aria-label="anchor" href="#dsm-222"></a></h2>
+<ul><li>version number reporting now works (Len Thomas found this bug)</li>
+<li>more documentation</li>
+<li>by default density and presence models are now weighted by area of each segment</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.2.1" id="dsm-221">dsm 2.2.1<a class="anchor" aria-label="anchor" href="#dsm-221"></a></h2>
+<ul><li>switch over to new testthat spec</li>
+<li>added new response variables (Dhat, density.est, count, n), suggestion from Len Thomas</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.1.4" id="dsm-214">dsm 2.1.4<a class="anchor" aria-label="anchor" href="#dsm-214"></a></h2>
+<ul><li>check that all observed distances are within width will now throw an error</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.1.3" id="dsm-213">dsm 2.1.3<a class="anchor" aria-label="anchor" href="#dsm-213"></a></h2><p class="text-muted">CRAN release: 2013-08-20</p>
+<ul><li>CRAN-compatibility fixes</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.1.2" id="dsm-212">dsm 2.1.2<a class="anchor" aria-label="anchor" href="#dsm-212"></a></h2>
+<ul><li>updates to dsm.cor</li>
+<li>more unified plotting</li>
+<li>variance estimation tweaks for non-dsm models</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.0.6" id="dsm-206">dsm 2.0.6<a class="anchor" aria-label="anchor" href="#dsm-206"></a></h2>
+<ul><li>removed warning about if() comparison length in make.soapgrid need to pass bnd as a list()</li>
+<li>dsm.cor now handles gamm objects</li>
+<li>dsm.cor allows one to check residual autocorrelation in your models</li>
+<li>presence/absence data now no longer needs a dummy detection function object</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="2.0.5" id="dsm-205">dsm 2.0.5<a class="anchor" aria-label="anchor" href="#dsm-205"></a></h2>
+<ul><li>started NEWS file</li>
+<li>fixed bug where dsm.var.gam() didn’t return predictions so plotting the result would fail</li>
+<li>silly typo in docs</li>
+<li>check if we’re using mgcv version &gt;= 1.7-24 for GAMM fitting and warn if not</li>
+</ul></div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
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+  
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+
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+pandoc: 3.1.11
+pkgdown: 2.0.9
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the transect data and works out how many blocks of a given
+size (in segment terms) fit into each."><title>Find the block information — block.info.per.su • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Find the block information — block.info.per.su"><meta property="og:description" content="Takes the transect data and works out how many blocks of a given
+size (in segment terms) fit into each."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Find the block information</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/block.info.per.su.R" class="external-link"><code>R/block.info.per.su.R</code></a></small>
+      <div class="d-none name"><code>block.info.per.su.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Takes the transect data and works out how many blocks of a given
+size (in segment terms) fit into each.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">block.info.per.su</span><span class="op">(</span><span class="va">block.size</span>, <span class="va">data</span>, <span class="va">name.su</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>block.size</dt>
+<dd><p>number of segments per block</p></dd>
+
+
+<dt>data</dt>
+<dd><p>data used to build the model</p></dd>
+
+
+<dt>name.su</dt>
+<dd><p>names of the sampling units (i.e., transects)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a <code>data.frame</code> with the following columns</p><ul><li><p><code>name</code>        the sample unit name (e.g. transect label)</p></li>
+<li><p><code>num.seg</code>     number of segments in that transect</p></li>
+<li><p><code>num.block</code>   number of blocks available</p></li>
+<li><p><code>start.block</code> block number for first block</p></li>
+<li><p><code>end.block</code>   block number for last block</p></li>
+<li><p><code>num.req</code>     number of blocks needed for the unit</p></li>
+</ul></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/check.cols.html b/docs/reference/check.cols.html
new file mode 100644
index 0000000..6e0f66e
--- /dev/null
+++ b/docs/reference/check.cols.html
@@ -0,0 +1,120 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Internal function to check that supplied data.frames have the correct
+columns and checks that sample labels are all unique."><title>Check column names exist — check.cols • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check column names exist — check.cols"><meta property="og:description" content="Internal function to check that supplied data.frames have the correct
+columns and checks that sample labels are all unique."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Check column names exist</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/check.cols.R" class="external-link"><code>R/check.cols.R</code></a></small>
+      <div class="d-none name"><code>check.cols.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Internal function to check that supplied <code>data.frames</code> have the correct
+columns and checks that sample labels are all unique.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">check.cols</span><span class="op">(</span><span class="va">ddf.obj</span>, <span class="va">segment.data</span>, <span class="va">observation.data</span>, <span class="va">segment.area</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>ddf.obj</dt>
+<dd><p>a <code>ddf</code> object from <code>mrds</code></p></dd>
+
+
+<dt>segment.data</dt>
+<dd><p>segment data as defined in <code><a href="dsm.html">dsm</a></code></p></dd>
+
+
+<dt>observation.data</dt>
+<dd><p>observation data as defined in <code><a href="dsm.html">dsm</a></code></p></dd>
+
+
+<dt>segment.area</dt>
+<dd><p>area of segments</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>nothing, but throws an error if something went wrong</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David Lawrence Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/dsm-data.html b/docs/reference/dsm-data.html
new file mode 100644
index 0000000..aae1bc0
--- /dev/null
+++ b/docs/reference/dsm-data.html
@@ -0,0 +1,138 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Two data.frames must be provided to dsm. They are referred to as
+observation.data and segment.data."><title>Data format for DSM — dsm-data • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data format for DSM — dsm-data"><meta property="og:description" content="Two data.frames must be provided to dsm. They are referred to as
+observation.data and segment.data."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Data format for DSM</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm-package.R" class="external-link"><code>R/dsm-package.R</code></a></small>
+      <div class="d-none name"><code>dsm-data.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Two <code>data.frame</code>s must be provided to <code><a href="dsm.html">dsm</a></code>. They are referred to as
+<code>observation.data</code> and <code>segment.data</code>.</p>
+    </div>
+
+
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>The <code>segment.data</code> table has the sample identifiers which define the
+segments, the corresponding effort (line length) expended and the
+environmental covariates that will be used to model abundance/density.
+<code>observation.data</code> provides a link table between the observations used in
+the detection function and the samples (segments), so that we can aggregate
+the observations to the segments (i.e., <code>observation.data</code> is a "look-up
+table" between the observations and the segments).</p>
+<p><code>observation.data</code> - the observation <code>data.frame</code> must have (at least) the
+following columns:</p><ul><li><p><code>object</code> unique object identifier</p></li>
+<li><p><code>Sample.Label</code> the identifier for the segment where observation occurred</p></li>
+<li><p><code>size</code> the size of each observed group (e.g., 1 if all animals occurred
+individually)</p></li>
+<li><p><code>distance</code> distance to observation</p></li>
+</ul><p>One can often also use <code>observation.data</code> to fit a detection function (so
+additional columns for detection function covariates are allowed in this
+table).</p>
+<p><code>segment.data</code>: the segment <code>data.frame</code> must have (at least) the following
+columns:</p><ul><li><p><code>Effort</code> the effort (in terms of length of the segment)</p></li>
+<li><p><code>Sample.Label</code> identifier for the segment (unique!)</p></li>
+<li><p>??? environmental covariates, for example location (projected latitude
+and longitude), and other relevant covariates (sea surface temperature,
+foliage type, altitude, bathymetry etc).</p></li>
+</ul></div>
+    <div class="section level2">
+    <h2 id="multiple-detection-functions">Multiple detection functions<a class="anchor" aria-label="anchor" href="#multiple-detection-functions"></a></h2>
+    
+
+
+<p>If multiple detection functions are to be used, then a column named <code>ddfobj</code>
+must be included in <code>observation.data</code> and <code>segment.data</code>. This lets the
+model know which detection function each observation is from. These are
+numeric and ordered as the <code>ddf.obj</code> argument to <code><a href="dsm.html">dsm</a></code>, e.g.,
+<code>ddf.obj=list(ship_ddf, aerial_ddf)</code> means ship detections have <code>ddfobj=1</code>
+and aerial detections have <code>ddfobj=2</code> in the observation data.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="mark-recapture-distance-sampling-models">Mark-recapture distance sampling models<a class="anchor" aria-label="anchor" href="#mark-recapture-distance-sampling-models"></a></h2>
+    
+
+
+<p>When using <code>mrds</code> models that include mark-recapture components (currently
+independent observer and trial modes are supported) then the format of the
+observation data needs to be checked to ensure that observations are not
+duplicated. The <code>observer</code> column is also required in the
+<code>observation.data</code>.</p><ul><li><p><em>Independent observer mode</em> only unique observations (unique object IDs)
+are required.</p></li>
+<li><p><em>Trial mode</em> only observations made by observer 1 are required.</p></li>
+</ul></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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+</div>
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+
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+  
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+
diff --git a/docs/reference/dsm-package.html b/docs/reference/dsm-package.html
new file mode 100644
index 0000000..ec1279d
--- /dev/null
+++ b/docs/reference/dsm-package.html
@@ -0,0 +1,113 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="dsm implements spatial models for distance sampling data. Models for
+detectability can be fitted using packages mrds or Distance. dsm fits
+generalized additive models to spatially-referenced data. See Miller et al
+(2013) for an introduction."><title>Density surface modelling — dsm-package • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Density surface modelling — dsm-package"><meta property="og:description" content="dsm implements spatial models for distance sampling data. Models for
+detectability can be fitted using packages mrds or Distance. dsm fits
+generalized additive models to spatially-referenced data. See Miller et al
+(2013) for an introduction."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
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+
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
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+
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Density surface modelling</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm-package.R" class="external-link"><code>R/dsm-package.R</code></a></small>
+      <div class="d-none name"><code>dsm-package.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p><code>dsm</code> implements spatial models for distance sampling data. Models for
+detectability can be fitted using packages <code>mrds</code> or <code>Distance</code>. <code>dsm</code> fits
+generalized additive models to spatially-referenced data. See Miller et al
+(2013) for an introduction.</p>
+    </div>
+
+
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Further information on distance sampling methods and example code is
+available at <a href="http://distancesampling.org/R/" class="external-link">http://distancesampling.org/R/</a>.</p>
+<p>For help with distance sampling and this package, there is a Google Group
+<a href="https://groups.google.com/forum/#!forum/distance-sampling" class="external-link">https://groups.google.com/forum/#!forum/distance-sampling</a>.</p>
+<p>A example analyses are available at <a href="http://examples.distancesampling.org" class="external-link">http://examples.distancesampling.org</a>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hedley, S. and S. T. Buckland. 2004. Spatial models for line
+transect sampling. JABES 9:181-199.</p>
+<p>Miller, D. L., Burt, M. L., Rexstad, E. A., Thomas, L. (2013), Spatial
+models for distance sampling data: recent developments and future
+directions. Methods in Ecology and Evolution, 4: 1001-1010. doi:
+10.1111/2041-210X.12105 (Open Access)</p>
+<p>Wood, S.N. 2006. Generalized Additive Models: An Introduction with R.
+CRC/Chapman &amp; Hall.</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
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+
diff --git a/docs/reference/dsm.cor.html b/docs/reference/dsm.cor.html
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@@ -0,0 +1,144 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use dsm_cor."><title>Check for autocorrelation in residuals — dsm.cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm.cor"><meta property="og:description" content="This function is deprecated, use dsm_cor."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    <span class="fab fa fab fa-github fa-lg"></span>
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+      </ul></div>
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+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Check for autocorrelation in residuals</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
+      <div class="d-none name"><code>dsm.cor.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function is deprecated, use <a href="dsm_cor.html">dsm_cor</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm.cor</span><span class="op">(</span></span>
+<span>  <span class="va">dsm.obj</span>,</span>
+<span>  Transect.Label <span class="op">=</span> <span class="st">"Transect.Label"</span>,</span>
+<span>  Segment.Label <span class="op">=</span> <span class="st">"Segment.Label"</span>,</span>
+<span>  max.lag <span class="op">=</span> <span class="fl">10</span>,</span>
+<span>  resid.type <span class="op">=</span> <span class="st">"scaled.pearson"</span>,</span>
+<span>  fun <span class="op">=</span> <span class="va">cor</span>,</span>
+<span>  ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span>,</span>
+<span>  subset <span class="op">=</span> <span class="st">"all"</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.obj</dt>
+<dd><p>a fitted dsm object.</p></dd>
+
+
+<dt>Transect.Label</dt>
+<dd><p>label for the transect (default: <code>Transect.Label</code>).
+Using different labels can be useful when transects are split over
+geographical features or when transects are surveyed multiple times.</p></dd>
+
+
+<dt>Segment.Label</dt>
+<dd><p>label for the segments (default: <code>Segment.Label</code>).The
+result of calling <code><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></code> must make sense.</p></dd>
+
+
+<dt>max.lag</dt>
+<dd><p>maximum lag to calculate at.</p></dd>
+
+
+<dt>resid.type</dt>
+<dd><p>the type of residuals used, see
+<code>residuals.gam</code>. Defaults to <code>"scaled.pearson"</code> in the GAM
+case and <code>"normalized"</code> in the GAMM case (which are equivalent).</p></dd>
+
+
+<dt>fun</dt>
+<dd><p>the function to use, by default <code><a href="https://rdrr.io/r/stats/cor.html" class="external-link">cor</a></code>, must take two
+column vectors as arguments.</p></dd>
+
+
+<dt>ylim</dt>
+<dd><p>user defined limits in y direction.</p></dd>
+
+
+<dt>subset</dt>
+<dd><p>which subset of the data should the correlation function be
+calculated on?</p></dd>
+
+
+<dt>...</dt>
+<dd><p>other options to pass to <code><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></code>.</p></dd>
+
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
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diff --git a/docs/reference/dsm.html b/docs/reference/dsm.html
new file mode 100644
index 0000000..c63ce81
--- /dev/null
+++ b/docs/reference/dsm.html
@@ -0,0 +1,316 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fits a density surface model (DSM) to detection adjusted counts from a
+spatially-referenced distance sampling analysis. dsm takes observations of
+animals, allocates them to segments of line (or strip transects) and
+optionally adjusts the counts based on detectability using a supplied
+detection function model. A generalized additive model, generalized mixed
+model or generalized linear model is then used to model these adjusted
+counts based on a formula involving environmental covariates."><title>Fit a density surface model to segment-specific estimates of abundance or density. — dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit a density surface model to segment-specific estimates of abundance or density. — dsm"><meta property="og:description" content="Fits a density surface model (DSM) to detection adjusted counts from a
+spatially-referenced distance sampling analysis. dsm takes observations of
+animals, allocates them to segments of line (or strip transects) and
+optionally adjusts the counts based on detectability using a supplied
+detection function model. A generalized additive model, generalized mixed
+model or generalized linear model is then used to model these adjusted
+counts based on a formula involving environmental covariates."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
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+
+    
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+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
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+    <span class="fab fa fab fa-github fa-lg"></span>
+     
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+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Fit a density surface model to segment-specific estimates of abundance or density.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm.R" class="external-link"><code>R/dsm.R</code></a></small>
+      <div class="d-none name"><code>dsm.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Fits a density surface model (DSM) to detection adjusted counts from a
+spatially-referenced distance sampling analysis. <code>dsm</code> takes observations of
+animals, allocates them to segments of line (or strip transects) and
+optionally adjusts the counts based on detectability using a supplied
+detection function model. A generalized additive model, generalized mixed
+model or generalized linear model is then used to model these adjusted
+counts based on a formula involving environmental covariates.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm</span><span class="op">(</span></span>
+<span>  <span class="va">formula</span>,</span>
+<span>  <span class="va">ddf.obj</span>,</span>
+<span>  <span class="va">segment.data</span>,</span>
+<span>  <span class="va">observation.data</span>,</span>
+<span>  engine <span class="op">=</span> <span class="st">"gam"</span>,</span>
+<span>  convert.units <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  family <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/family.html" class="external-link">quasipoisson</a></span><span class="op">(</span>link <span class="op">=</span> <span class="st">"log"</span><span class="op">)</span>,</span>
+<span>  group <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>keepData <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
+<span>  availability <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  segment.area <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  weights <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  method <span class="op">=</span> <span class="st">"REML"</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>formula</dt>
+<dd><p>formula for the surface. This should be a valid formula. See
+"Details", below, for how to define the response.</p></dd>
+
+
+<dt>ddf.obj</dt>
+<dd><p>result from call to <code><a href="https://rdrr.io/pkg/mrds/man/ddf.html" class="external-link">ddf</a></code> or
+<code><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></code>. If multiple detection functions are required a <code>list</code>
+can be provided. For strip/circle transects where it is assumed all objects
+are observed, see <code><a href="dummy_ddf.html">dummy_ddf</a></code>. Mark-recapture distance sampling
+(<code>mrds</code>) models of type <code>io</code> (independent observers) and <code>trial</code> are
+allowed.</p></dd>
+
+
+<dt>segment.data</dt>
+<dd><p>segment data, see <code><a href="dsm-data.html">dsm-data</a></code>.</p></dd>
+
+
+<dt>observation.data</dt>
+<dd><p>observation data, see <code><a href="dsm-data.html">dsm-data</a></code>.</p></dd>
+
+
+<dt>engine</dt>
+<dd><p>which fitting engine should be used for the DSM
+(<code>"glm"</code>/<code>"gam"</code>/<code>"gamm"</code>/<code>"bam"</code>).</p></dd>
+
+
+<dt>convert.units</dt>
+<dd><p>conversion factor to multiply the area of the segments
+by. See 'Units' below.</p></dd>
+
+
+<dt>family</dt>
+<dd><p>response distribution (popular choices include
+<code><a href="https://rdrr.io/r/stats/family.html" class="external-link">quasipoisson</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">Tweedie</a></code>/<code><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></code>
+and <code><a href="https://rdrr.io/pkg/mgcv/man/negbin.html" class="external-link">negbin</a></code>/<code><a href="https://rdrr.io/pkg/mgcv/man/negbin.html" class="external-link">nb</a></code>). Defaults
+<code><a href="https://rdrr.io/r/stats/family.html" class="external-link">quasipoisson</a></code>.</p></dd>
+
+
+<dt>group</dt>
+<dd><p>if <code>TRUE</code> the abundance of <em>groups</em> will be calculated rather
+than the abundance of <em>individuals</em>. Setting this option to <code>TRUE</code> is
+equivalent to setting the size of each group to be 1.</p></dd>
+
+
+<dt>control</dt>
+<dd><p>the usual <code>control</code> argument for a <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>;
+<code>keepData</code> must be <code>TRUE</code> for variance estimation to work (though this
+option cannot be set for GLMs or GAMMs).</p></dd>
+
+
+<dt>availability</dt>
+<dd><p>an estimate of availability bias. For count models used
+to multiply the effective strip width (must be a vector of length 1 or
+length the number of rows in <code>segment.data</code>); for estimated
+abundance/estimated density models used to scale the response (must be a
+vector of length 1 or length the number of rows in <code>observation.data</code>).
+Uncertainty in the availability is not handled at present.</p></dd>
+
+
+<dt>segment.area</dt>
+<dd><p>if <code>NULL</code> (default) segment areas will be calculated by
+multiplying the <code>Effort</code> column in <code>segment.data</code> by the (right minus left)
+truncation distance for the <code>ddf.obj</code> or by <code>strip.width</code>. Alternatively a
+vector of segment areas can be provided (which must be the same length as
+the number of rows in <code>segment.data</code>) or a character string giving the name
+of a column in <code>segment.data</code> which contains the areas. If <code>segment.area</code> is
+specified it takes precedent.</p></dd>
+
+
+<dt>weights</dt>
+<dd><p>weights for each observation used in model fitting. The
+default, <code>weights=NULL</code>, weights each observation by its area (see Details).
+Setting a scalar value (e.g., <code>weights=1</code>) all observations are equally
+weighted.</p></dd>
+
+
+<dt>method</dt>
+<dd><p>The smoothing parameter estimation method. Default is
+<code>"REML"</code>, using Restricted Maximum Likelihood. See <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code> for
+other options. Ignored for <code>engine="glm"</code>.</p></dd>
+
+
+<dt>...</dt>
+<dd><p>anything else to be passed straight to <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code>,
+<code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/gamm.html" class="external-link">gamm</a></code> or <code><a href="https://rdrr.io/pkg/mgcv/man/bam.html" class="external-link">bam</a></code>.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/gamm.html" class="external-link">gamm</a></code> or
+<code><a href="https://rdrr.io/pkg/mgcv/man/bam.html" class="external-link">bam</a></code> object, with an additional element, <code>$ddf</code> which holds the
+detection function object.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>The response (LHS of <code>formula</code>) can be one of the following (with
+restrictions outlined below):</p><ul><li><p><code>count</code> count in each segment</p></li>
+<li><p><code>abundance.est</code> estimated abundance per segment, estimation is via a
+Horvitz-Thompson estimator</p></li>
+<li><p><code>density.est</code> density per segment</p></li>
+</ul><p>The offset used in the model is dependent on the response:</p><ul><li><p><code>count</code> area of segment multiplied by average probability of detection
+in the segment</p></li>
+<li><p><code>abundance.est</code> area of the segment</p></li>
+<li><p><code>density</code> zero</p></li>
+</ul><p>The <code>count</code> response can only be used when detection function covariates
+only vary between segments/points (not within). For example, weather
+conditions (like visibility or sea state) or foliage cover are usually
+acceptable as they do not change within the segment, but animal sex or
+behaviour will not work. The <code>abundance.est</code> response can be used with any
+covariates in the detection function.</p>
+<p>In the density case, observations can be weighted by segment areas via the
+<code>weights=</code> argument. By default (<code>weights=NULL</code>), when density is estimated
+the weights are set to the segment areas (using <code>segment.area</code> or by
+calculated from detection function object metadata and <code>Effort</code> data).
+Alternatively <code>weights=1</code> will set the weights to all be equal. A third
+alternative is to pass in a vector of length equal to the number of
+segments, containing appropriate weights.</p>
+<p>A example analyses are available at <a href="http://examples.distancesampling.org" class="external-link">http://examples.distancesampling.org</a>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="units">Units<a class="anchor" aria-label="anchor" href="#units"></a></h2>
+    
+
+
+<p>It is often the case that distances are collected in metres and segment
+lengths are recorded in kilometres. <code>dsm</code> allows you to provide a conversion
+factor (<code>convert.units</code>) to multiply the areas by. For example: if distances
+are in metres and segment lengths are in kilometres setting
+<code>convert.units=1000</code> will lead to the analysis being in metres. Setting
+<code>convert.units=1/1000</code> will lead to the analysis being in kilometres. The
+conversion factor will be applied to <code>segment.area</code> if that is specified.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="large-models">Large models<a class="anchor" aria-label="anchor" href="#large-models"></a></h2>
+    
+
+
+<p>For large models, <code>engine="bam"</code> with <code>method="fREML"</code> may be useful. Models
+specified for <code>bam</code> should be as <code>gam</code>. Read <code><a href="https://rdrr.io/pkg/mgcv/man/bam.html" class="external-link">bam</a></code> before using
+this option; this option is considered EXPERIMENTAL at the moment. In
+particular note that the default basis choice (thin plate regression
+splines) will be slow and that in general fitting is less stable than when
+using <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>. For negative binomial response, theta must be
+specified when using <code><a href="https://rdrr.io/pkg/mgcv/man/bam.html" class="external-link">bam</a></code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hedley, S. and S. T. Buckland. 2004. Spatial models for line
+transect sampling. JABES 9:181-199.</p>
+<p>Miller, D. L., Burt, M. L., Rexstad, E. A., Thomas, L. (2013), Spatial
+models for distance sampling data: recent developments and future
+directions. Methods in Ecology and Evolution, 4: 1001-1010. doi:
+10.1111/2041-210X.12105 (Open Access)</p>
+<p>Wood, S.N. 2006. Generalized Additive Models: An Introduction with R.
+CRC/Chapman &amp; Hall.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># load the Gulf of Mexico dolphin data (see ?mexdolphins)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># fit a detection function and look at the summary</span></span></span>
+<span class="r-in"><span><span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
+<span class="r-in"><span>               key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">hr.model</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># fit a simple smooth of x and y to counts</span></span></span>
+<span class="r-in"><span><span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu">dsm</span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mod1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># predict over a grid</span></span></span>
+<span class="r-in"><span><span class="va">mod1.pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">mod1</span>, <span class="va">preddata</span>, <span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># calculate the predicted abundance over the grid</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">mod1.pred</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># plot the smooth</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mod1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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diff --git a/docs/reference/dsm.var.gam.html b/docs/reference/dsm.var.gam.html
new file mode 100644
index 0000000..a4550f8
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@@ -0,0 +1,125 @@
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+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Prediction variance estimation assuming independence</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
+      <div class="d-none name"><code>dsm.var.gam.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function is deprecated, use <a href="dsm_var_gam.html">dsm_var_gam</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm.var.gam</span><span class="op">(</span></span>
+<span>  <span class="va">dsm.obj</span>,</span>
+<span>  <span class="va">pred.data</span>,</span>
+<span>  <span class="va">off.set</span>,</span>
+<span>  seglen.varname <span class="op">=</span> <span class="st">"Effort"</span>,</span>
+<span>  type.pred <span class="op">=</span> <span class="st">"response"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.obj</dt>
+<dd><p>a model object fitted by <code><a href="dsm.html">dsm</a></code>.</p></dd>
+
+
+<dt>pred.data</dt>
+<dd><p>either: a single prediction grid or list of prediction
+grids. Each grid should be a <code>data.frame</code> with the same columns as the
+original data.</p></dd>
+
+
+<dt>off.set</dt>
+<dd><p>a a vector or list of vectors with as many elements as there
+are in <code>pred.data</code>. Each vector is as long as the number of rows in the
+corresponding element of <code>pred.data</code>. These give the area associated with
+each prediction cell. If a single number is supplied it will be replicated
+for the length of <code>pred.data</code>.</p></dd>
+
+
+<dt>seglen.varname</dt>
+<dd><p>name for the column which holds the segment length
+(default value <code>"Effort"</code>).</p></dd>
+
+
+<dt>type.pred</dt>
+<dd><p>should the predictions be on the "response" or "link"
+scale? (default <code>"response"</code>).</p></dd>
+
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
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+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/dsm.var.movblk.html b/docs/reference/dsm.var.movblk.html
new file mode 100644
index 0000000..73fd4f2
--- /dev/null
+++ b/docs/reference/dsm.var.movblk.html
@@ -0,0 +1,153 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use dsm_var_movblk."><title>Variance estimation via parametric moving block bootstrap — dsm.var.movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm.var.movblk"><meta property="og:description" content="This function is deprecated, use dsm_var_movblk."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Variance estimation via parametric moving block bootstrap</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
+      <div class="d-none name"><code>dsm.var.movblk.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function is deprecated, use <a href="dsm_var_movblk.html">dsm_var_movblk</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm.var.movblk</span><span class="op">(</span></span>
+<span>  <span class="va">dsm.object</span>,</span>
+<span>  <span class="va">pred.data</span>,</span>
+<span>  <span class="va">n.boot</span>,</span>
+<span>  <span class="va">block.size</span>,</span>
+<span>  <span class="va">off.set</span>,</span>
+<span>  ds.uncertainty <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  samp.unit.name <span class="op">=</span> <span class="st">"Transect.Label"</span>,</span>
+<span>  progress.file <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  bs.file <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  bar <span class="op">=</span> <span class="cn">TRUE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.object</dt>
+<dd><p>object returned from <code><a href="dsm.html">dsm</a></code>.</p></dd>
+
+
+<dt>pred.data</dt>
+<dd><p>either: a single prediction grid or list of prediction
+grids. Each grid should be a <code>data.frame</code> with the same columns as the
+original data.</p></dd>
+
+
+<dt>n.boot</dt>
+<dd><p>number of bootstrap resamples.</p></dd>
+
+
+<dt>block.size</dt>
+<dd><p>number of segments in each block.</p></dd>
+
+
+<dt>off.set</dt>
+<dd><p>a a vector or list of vectors with as many elements as there
+are in <code>pred.data</code>. Each vector is as long as the number of rows in the
+corresponding element of <code>pred.data</code>. These give the area associated with
+each prediction cell. If a single number is supplied it will be replicated
+for the length of <code>pred.data</code>.</p></dd>
+
+
+<dt>ds.uncertainty</dt>
+<dd><p>incorporate uncertainty in the detection function? See
+Details, below. Note that this feature is EXPERIMENTAL at the moment.</p></dd>
+
+
+<dt>samp.unit.name</dt>
+<dd><p>name sampling unit to resample (default
+'Transect.Label').</p></dd>
+
+
+<dt>progress.file</dt>
+<dd><p>path to a file to be used (usually by Distance) to
+generate a progress bar (default <code>NULL</code> -- no file written).</p></dd>
+
+
+<dt>bs.file</dt>
+<dd><p>path to a file to store each bootstrap round. This stores all
+of the bootstrap results rather than just the summaries, enabling
+outliers to be detected and removed. (Default <code>NULL</code>).</p></dd>
+
+
+<dt>bar</dt>
+<dd><p>should a progress bar be printed to screen? (Default <code>TRUE</code>).</p></dd>
+
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/dsm.var.prop.html b/docs/reference/dsm.var.prop.html
new file mode 100644
index 0000000..d991d80
--- /dev/null
+++ b/docs/reference/dsm.var.prop.html
@@ -0,0 +1,125 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use dsm_var_prop."><title>Prediction variance propagation for DSMs — dsm.var.prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm.var.prop"><meta property="og:description" content="This function is deprecated, use dsm_var_prop."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Prediction variance propagation for DSMs</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
+      <div class="d-none name"><code>dsm.var.prop.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function is deprecated, use <a href="dsm_var_prop.html">dsm_var_prop</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm.var.prop</span><span class="op">(</span></span>
+<span>  <span class="va">dsm.obj</span>,</span>
+<span>  <span class="va">pred.data</span>,</span>
+<span>  <span class="va">off.set</span>,</span>
+<span>  seglen.varname <span class="op">=</span> <span class="st">"Effort"</span>,</span>
+<span>  type.pred <span class="op">=</span> <span class="st">"response"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.obj</dt>
+<dd><p>a model object fitted by <code><a href="dsm.html">dsm</a></code>.</p></dd>
+
+
+<dt>pred.data</dt>
+<dd><p>either: a single prediction grid or list of prediction
+grids. Each grid should be a <code>data.frame</code> with the same columns as the
+original data.</p></dd>
+
+
+<dt>off.set</dt>
+<dd><p>a a vector or list of vectors with as many elements as there
+are in <code>pred.data</code>. Each vector is as long as the number of rows in the
+corresponding element of <code>pred.data</code>. These give the area associated with
+each prediction cell. If a single number is supplied it will be replicated
+for the length of <code>pred.data</code>.</p></dd>
+
+
+<dt>seglen.varname</dt>
+<dd><p>name for the column which holds the segment length
+(default value <code>"Effort"</code>).</p></dd>
+
+
+<dt>type.pred</dt>
+<dd><p>should the predictions be on the "response" or "link"
+scale? (default <code>"response"</code>).</p></dd>
+
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
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diff --git a/docs/reference/dsm_cor.html b/docs/reference/dsm_cor.html
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+<!DOCTYPE html>
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+autocorrelation in the residuals."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Check for autocorrelation in residuals</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_cor.R" class="external-link"><code>R/dsm_cor.R</code></a></small>
+      <div class="d-none name"><code>dsm_cor.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Once a DSM has been fitted to data, this function can be used to check for
+autocorrelation in the residuals.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm_cor</span><span class="op">(</span></span>
+<span>  <span class="va">dsm.obj</span>,</span>
+<span>  Transect.Label <span class="op">=</span> <span class="st">"Transect.Label"</span>,</span>
+<span>  Segment.Label <span class="op">=</span> <span class="st">"Segment.Label"</span>,</span>
+<span>  max.lag <span class="op">=</span> <span class="fl">10</span>,</span>
+<span>  resid.type <span class="op">=</span> <span class="st">"scaled.pearson"</span>,</span>
+<span>  fun <span class="op">=</span> <span class="va">cor</span>,</span>
+<span>  ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span>,</span>
+<span>  subset <span class="op">=</span> <span class="st">"all"</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.obj</dt>
+<dd><p>a fitted dsm object.</p></dd>
+
+
+<dt>Transect.Label</dt>
+<dd><p>label for the transect (default: <code>Transect.Label</code>).
+Using different labels can be useful when transects are split over
+geographical features or when transects are surveyed multiple times.</p></dd>
+
+
+<dt>Segment.Label</dt>
+<dd><p>label for the segments (default: <code>Segment.Label</code>).The
+result of calling <code><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></code> must make sense.</p></dd>
+
+
+<dt>max.lag</dt>
+<dd><p>maximum lag to calculate at.</p></dd>
+
+
+<dt>resid.type</dt>
+<dd><p>the type of residuals used, see
+<code><a href="https://rdrr.io/pkg/mgcv/man/residuals.gam.html" class="external-link">residuals.gam</a></code>. Defaults to <code>"scaled.pearson"</code> in the GAM
+case and <code>"normalized"</code> in the GAMM case (which are equivalent).</p></dd>
+
+
+<dt>fun</dt>
+<dd><p>the function to use, by default <code><a href="https://rdrr.io/r/stats/cor.html" class="external-link">cor</a></code>, must take two
+column vectors as arguments.</p></dd>
+
+
+<dt>ylim</dt>
+<dd><p>user defined limits in y direction.</p></dd>
+
+
+<dt>subset</dt>
+<dd><p>which subset of the data should the correlation function be
+calculated on?</p></dd>
+
+
+<dt>...</dt>
+<dd><p>other options to pass to <code><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></code>.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a plot or a vector of <code>fun</code> applied at the lags.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Within each <code>Transect.Label</code>, segments will be sorted
+according to their <code>Segment.Label</code>s. This may require some time to get right
+for your particular data. If one has multiple surveys where transects are
+revisited, for example, one may want to make <code>Transect.Label</code> a unique
+transect-survey identifier. Neither label need to be included in the model,
+they must just be present in the <code>$data</code> field in the model. This usually
+means that they have to be in the segment data passed to <code><a href="dsm.html">dsm</a></code>.</p>
+<p>The current iteration of this function will only plot correlations nicely,
+other things are up to you but you can get the function to return the data
+(by assigning the result to an object).</p>
+<p>If there are NA values in the residuals then the correlogram will not be
+calculated. This usually occurs due to <code>NA</code> values in the covariates (so the
+smoother will not have fitted values there). Code like
+<code>any(is.na(dsm.obj$data))</code> might be helpful.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \donttest{</span></span></span>
+<span class="r-in"><span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Attaching package: 'Distance'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> The following object is masked from 'package:dsm':</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>     dummy_ddf</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> The following object is masked from 'package:mrds':</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>     create.bins</span>
+<span class="r-in"><span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span> <span class="co"># load the data, see ?mexdolphins</span></span></span>
+<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span> <span class="co"># fit a model</span></span></span>
+<span class="r-in"><span> <span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
+<span class="r-in"><span>                key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Fitting hazard-rate key function</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> AIC= 677.418</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> No survey area information supplied, only estimating detection function.</span>
+<span class="r-in"><span> <span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Some observations are outside of detection function 1 truncation!</span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span> <span class="co"># look at lag 1 differences up to a maximum of lag 9, using deviance</span></span></span>
+<span class="r-in"><span> <span class="co"># residuals</span></span></span>
+<span class="r-in"><span> <span class="fu">dsm_cor</span><span class="op">(</span><span class="va">mod1</span>, resid.type<span class="op">=</span><span class="st">"deviance"</span>, max.lag<span class="op">=</span><span class="fl">9</span>,</span></span>
+<span class="r-in"><span>         Segment.Label<span class="op">=</span><span class="st">"Sample.Label"</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="dsm_cor-1.png" alt="" width="700" height="433"></span>
+<span class="r-in"><span><span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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diff --git a/docs/reference/dsm_var_gam.html b/docs/reference/dsm_var_gam.html
new file mode 100644
index 0000000..ea72169
--- /dev/null
+++ b/docs/reference/dsm_var_gam.html
@@ -0,0 +1,171 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="If one is willing to assume the the detection function and spatial model are
+independent, this function will produce estimates of variance of predictions
+of abundance, using the result that squared coefficients of variation will
+add."><title>Prediction variance estimation assuming independence — dsm_var_gam • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance estimation assuming independence — dsm_var_gam"><meta property="og:description" content="If one is willing to assume the the detection function and spatial model are
+independent, this function will produce estimates of variance of predictions
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+add."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
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+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Prediction variance estimation assuming independence</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_var_gam.R" class="external-link"><code>R/dsm_var_gam.R</code></a></small>
+      <div class="d-none name"><code>dsm_var_gam.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>If one is willing to assume the the detection function and spatial model are
+independent, this function will produce estimates of variance of predictions
+of abundance, using the result that squared coefficients of variation will
+add.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm_var_gam</span><span class="op">(</span></span>
+<span>  <span class="va">dsm.obj</span>,</span>
+<span>  <span class="va">pred.data</span>,</span>
+<span>  <span class="va">off.set</span>,</span>
+<span>  seglen.varname <span class="op">=</span> <span class="st">"Effort"</span>,</span>
+<span>  type.pred <span class="op">=</span> <span class="st">"response"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.obj</dt>
+<dd><p>a model object fitted by <code><a href="dsm.html">dsm</a></code>.</p></dd>
+
+
+<dt>pred.data</dt>
+<dd><p>either: a single prediction grid or list of prediction
+grids. Each grid should be a <code>data.frame</code> with the same columns as the
+original data.</p></dd>
+
+
+<dt>off.set</dt>
+<dd><p>a a vector or list of vectors with as many elements as there
+are in <code>pred.data</code>. Each vector is as long as the number of rows in the
+corresponding element of <code>pred.data</code>. These give the area associated with
+each prediction cell. If a single number is supplied it will be replicated
+for the length of <code>pred.data</code>.</p></dd>
+
+
+<dt>seglen.varname</dt>
+<dd><p>name for the column which holds the segment length
+(default value <code>"Effort"</code>).</p></dd>
+
+
+<dt>type.pred</dt>
+<dd><p>should the predictions be on the "response" or "link"
+scale? (default <code>"response"</code>).</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a <code>list</code> with elements</p><ul><li><p><code>model</code> the fitted model object</p></li>
+<li><p><code>pred.var</code> variance of the regions given in <code>pred.data</code>.</p></li>
+<li><p><code>bootstrap</code> logical, always <code>FALSE</code></p></li>
+<li><p><code>model</code> the fitted model with the extra term</p></li>
+<li><p><code>dsm.object</code> the original model (<code>dsm.obj</code> above)</p></li>
+</ul></div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span> <span class="co"># load the Gulf of Mexico dolphin data (see ?mexdolphins)</span></span></span>
+<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span> <span class="co"># fit a detection function and look at the summary</span></span></span>
+<span class="r-in"><span> <span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
+<span class="r-in"><span>                key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">hr.model</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span> <span class="co"># fit a simple smooth of x and y</span></span></span>
+<span class="r-in"><span> <span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span> <span class="co"># Calculate the variance</span></span></span>
+<span class="r-in"><span> <span class="co"># this will give a summary over the whole area in mexdolphins$preddata</span></span></span>
+<span class="r-in"><span> <span class="va">mod1.var</span> <span class="op">&lt;-</span> <span class="fu">dsm_var_gam</span><span class="op">(</span><span class="va">mod1</span>, <span class="va">preddata</span>, off.set<span class="op">=</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
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+
+  
+
+  </body></html>
+
diff --git a/docs/reference/dsm_var_movblk.html b/docs/reference/dsm_var_movblk.html
new file mode 100644
index 0000000..11cd000
--- /dev/null
+++ b/docs/reference/dsm_var_movblk.html
@@ -0,0 +1,202 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Estimate the variance in abundance over an area using a moving block
+bootstrap. Two procedures are implemented, one incorporating detection
+function uncertainty, one not."><title>Variance estimation via parametric moving block bootstrap — dsm_var_movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm_var_movblk"><meta property="og:description" content="Estimate the variance in abundance over an area using a moving block
+bootstrap. Two procedures are implemented, one incorporating detection
+function uncertainty, one not."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+
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+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Variance estimation via parametric moving block bootstrap</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_var_movblk.R" class="external-link"><code>R/dsm_var_movblk.R</code></a></small>
+      <div class="d-none name"><code>dsm_var_movblk.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Estimate the variance in abundance over an area using a moving block
+bootstrap. Two procedures are implemented, one incorporating detection
+function uncertainty, one not.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm_var_movblk</span><span class="op">(</span></span>
+<span>  <span class="va">dsm.object</span>,</span>
+<span>  <span class="va">pred.data</span>,</span>
+<span>  <span class="va">n.boot</span>,</span>
+<span>  <span class="va">block.size</span>,</span>
+<span>  <span class="va">off.set</span>,</span>
+<span>  ds.uncertainty <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  samp.unit.name <span class="op">=</span> <span class="st">"Transect.Label"</span>,</span>
+<span>  progress.file <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  bs.file <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  bar <span class="op">=</span> <span class="cn">TRUE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.object</dt>
+<dd><p>object returned from <code><a href="dsm.html">dsm</a></code>.</p></dd>
+
+
+<dt>pred.data</dt>
+<dd><p>either: a single prediction grid or list of prediction
+grids. Each grid should be a <code>data.frame</code> with the same columns as the
+original data.</p></dd>
+
+
+<dt>n.boot</dt>
+<dd><p>number of bootstrap resamples.</p></dd>
+
+
+<dt>block.size</dt>
+<dd><p>number of segments in each block.</p></dd>
+
+
+<dt>off.set</dt>
+<dd><p>a a vector or list of vectors with as many elements as there
+are in <code>pred.data</code>. Each vector is as long as the number of rows in the
+corresponding element of <code>pred.data</code>. These give the area associated with
+each prediction cell. If a single number is supplied it will be replicated
+for the length of <code>pred.data</code>.</p></dd>
+
+
+<dt>ds.uncertainty</dt>
+<dd><p>incorporate uncertainty in the detection function? See
+Details, below. Note that this feature is EXPERIMENTAL at the moment.</p></dd>
+
+
+<dt>samp.unit.name</dt>
+<dd><p>name sampling unit to resample (default
+'Transect.Label').</p></dd>
+
+
+<dt>progress.file</dt>
+<dd><p>path to a file to be used (usually by Distance) to
+generate a progress bar (default <code>NULL</code> -- no file written).</p></dd>
+
+
+<dt>bs.file</dt>
+<dd><p>path to a file to store each bootstrap round. This stores all
+of the bootstrap results rather than just the summaries, enabling
+outliers to be detected and removed. (Default <code>NULL</code>).</p></dd>
+
+
+<dt>bar</dt>
+<dd><p>should a progress bar be printed to screen? (Default <code>TRUE</code>).</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    
+
+<p>Setting <code>ds.uncertainty=TRUE</code> will incorporate detection function
+uncertainty directly into the bootstrap. This is done by generating
+observations from the fitted detection function and then re-fitting a new
+detection function (of the same form), then calculating a new effective
+strip width. Rejection sampling is used to generate the observations
+(except in the half-normal case) so the procedure can be rather slow. Note
+that this is currently not supported with covariates in the detection
+function.</p>
+<p>Setting <code>ds.uncertainty=FALSE</code> will incorporate detection function
+uncertainty using the delta method. This assumes that the detection
+function and the spatial model are INDEPENDENT. This is probably not
+reasonable.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># load the Gulf of Mexico dolphin data (see ?mexdolphins)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># fit a detection function and look at the summary</span></span></span>
+<span class="r-in"><span><span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
+<span class="r-in"><span>               key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">hr.model</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># fit a simple smooth of x and y</span></span></span>
+<span class="r-in"><span><span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mod1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># calculate the variance by 500 moving block bootstraps</span></span></span>
+<span class="r-in"><span><span class="va">mod1.movblk</span> <span class="op">&lt;-</span> <span class="fu">dsm_var_movblk</span><span class="op">(</span><span class="va">mod1</span>, <span class="va">preddata</span>, n.boot <span class="op">=</span> <span class="fl">500</span>,</span></span>
+<span class="r-in"><span>   block.size <span class="op">=</span> <span class="fl">3</span>, samp.unit.name <span class="op">=</span> <span class="st">"Transect.Label"</span>,</span></span>
+<span class="r-in"><span>   off.set <span class="op">=</span> <span class="va">preddata</span><span class="op">$</span><span class="va">area</span>,</span></span>
+<span class="r-in"><span>   bar <span class="op">=</span> <span class="cn">TRUE</span>, bs.file <span class="op">=</span> <span class="st">"mexico-bs.csv"</span>, ds.uncertainty <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
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diff --git a/docs/reference/dsm_var_prop.html b/docs/reference/dsm_var_prop.html
new file mode 100644
index 0000000..ca41079
--- /dev/null
+++ b/docs/reference/dsm_var_prop.html
@@ -0,0 +1,206 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="To ensure that uncertainty from the detection function is correctly
+propagated to the final variance estimate of abundance, this function uses a
+method first detailed in Williams et al (2011), further explanation is given
+in Bravington et al. (2021)."><title>Prediction variance propagation for DSMs — dsm_var_prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm_var_prop"><meta property="og:description" content="To ensure that uncertainty from the detection function is correctly
+propagated to the final variance estimate of abundance, this function uses a
+method first detailed in Williams et al (2011), further explanation is given
+in Bravington et al. (2021)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Prediction variance propagation for DSMs</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_var_prop.R" class="external-link"><code>R/dsm_var_prop.R</code></a></small>
+      <div class="d-none name"><code>dsm_var_prop.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>To ensure that uncertainty from the detection function is correctly
+propagated to the final variance estimate of abundance, this function uses a
+method first detailed in Williams et al (2011), further explanation is given
+in Bravington et al. (2021).</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm_var_prop</span><span class="op">(</span></span>
+<span>  <span class="va">dsm.obj</span>,</span>
+<span>  <span class="va">pred.data</span>,</span>
+<span>  <span class="va">off.set</span>,</span>
+<span>  seglen.varname <span class="op">=</span> <span class="st">"Effort"</span>,</span>
+<span>  type.pred <span class="op">=</span> <span class="st">"response"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.obj</dt>
+<dd><p>a model object fitted by <code><a href="dsm.html">dsm</a></code>.</p></dd>
+
+
+<dt>pred.data</dt>
+<dd><p>either: a single prediction grid or list of prediction
+grids. Each grid should be a <code>data.frame</code> with the same columns as the
+original data.</p></dd>
+
+
+<dt>off.set</dt>
+<dd><p>a a vector or list of vectors with as many elements as there
+are in <code>pred.data</code>. Each vector is as long as the number of rows in the
+corresponding element of <code>pred.data</code>. These give the area associated with
+each prediction cell. If a single number is supplied it will be replicated
+for the length of <code>pred.data</code>.</p></dd>
+
+
+<dt>seglen.varname</dt>
+<dd><p>name for the column which holds the segment length
+(default value <code>"Effort"</code>).</p></dd>
+
+
+<dt>type.pred</dt>
+<dd><p>should the predictions be on the "response" or "link"
+scale? (default <code>"response"</code>).</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a <code>list</code> with elements</p><ul><li><p><code>model</code> the fitted model object</p></li>
+<li><p><code>pred.var</code> variance of each region given in <code>pred.data</code></p></li>
+<li><p><code>bootstrap</code> logical, always <code>FALSE</code></p></li>
+<li><p><code>pred.data</code> as above</p></li>
+<li><p><code>off.set</code> as above</p></li>
+<li><p><code>model</code> the fitted model with the extra term</p></li>
+<li><p><code>dsm.object</code> the original model, as above</p></li>
+<li><p><code>model.check</code> simple check of subtracting the coefficients of the two
+models to see if there is a large difference</p></li>
+<li><p><code>deriv</code> numerically calculated Hessian of the offset</p></li>
+</ul></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>The idea is to refit the spatial model but including an extra random effect.
+This random effect has zero mean and hence to effect on point estimates. Its
+variance is the Hessian of the detection function. Variance estimates then
+incorporate detection function uncertainty. Further mathematical details are
+given in the paper in the references below.</p>
+<p>Many prediction grids can be supplied by supplying a list of <code>data.frame</code>s
+to the function.</p>
+<p>Note that this routine simply calls <code><a href="dsm_varprop.html">dsm_varprop</a></code>. If you
+don't require multiple prediction grids, the other routine will probably be
+faster.</p>
+<p>This routine is only useful if a detection function with covariates has been
+used in the DSM.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="diagnostics">Diagnostics<a class="anchor" aria-label="anchor" href="#diagnostics"></a></h2>
+    
+
+<p>The summary output from the function includes a simply diagnostic that shows
+the average probability of detection from the "original" fitted model (the
+model supplied to this function; column <code>Fitted.model</code>) and the probability
+of detection from the refitted model (used for variance propagation; column
+<code>Refitted.model</code>) along with the standard error of the probability of
+detection from the fitted model (<code>Fitted.model.se</code>), at the unique values of
+any factor covariates used in the detection function (for continuous
+covariates the 5%, 50% and 95% quantiles are shown). If there are large
+differences between the probabilities of detection then there are
+potentially problems with the fitted model, the variance propagation or
+both. This can be because the fitted model does not account for enough of
+the variability in the data and in refitting the variance model accounts for
+this in the random effect.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a></h2>
+    
+
+<p>Note that this routine is only useful if a detection function has been used
+in the DSM. It cannot be used when the <code>abundance.est</code> or <code>density.est</code>
+responses are used. Importantly this requires that if the detection function
+has covariates, then these do not vary within a segment (so, for example
+covariates like sex cannot be used).</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Bravington, M. V., Miller, D. L., &amp; Hedley, S. L. (2021). Variance
+Propagation for Density Surface Models. Journal of Agricultural, Biological
+and Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2</p>
+<p>Williams, R., Hedley, S.L., Branch, T.A., Bravington, M.V., Zerbini, A.N.
+and Findlay, K.P. (2011). Chilean Blue Whales as a Case Study to Illustrate
+Methods to Estimate Abundance and Evaluate Conservation Status of Rare
+Species. Conservation Biology 25(3), 526-535.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>Mark V. Bravington, Sharon L. Hedley. Bugs added by David L. Miller.</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
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+    </footer></div>
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diff --git a/docs/reference/dsm_varprop.html b/docs/reference/dsm_varprop.html
new file mode 100644
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+<!DOCTYPE html>
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+uncertainty from the detection function."><title>Variance propagation for density surface models — dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance propagation for density surface models — dsm_varprop"><meta property="og:description" content="Calculate the uncertainty in predictions from a fitted DSM, including
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Variance propagation for density surface models</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_varprop.R" class="external-link"><code>R/dsm_varprop.R</code></a></small>
+      <div class="d-none name"><code>dsm_varprop.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Calculate the uncertainty in predictions from a fitted DSM, including
+uncertainty from the detection function.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dsm_varprop</span><span class="op">(</span></span>
+<span>  <span class="va">model</span>,</span>
+<span>  newdata <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  trace <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  var.type <span class="op">=</span> <span class="st">"Vp"</span>,</span>
+<span>  var_type <span class="op">=</span> <span class="cn">NULL</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>model</dt>
+<dd><p>a fitted <code><a href="dsm.html">dsm</a></code>.</p></dd>
+
+
+<dt>newdata</dt>
+<dd><p>the prediction grid. Set to <code>NULL</code> to avoid making
+predictions and just return model objects.</p></dd>
+
+
+<dt>trace</dt>
+<dd><p>for debugging, see how the scale parameter estimation is going.</p></dd>
+
+
+<dt>var.type</dt>
+<dd><p>which variance-covariance matrix should be used (<code>"Vp"</code> for
+variance-covariance conditional on smoothing parameter(s), <code>"Vc"</code> for
+unconditional). See <code><a href="https://rdrr.io/pkg/mgcv/man/gamObject.html" class="external-link">gamObject</a></code> for an details/explanation. If
+in doubt, stick with the default, <code>"Vp"</code>.</p></dd>
+
+
+<dt>var_type</dt>
+<dd><p>deprecated, use <code>var.type</code> instead.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a <code>list</code> with elements:</p><ul><li><p><code>old_model</code> fitted model supplied to the function as <code>model</code></p></li>
+<li><p><code>refit</code> refitted model object, with extra term</p></li>
+<li><p><code>pred</code> point estimates of predictions at <code>newdata</code></p></li>
+<li><p><code>var</code> total variance calculated over all of <code>newdata</code></p></li>
+<li><p><code>ses</code> standard error for each prediction cell in <code>newdata</code>
+if <code>newdata=NULL</code> then the last three entries are <code>NA</code>.</p></li>
+</ul></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>When we make predictions from a spatial model, we also want to know the
+uncertainty about that abundance estimate. Since density surface models are
+2 (or more) stage models, we need to incorporate the uncertainty from the
+earlier stages (i.e. the detection function) into our "final" uncertainty
+estimate.</p>
+<p>This function will refit the spatial model but include the Hessian of the
+offset as an extra term. Variance estimates using this new model can then be
+used to calculate the variance of predicted abundance estimates which
+incorporate detection function uncertainty. Importantly this requires that
+if the detection function has covariates, then these do not vary within a
+segment (so, for example covariates like sex cannot be used).</p>
+<p>For more information on how to construct the prediction grid <code>data.frame</code>,
+<code>newdata</code>, see <code><a href="predict.dsm.html">predict.dsm</a></code>.</p>
+<p>This routine is only useful if a detection function with covariates has been
+used in the DSM.</p>
+<p>Note that we can use <code>var.type="Vc"</code> here (see <code>gamObject</code>), which is the
+variance-covariance matrix for the spatial model, corrected for smoothing
+parameter uncertainty. See Wood, Pya &amp; S\"afken (2016) for more
+information.</p>
+<p>Models with fixed scale parameters (e.g., negative binomial) do not require
+an extra round of optimisation.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="diagnostics">Diagnostics<a class="anchor" aria-label="anchor" href="#diagnostics"></a></h2>
+    
+
+<p>The summary output from the function includes a simply diagnostic that shows
+the average probability of detection from the "original" fitted model (the
+model supplied to this function; column <code>Fitted.model</code>) and the probability
+of detection from the refitted model (used for variance propagation; column
+<code>Refitted.model</code>) along with the standard error of the probability of
+detection from the fitted model (<code>Fitted.model.se</code>), at the unique values of
+any factor covariates used in the detection function (for continuous
+covariates the 5%, 50% and 95% quantiles are shown). If there are large
+differences between the probabilities of detection then there are
+potentially problems with the fitted model, the variance propagation or
+both. This can be because the fitted model does not account for enough of
+the variability in the data and in refitting the variance model accounts for
+this in the random effect.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Bravington, M. V., Miller, D. L., &amp; Hedley, S. L. (2021). Variance
+Propagation for Density Surface Models. Journal of Agricultural, Biological
+and Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2</p>
+<p>Williams, R., Hedley, S.L., Branch, T.A., Bravington, M.V., Zerbini, A.N.
+and Findlay, K.P. (2011). Chilean Blue Whales as a Case Study to Illustrate
+Methods to Estimate Abundance and Evaluate Conservation Status of Rare
+Species. Conservation Biology 25(3), 526-535.</p>
+<p>Wood, S.N., Pya, N. and S\"afken, B. (2016) Smoothing parameter and model
+selection for general smooth models. Journal of the American Statistical
+Association, 1-45.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller, based on code from Mark V. Bravington and Sharon L.
+Hedley.</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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diff --git a/docs/reference/dummy_ddf.html b/docs/reference/dummy_ddf.html
new file mode 100644
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--- /dev/null
+++ b/docs/reference/dummy_ddf.html
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Create a detection function object for strip/plot surveys for use in density
+surface models."><title>Detection function objects when detection is certain — dummy_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Detection function objects when detection is certain — dummy_ddf"><meta property="og:description" content="Create a detection function object for strip/plot surveys for use in density
+surface models."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Detection function objects when detection is certain</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dummy_ddf.R" class="external-link"><code>R/dummy_ddf.R</code></a></small>
+      <div class="d-none name"><code>dummy_ddf.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Create a detection function object for strip/plot surveys for use in density
+surface models.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dummy_ddf</span><span class="op">(</span><span class="va">object</span>, size <span class="op">=</span> <span class="fl">1</span>, <span class="va">width</span>, left <span class="op">=</span> <span class="fl">0</span>, transect <span class="op">=</span> <span class="st">"line"</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>object</dt>
+<dd><p>numeric vector of object identifiers, relating to the <code>object</code>
+field in the observation data of the DSM.</p></dd>
+
+
+<dt>size</dt>
+<dd><p>group size for each observation (default all groups size 1)</p></dd>
+
+
+<dt>width</dt>
+<dd><p>right truncation</p></dd>
+
+
+<dt>left</dt>
+<dd><p>left truncation (default 0, no left truncation)</p></dd>
+
+
+<dt>transect</dt>
+<dd><p><code>"line"</code> or <code>"point"</code> transect</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/generate.ds.uncertainty.html b/docs/reference/generate.ds.uncertainty.html
new file mode 100644
index 0000000..4dd0005
--- /dev/null
+++ b/docs/reference/generate.ds.uncertainty.html
@@ -0,0 +1,112 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
+procedure generates data from the fitted detection function (assuming that
+it is correct)."><title>Generate data from a fitted detection function — generate.ds.uncertainty • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generate data from a fitted detection function — generate.ds.uncertainty"><meta property="og:description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
+procedure generates data from the fitted detection function (assuming that
+it is correct)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Generate data from a fitted detection function</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/generate.ds.uncertainty.R" class="external-link"><code>R/generate.ds.uncertainty.R</code></a></small>
+      <div class="d-none name"><code>generate.ds.uncertainty.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>When using <code><a href="dsm.var.movblk.html">dsm.var.movblk</a></code> if <code>ds.uncertainty=TRUE</code>, this
+procedure generates data from the fitted detection function (assuming that
+it is correct).</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">generate.ds.uncertainty</span><span class="op">(</span><span class="va">ds.object</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>ds.object</dt>
+<dd><p>a fitted detection function object (as returned by a call
+to <code><a href="https://rdrr.io/pkg/mrds/man/ddf.ds.html" class="external-link">ddf.ds</a></code>).</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>This function changes the random number generator seed. To avoid any
+potential side-effects, use something like: <code>seed &lt;- get(".Random.seed",envir=.GlobalEnv)</code> before running code and
+<code>assign(".Random.seed",seed,envir=.GlobalEnv)</code> after.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/generate.mb.sample.html b/docs/reference/generate.mb.sample.html
new file mode 100644
index 0000000..5c6d327
--- /dev/null
+++ b/docs/reference/generate.mb.sample.html
@@ -0,0 +1,131 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Not usually used on its own, called from within
+dsm.var.movblk."><title>Moving block bootstrap sampler — generate.mb.sample • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Moving block bootstrap sampler — generate.mb.sample"><meta property="og:description" content="Not usually used on its own, called from within
+dsm.var.movblk."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Moving block bootstrap sampler</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/generate.mb.sample.R" class="external-link"><code>R/generate.mb.sample.R</code></a></small>
+      <div class="d-none name"><code>generate.mb.sample.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Not usually used on its own, called from within
+<code><a href="dsm.var.movblk.html">dsm.var.movblk</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">generate.mb.sample</span><span class="op">(</span></span>
+<span>  <span class="va">num.blocks.required</span>,</span>
+<span>  <span class="va">block.size</span>,</span>
+<span>  <span class="va">which.blocks</span>,</span>
+<span>  <span class="va">dsm.data</span>,</span>
+<span>  <span class="va">unit.info</span>,</span>
+<span>  <span class="va">n.units</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>num.blocks.required</dt>
+<dd><p>number of blocks that we need.</p></dd>
+
+
+<dt>block.size</dt>
+<dd><p>number of segments per block.</p></dd>
+
+
+<dt>which.blocks</dt>
+<dd><p>which blocks should be sampled.</p></dd>
+
+
+<dt>dsm.data</dt>
+<dd><p>the <code>$data</code> element of the result of a call to <code><a href="dsm.html">dsm</a></code>.</p></dd>
+
+
+<dt>unit.info</dt>
+<dd><p>result of calling <code><a href="block.info.per.su.html">block.info.per.su</a></code>.</p></dd>
+
+
+<dt>n.units</dt>
+<dd><p>number of sampling units.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>vector of log-residuals</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/index.html b/docs/reference/index.html
new file mode 100644
index 0000000..1a94dd1
--- /dev/null
+++ b/docs/reference/index.html
@@ -0,0 +1,283 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function reference • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function reference"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-index">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Function reference</h1>
+    </div>
+
+    <div class="section level2">
+      <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="#all-functions"></a></h2>
+      
+      
+
+      
+    </div><div id="" class="section level2">
+      
+      
+      
+
+      <dl><dt>
+          
+          <code><a href="block.info.per.su.html">block.info.per.su()</a></code> 
+        </dt>
+        <dd>Find the block information</dd>
+      </dl><dl><dt>
+          
+          <code><a href="check.cols.html">check.cols()</a></code> 
+        </dt>
+        <dd>Check column names exist</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm-data.html">dsm-data</a></code> 
+        </dt>
+        <dd>Data format for DSM</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm-package.html">dsm-package</a></code> 
+        </dt>
+        <dd>Density surface modelling</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm.cor.html">dsm.cor()</a></code> 
+        </dt>
+        <dd>Check for autocorrelation in residuals</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm.html">dsm()</a></code> 
+        </dt>
+        <dd>Fit a density surface model to segment-specific estimates of abundance or density.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm.var.gam.html">dsm.var.gam()</a></code> 
+        </dt>
+        <dd>Prediction variance estimation assuming independence</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm.var.movblk.html">dsm.var.movblk()</a></code> 
+        </dt>
+        <dd>Variance estimation via parametric moving block bootstrap</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm.var.prop.html">dsm.var.prop()</a></code> 
+        </dt>
+        <dd>Prediction variance propagation for DSMs</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm_cor.html">dsm_cor()</a></code> 
+        </dt>
+        <dd>Check for autocorrelation in residuals</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm_varprop.html">dsm_varprop()</a></code> 
+        </dt>
+        <dd>Variance propagation for density surface models</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm_var_gam.html">dsm_var_gam()</a></code> 
+        </dt>
+        <dd>Prediction variance estimation assuming independence</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm_var_movblk.html">dsm_var_movblk()</a></code> 
+        </dt>
+        <dd>Variance estimation via parametric moving block bootstrap</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dsm_var_prop.html">dsm_var_prop()</a></code> 
+        </dt>
+        <dd>Prediction variance propagation for DSMs</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dummy_ddf.html">dummy_ddf()</a></code> 
+        </dt>
+        <dd>Detection function objects when detection is certain</dd>
+      </dl><dl><dt>
+          
+          <code><a href="generate.ds.uncertainty.html">generate.ds.uncertainty()</a></code> 
+        </dt>
+        <dd>Generate data from a fitted detection function</dd>
+      </dl><dl><dt>
+          
+          <code><a href="generate.mb.sample.html">generate.mb.sample()</a></code> 
+        </dt>
+        <dd>Moving block bootstrap sampler</dd>
+      </dl><dl><dt>
+          
+          <code><a href="latlong2km.html">latlong2km()</a></code> 
+        </dt>
+        <dd>Convert latitude and longitude to Northings and Eastings</dd>
+      </dl><dl><dt>
+          
+          <code><a href="make.soapgrid.html">make.soapgrid()</a></code> 
+        </dt>
+        <dd>Create a knot grid for the internal part of a soap film smoother.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="mexdolphins.html">mexdolphins</a></code> <code><a href="mexdolphins.html">obsdata</a></code> <code><a href="mexdolphins.html">segdata</a></code> <code><a href="mexdolphins.html">preddata</a></code> <code><a href="mexdolphins.html">pred.polys</a></code> <code><a href="mexdolphins.html">distdata</a></code> <code><a href="mexdolphins.html">survey.area</a></code> 
+        </dt>
+        <dd>Pan-tropical spotted dolphins in the Gulf of Mexico</dd>
+      </dl><dl><dt>
+          
+          <code><a href="obs_exp.html">obs_exp()</a></code> 
+        </dt>
+        <dd>Observed versus expected diagnostics for fitted DSMs</dd>
+      </dl><dl><dt>
+          
+          <code><a href="plot.dsm.html">plot(<i>&lt;dsm&gt;</i>)</a></code> 
+        </dt>
+        <dd>Plot a density surface model.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="plot.dsm.var.html">plot(<i>&lt;dsm.var&gt;</i>)</a></code> 
+        </dt>
+        <dd>Create plots of abundance uncertainty</dd>
+      </dl><dl><dt>
+          
+          <code><a href="plot_pred_by_term.html">plot_pred_by_term()</a></code> 
+        </dt>
+        <dd>Spatially plot predictions per model term</dd>
+      </dl><dl><dt>
+          
+          <code><a href="predict.dsm.html">predict(<i>&lt;dsm&gt;</i>)</a></code> 
+        </dt>
+        <dd>Predict from a fitted density surface model</dd>
+      </dl><dl><dt>
+          
+          <code><a href="predict.fake_ddf.html">predict(<i>&lt;fake_ddf&gt;</i>)</a></code> 
+        </dt>
+        <dd>Prediction for fake detection functions</dd>
+      </dl><dl><dt>
+          
+          <code><a href="print.dsm.html">print(<i>&lt;dsm&gt;</i>)</a></code> 
+        </dt>
+        <dd>Print a description of a density surface model object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="print.dsm.var.html">print(<i>&lt;dsm.var&gt;</i>)</a></code> 
+        </dt>
+        <dd>Print a description of a density surface model variance object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="print.dsm_varprop.html">print(<i>&lt;dsm_varprop&gt;</i>)</a></code> 
+        </dt>
+        <dd>Print a description of a density surface model variance object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="print.summary.dsm.var.html">print(<i>&lt;summary.dsm.var&gt;</i>)</a></code> 
+        </dt>
+        <dd>Print summary of density surface model variance object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="print.summary.dsm_varprop.html">print(<i>&lt;summary.dsm_varprop&gt;</i>)</a></code> 
+        </dt>
+        <dd>Print summary of density surface model variance object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="rqgam.check.html">rqgam.check()</a></code> 
+        </dt>
+        <dd>Randomised quantile residuals check plot for GAMs/DSMs</dd>
+      </dl><dl><dt>
+          
+          <code><a href="rqgam_check.html">rqgam_check()</a></code> 
+        </dt>
+        <dd>Randomised quantile residuals check plot for GAMs/DSMs</dd>
+      </dl><dl><dt>
+          
+          <code><a href="summary.dsm.html">summary(<i>&lt;dsm&gt;</i>)</a></code> 
+        </dt>
+        <dd>Summarize a fitted density surface model</dd>
+      </dl><dl><dt>
+          
+          <code><a href="summary.dsm.var.html">summary(<i>&lt;dsm.var&gt;</i>)</a></code> 
+        </dt>
+        <dd>Summarize the variance of a density surface model</dd>
+      </dl><dl><dt>
+          
+          <code><a href="summary.dsm_varprop.html">summary(<i>&lt;dsm_varprop&gt;</i>)</a></code> 
+        </dt>
+        <dd>Summarize the variance of a density surface model</dd>
+      </dl><dl><dt>
+          
+          <code><a href="trim.var.html">trim.var()</a></code> 
+        </dt>
+        <dd>Trimmed variance</dd>
+      </dl><dl><dt>
+          
+          <code><a href="vis.concurvity.html">vis.concurvity()</a></code> 
+        </dt>
+        <dd>Visualise concurvity between terms in a GAM</dd>
+      </dl><dl><dt>
+          
+          <code><a href="vis_concurvity.html">vis_concurvity()</a></code> 
+        </dt>
+        <dd>Visualise concurvity between terms in a GAM</dd>
+      </dl></div>
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/latlong2km.html b/docs/reference/latlong2km.html
new file mode 100644
index 0000000..58908ac
--- /dev/null
+++ b/docs/reference/latlong2km.html
@@ -0,0 +1,129 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
+south-north from axes through (lon0,lat0) using the
+"spherical law of cosines".'><title>Convert latitude and longitude to Northings and Eastings — latlong2km • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert latitude and longitude to Northings and Eastings — latlong2km"><meta property="og:description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
+south-north from axes through (lon0,lat0) using the
+"spherical law of cosines".'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
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+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Convert latitude and longitude to Northings and Eastings</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/latlong2km.R" class="external-link"><code>R/latlong2km.R</code></a></small>
+      <div class="d-none name"><code>latlong2km.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Convert longitude and latitude co-ordinates to kilometres west-east and
+south-north from axes through (<code>lon0</code>,<code>lat0</code>) using the
+"spherical law of cosines".</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">latlong2km</span><span class="op">(</span><span class="va">lon</span>, <span class="va">lat</span>, lon0 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">lon</span><span class="op">)</span><span class="op">)</span><span class="op">/</span><span class="fl">2</span>, lat0 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">lat</span><span class="op">)</span><span class="op">)</span><span class="op">/</span><span class="fl">2</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>lon</dt>
+<dd><p>longitude</p></dd>
+
+
+<dt>lat</dt>
+<dd><p>latitude</p></dd>
+
+
+<dt>lon0</dt>
+<dd><p>longitude reference point (defaults to mean longitude)</p></dd>
+
+
+<dt>lat0</dt>
+<dd><p>latitude reference point (defaults to mean latitude)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>list with elements <code>km.e</code> and <code>km.n</code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>WARNING: This is an approximate procedure for converting between latitude/
+longitude and Northing/Easting. Consider using projection conversions
+available in packages <code>sp</code>, <code>sf</code> and <code>rgdal</code> for better results.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>Simon N. Wood</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/make.soapgrid.html b/docs/reference/make.soapgrid.html
new file mode 100644
index 0000000..a0758e9
--- /dev/null
+++ b/docs/reference/make.soapgrid.html
@@ -0,0 +1,115 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='This routine simply creates a grid of knots (in the correct format) to be
+used as in the "internal" part of the soap film smoother'><title>Create a knot grid for the internal part of a soap film smoother. — make.soapgrid • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a knot grid for the internal part of a soap film smoother. — make.soapgrid"><meta property="og:description" content='This routine simply creates a grid of knots (in the correct format) to be
+used as in the "internal" part of the soap film smoother'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
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+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Create a knot grid for the internal part of a soap film smoother.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/make.soapgrid.R" class="external-link"><code>R/make.soapgrid.R</code></a></small>
+      <div class="d-none name"><code>make.soapgrid.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This routine simply creates a grid of knots (in the correct format) to be
+used as in the "internal" part of the soap film smoother</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">make.soapgrid</span><span class="op">(</span><span class="va">bnd</span>, <span class="va">n.grid</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>bnd</dt>
+<dd><p><code>list</code> with elements <code>x</code> and <code>y</code> which give the locations of the
+boundary vertices. The first and last elements should be the same.</p></dd>
+
+
+<dt>n.grid</dt>
+<dd><p>either one number giving the number of points along the <code>x</code>
+and <code>y</code> axes that should be used to create the grid, or a vector giving the
+number in the <code>x</code> direction, then <code>y</code> direction.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a list with elements <code>x</code> and <code>y</code>, containing the knot locations.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/mexdolphins.html b/docs/reference/mexdolphins.html
new file mode 100644
index 0000000..09bdb10
--- /dev/null
+++ b/docs/reference/mexdolphins.html
@@ -0,0 +1,113 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Data from a combination of several NOAA shipboard surveys conducted on
+pan-tropical spotted dolphins in the Gulf of Mexico. The data consist of 47
+observations of groups of dolphins. The group size was recorded, as well as
+the Beaufort sea state at the time of the observation. Coordinates for each
+observation and bathymetry data were also available as covariates for the
+analysis. A complete example analysis (and description of the data) is
+provided at http://distancesampling.org/R/vignettes/mexico-analysis.html."><title>Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins"><meta property="og:description" content="Data from a combination of several NOAA shipboard surveys conducted on
+pan-tropical spotted dolphins in the Gulf of Mexico. The data consist of 47
+observations of groups of dolphins. The group size was recorded, as well as
+the Beaufort sea state at the time of the observation. Coordinates for each
+observation and bathymetry data were also available as covariates for the
+analysis. A complete example analysis (and description of the data) is
+provided at http://distancesampling.org/R/vignettes/mexico-analysis.html."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+      <img src="" class="logo" alt=""><h1>Pan-tropical spotted dolphins in the Gulf of Mexico</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm-package.R" class="external-link"><code>R/dsm-package.R</code></a></small>
+      <div class="d-none name"><code>mexdolphins.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Data from a combination of several NOAA shipboard surveys conducted on
+pan-tropical spotted dolphins in the Gulf of Mexico. The data consist of 47
+observations of groups of dolphins. The group size was recorded, as well as
+the Beaufort sea state at the time of the observation. Coordinates for each
+observation and bathymetry data were also available as covariates for the
+analysis. A complete example analysis (and description of the data) is
+provided at <a href="http://distancesampling.org/R/vignettes/mexico-analysis.html" class="external-link">http://distancesampling.org/R/vignettes/mexico-analysis.html</a>.</p>
+    </div>
+
+
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Halpin, P.N., A.J. Read, E. Fujioka, B.D. Best, B. Donnelly,
+L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, A. Dimatteo, J. Cleary, C. Good,
+L.B. Crowder, and K.D. Hyrenbach. 2009. OBIS-SEAMAP: The world data center
+for marine mammal, sea bird, and sea turtle distributions. Oceanography
+22(2):104-115</p>
+<p>NOAA Southeast Fisheries Science Center. 1996. Report of a Cetacean Survey
+of Oceanic and Selected Continental Shelf Waters of the Northern Gulf of
+Mexico aboard NOAA Ship Oregon II (Cruise 220)</p>
+    </div>
+
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
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+
diff --git a/docs/reference/obs_exp.html b/docs/reference/obs_exp.html
new file mode 100644
index 0000000..01c15b6
--- /dev/null
+++ b/docs/reference/obs_exp.html
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Given a covariate, calculate the observed and expected counts for each
+unique value of the covariate. This can be a useful goodness of fit check
+for DSMs."><title>Observed versus expected diagnostics for fitted DSMs — obs_exp • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Observed versus expected diagnostics for fitted DSMs — obs_exp"><meta property="og:description" content="Given a covariate, calculate the observed and expected counts for each
+unique value of the covariate. This can be a useful goodness of fit check
+for DSMs."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Observed versus expected diagnostics for fitted DSMs</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/obs_exp.R" class="external-link"><code>R/obs_exp.R</code></a></small>
+      <div class="d-none name"><code>obs_exp.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Given a covariate, calculate the observed and expected counts for each
+unique value of the covariate. This can be a useful goodness of fit check
+for DSMs.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">obs_exp</span><span class="op">(</span><span class="va">model</span>, <span class="va">covar</span>, cut <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>model</dt>
+<dd><p>a fitted <code><a href="dsm.html">dsm</a></code> model object</p></dd>
+
+
+<dt>covar</dt>
+<dd><p>covariate to aggregate by (<code>character</code>)</p></dd>
+
+
+<dt>cut</dt>
+<dd><p>vector of cut points to aggregate at. If not supplied, the unique
+values of <code>covar</code> are used.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p><code>data.frame</code> with values of observed and expected counts.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>One strategy for model checking is to calculate observed and expected counts
+at different aggregations of the variable. If these match well then the
+model fit is good.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L Miller, on the suggestion of Mark Bravington.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># example with the Gulf of Mexico dolphin data</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
+<span class="r-in"><span>               key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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diff --git a/docs/reference/plot.dsm.html b/docs/reference/plot.dsm.html
new file mode 100644
index 0000000..e3269ca
--- /dev/null
+++ b/docs/reference/plot.dsm.html
@@ -0,0 +1,114 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="See plot.gam."><title>Plot a density surface model. — plot.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot a density surface model. — plot.dsm"><meta property="og:description" content="See plot.gam."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    <span class="fab fa fab fa-github fa-lg"></span>
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+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Plot a density surface model.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/plot.dsm.R" class="external-link"><code>R/plot.dsm.R</code></a></small>
+      <div class="d-none name"><code>plot.dsm.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>See <code><a href="https://rdrr.io/pkg/mgcv/man/plot.gam.html" class="external-link">plot.gam</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>a model fitted by <code><a href="dsm.html">dsm</a></code></p></dd>
+
+
+<dt>...</dt>
+<dd><p>other arguments passed to <code><a href="https://rdrr.io/pkg/mgcv/man/plot.gam.html" class="external-link">plot.gam</a></code>.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a plot!</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="dsm.html">dsm</a></code> <code><a href="https://rdrr.io/pkg/mgcv/man/plot.gam.html" class="external-link">plot.gam</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
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+  <p>If you wish to donate to development and maintenance</p>
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+  
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+  </body></html>
+
diff --git a/docs/reference/plot.dsm.var.html b/docs/reference/plot.dsm.var.html
new file mode 100644
index 0000000..140ae70
--- /dev/null
+++ b/docs/reference/plot.dsm.var.html
@@ -0,0 +1,198 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Note that the prediction data set must have x and y columns even if
+these were not used in the model."><title>Create plots of abundance uncertainty — plot.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create plots of abundance uncertainty — plot.dsm.var"><meta property="og:description" content="Note that the prediction data set must have x and y columns even if
+these were not used in the model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Create plots of abundance uncertainty</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/plot.dsm.var.R" class="external-link"><code>R/plot.dsm.var.R</code></a></small>
+      <div class="d-none name"><code>plot.dsm.var.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Note that the prediction data set must have <code>x</code> and <code>y</code> columns even if
+these were not used in the model.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm.var</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
+<span>  <span class="va">x</span>,</span>
+<span>  poly <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  limits <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  breaks <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  legend.breaks <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  xlab <span class="op">=</span> <span class="st">"x"</span>,</span>
+<span>  ylab <span class="op">=</span> <span class="st">"y"</span>,</span>
+<span>  observations <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  plot <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  boxplot.coef <span class="op">=</span> <span class="fl">1.5</span>,</span>
+<span>  x.name <span class="op">=</span> <span class="st">"x"</span>,</span>
+<span>  y.name <span class="op">=</span> <span class="st">"y"</span>,</span>
+<span>  gg.grad <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>a <code>dsm.var</code> object</p></dd>
+
+
+<dt>poly</dt>
+<dd><p>a <code>list</code> or <code>data.frame</code> with columns <code>x</code> and <code>y</code>, which gives
+the coordinates of a polygon to draw. It may also optionally have a column
+<code>group</code>, if there are many polygons.</p></dd>
+
+
+<dt>limits</dt>
+<dd><p>limits for the fill colours</p></dd>
+
+
+<dt>breaks</dt>
+<dd><p>breaks for the colour fill</p></dd>
+
+
+<dt>legend.breaks</dt>
+<dd><p>breaks as they should be displayed</p></dd>
+
+
+<dt>xlab</dt>
+<dd><p>label for the <code>x</code> axis</p></dd>
+
+
+<dt>ylab</dt>
+<dd><p>label for the <code>y</code> axis</p></dd>
+
+
+<dt>observations</dt>
+<dd><p>should observations be plotted?</p></dd>
+
+
+<dt>plot</dt>
+<dd><p>actually plot the map, or just return a <code>ggplot2</code> object?</p></dd>
+
+
+<dt>boxplot.coef</dt>
+<dd><p>control trimming (as in
+<code><a href="summary.dsm.var.html">summary.dsm.var</a></code>), only has an effect if the bootstrap
+file was saved.</p></dd>
+
+
+<dt>x.name</dt>
+<dd><p>name of the variable to plot as the <code>x</code> axis.</p></dd>
+
+
+<dt>y.name</dt>
+<dd><p>name of the variable to plot as the <code>y</code> axis.</p></dd>
+
+
+<dt>gg.grad</dt>
+<dd><p>optional <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></code> gradient object.</p></dd>
+
+
+<dt>...</dt>
+<dd><p>any other arguments</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a plot</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    
+
+
+<p>In order to get plotting to work with <code><a href="dsm_var_prop.html">dsm_var_prop</a></code> and
+<code><a href="dsm_var_gam.html">dsm_var_gam</a></code>, one must first format the data correctly
+since these functions are designed to compute very general summaries. One
+summary is calculated for each element of the list <code>pred</code> supplied to
+<code><a href="dsm_var_prop.html">dsm_var_prop</a></code> and <code><a href="dsm_var_gam.html">dsm_var_gam</a></code>.</p>
+<p>For a plot of uncertainty over a prediction grid, <code>pred</code> (a <code>data.frame</code>),
+say, we can create the correct format by simply using <code>pred.new &lt;- split(pred,1:nrow(pred))</code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="dsm_var_prop.html">dsm_var_prop</a></code>, <code><a href="dsm_var_gam.html">dsm_var_gam</a></code>,
+<code><a href="dsm_var_movblk.html">dsm_var_movblk</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/plot_pred_by_term.html b/docs/reference/plot_pred_by_term.html
new file mode 100644
index 0000000..629e293
--- /dev/null
+++ b/docs/reference/plot_pred_by_term.html
@@ -0,0 +1,143 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Plot the effect of each smooth in the model spatially. For each term in the
+model, plot its effect in space. Plots are made on the same scale, so that
+the relative influence of each smooth can be seen."><title>Spatially plot predictions per model term — plot_pred_by_term • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Spatially plot predictions per model term — plot_pred_by_term"><meta property="og:description" content="Plot the effect of each smooth in the model spatially. For each term in the
+model, plot its effect in space. Plots are made on the same scale, so that
+the relative influence of each smooth can be seen."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+<![endif]--></head><body>
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+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
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+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <a class="nav-link" href="../news/index.html">Changelog</a>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Spatially plot predictions per model term</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/plot_pred_by_term.R" class="external-link"><code>R/plot_pred_by_term.R</code></a></small>
+      <div class="d-none name"><code>plot_pred_by_term.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Plot the effect of each smooth in the model spatially. For each term in the
+model, plot its effect in space. Plots are made on the same scale, so that
+the relative influence of each smooth can be seen.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">plot_pred_by_term</span><span class="op">(</span><span class="va">dsm.obj</span>, <span class="va">data</span>, location.cov <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>, <span class="st">"y"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dsm.obj</dt>
+<dd><p>fitted <code><a href="dsm.html">dsm</a></code> object</p></dd>
+
+
+<dt>data</dt>
+<dd><p>data to use to plot (often the same as the prediction grid), data
+should also include <code>width</code> and <code>height</code> columns for plotting</p></dd>
+
+
+<dt>location.cov</dt>
+<dd><p>deprecated, use <code>location.cov</code></p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a <code>ggplot2</code> plot</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L Miller (idea taken from <code>inlabru</code>)</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># load the Gulf of Mexico dolphin data and fit a model</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
+<span class="r-in"><span>               key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">depth</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">preddata</span><span class="op">$</span><span class="va">width</span> <span class="op">&lt;-</span> <span class="va">preddata</span><span class="op">$</span><span class="va">height</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># make the plot</span></span></span>
+<span class="r-in"><span><span class="fu">plot_pred_by_term</span><span class="op">(</span><span class="va">mod1</span>, <span class="va">preddata</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># better plot would be</span></span></span>
+<span class="r-in"><span><span class="co"># library(viridis)</span></span></span>
+<span class="r-in"><span><span class="co"># plot_pred_by_term(mod1, preddata, c("x","y")) + scale_fill_viridis()</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/docs/reference/predict.dsm.html b/docs/reference/predict.dsm.html
new file mode 100644
index 0000000..4aca2d4
--- /dev/null
+++ b/docs/reference/predict.dsm.html
@@ -0,0 +1,149 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Make predictions of density or abundance outside (or inside) the covered
+area."><title>Predict from a fitted density surface model — predict.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Predict from a fitted density surface model — predict.dsm"><meta property="og:description" content="Make predictions of density or abundance outside (or inside) the covered
+area."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Predict from a fitted density surface model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/predict.dsm.R" class="external-link"><code>R/predict.dsm.R</code></a></small>
+      <div class="d-none name"><code>predict.dsm.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Make predictions of density or abundance outside (or inside) the covered
+area.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, off.set <span class="op">=</span> <span class="cn">NULL</span>, type <span class="op">=</span> <span class="st">"response"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>object</dt>
+<dd><p>a fitted <code><a href="dsm.html">dsm</a></code> object</p></dd>
+
+
+<dt>newdata</dt>
+<dd><p>spatially referenced covariates e.g. altitude, depth,
+distance to shore, etc. Covariates in the <code>data.frame</code> must have names
+<em>identical</em> to variable names used in fitting the model</p></dd>
+
+
+<dt>off.set</dt>
+<dd><p>area of each of the cells in the prediction grid. Should be
+in the same units as the segments/distances given to <code>dsm</code>. Replaces the
+column in <code>newdata</code> called <code>off.set</code> if it is supplied. Ignored if <code>newdata</code>
+is not supplied</p></dd>
+
+
+<dt>type</dt>
+<dd><p>what scale should the results be on. The default is
+<code>"response"</code>, see <code><a href="https://rdrr.io/pkg/mgcv/man/predict.gam.html" class="external-link">predict.gam</a></code> for an explanation of
+other options (usually not necessary)</p></dd>
+
+
+<dt>...</dt>
+<dd><p>any other arguments passed to <code><a href="https://rdrr.io/pkg/mgcv/man/predict.gam.html" class="external-link">predict.gam</a></code></p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>predicted values on the response scale by default (unless <code>type</code> is
+specified, in which case see <code><a href="https://rdrr.io/pkg/mgcv/man/predict.gam.html" class="external-link">predict.gam</a></code>).</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>If <code>newdata</code> is not supplied, predictions are made for the data that built
+the model. Note that the order of the results will not necessarily be the
+same as the <code>segdata</code> (segment data) <code>data.frame</code> that was supplied to
+<code><a href="dsm.html">dsm</a></code>.</p>
+<p>The area <code>off.set</code> is used if that argument is supplied, otherwise it will
+look for the areas in the column named <code>off.set</code> in the <code>newdata</code>. Either
+way the link function (usually <code>log</code>) will be applied to the offsets, so
+there is no need to log them before passing them to this function.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="https://rdrr.io/pkg/mgcv/man/predict.gam.html" class="external-link">predict.gam</a></code>, <code><a href="dsm_var_gam.html">dsm_var_gam</a></code>,
+<code><a href="dsm_var_prop.html">dsm_var_prop</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/predict.fake_ddf.html b/docs/reference/predict.fake_ddf.html
new file mode 100644
index 0000000..96bf1f7
--- /dev/null
+++ b/docs/reference/predict.fake_ddf.html
@@ -0,0 +1,133 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Prediction function for dummy detection functions. The function returns as
+many 1s as there are rows in newdata. If esw=TRUE then the
+strip width is returned."><title>Prediction for fake detection functions — predict.fake_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for fake detection functions — predict.fake_ddf"><meta property="og:description" content="Prediction function for dummy detection functions. The function returns as
+many 1s as there are rows in newdata. If esw=TRUE then the
+strip width is returned."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Prediction for fake detection functions</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dummy_ddf.R" class="external-link"><code>R/dummy_ddf.R</code></a></small>
+      <div class="d-none name"><code>predict.fake_ddf.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Prediction function for dummy detection functions. The function returns as
+many 1s as there are rows in <code>newdata</code>. If <code>esw=TRUE</code> then the
+strip width is returned.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for fake_ddf</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  newdata <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  compute <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  int.range <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  esw <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>object</dt>
+<dd><p>model object</p></dd>
+
+
+<dt>newdata</dt>
+<dd><p>how many 1s should we return?</p></dd>
+
+
+<dt>compute</dt>
+<dd><p>unused, compatibility with <code><a href="https://rdrr.io/pkg/mrds/man/predict.ds.html" class="external-link">mrds::predict</a></code></p></dd>
+
+
+<dt>int.range</dt>
+<dd><p>unused, compatibility with <code><a href="https://rdrr.io/pkg/mrds/man/predict.ds.html" class="external-link">mrds::predict</a></code></p></dd>
+
+
+<dt>esw</dt>
+<dd><p>should the strip width be returned?</p></dd>
+
+
+<dt>...</dt>
+<dd><p>for S3 consistency</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/print.dsm.html b/docs/reference/print.dsm.html
new file mode 100644
index 0000000..476ccbb
--- /dev/null
+++ b/docs/reference/print.dsm.html
@@ -0,0 +1,115 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This method just gives a short description of the fitted model. Use the
+summary.dsm method for more information."><title>Print a description of a density surface model object — print.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model object — print.dsm"><meta property="og:description" content="This method just gives a short description of the fitted model. Use the
+summary.dsm method for more information."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Print a description of a density surface model object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.dsm.R" class="external-link"><code>R/print.dsm.R</code></a></small>
+      <div class="d-none name"><code>print.dsm.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This method just gives a short description of the fitted model. Use the
+<code><a href="summary.dsm.html">summary.dsm</a></code> method for more information.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>a model fitted by <code><a href="dsm.html">dsm</a></code></p></dd>
+
+
+<dt>...</dt>
+<dd><p>unspecified and unused arguments for S3 consistency</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p><code>NULL</code></p>
+
+
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/print.dsm.var.html b/docs/reference/print.dsm.var.html
new file mode 100644
index 0000000..b19439c
--- /dev/null
+++ b/docs/reference/print.dsm.var.html
@@ -0,0 +1,119 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This method only provides a short summary, use the
+summary.dsm.var method for information."><title>Print a description of a density surface model variance object — print.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm.var"><meta property="og:description" content="This method only provides a short summary, use the
+summary.dsm.var method for information."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
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+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Print a description of a density surface model variance object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.dsm.var.R" class="external-link"><code>R/print.dsm.var.R</code></a></small>
+      <div class="d-none name"><code>print.dsm.var.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This method only provides a short summary, use the
+<code><a href="summary.dsm.var.html">summary.dsm.var</a></code> method for information.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm.var</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>a <code>dsm</code> variance object</p></dd>
+
+
+<dt>...</dt>
+<dd><p>unspecified and unused arguments for S3 consistency</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p><code>NULL</code></p>
+
+
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="summary.dsm.var.html">summary.dsm.var</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/print.dsm_varprop.html b/docs/reference/print.dsm_varprop.html
new file mode 100644
index 0000000..5b1610a
--- /dev/null
+++ b/docs/reference/print.dsm_varprop.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This method only provides a short summary, see
+summary.dsm_varprop."><title>Print a description of a density surface model variance object — print.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm_varprop"><meta property="og:description" content="This method only provides a short summary, see
+summary.dsm_varprop."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
+  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
+    <span class="fa fa-twitter"></span>
+     
+  </a>
+</li>
+<li class="nav-item">
+  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Print a description of a density surface model variance object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.dsm_varprop.R" class="external-link"><code>R/print.dsm_varprop.R</code></a></small>
+      <div class="d-none name"><code>print.dsm_varprop.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This method only provides a short summary, see
+<code><a href="summary.dsm_varprop.html">summary.dsm_varprop</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm_varprop</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>a <code>dsm</code> variance object</p></dd>
+
+
+<dt>...</dt>
+<dd><p>unspecified and unused arguments for S3 consistency</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="summary.dsm_varprop.html">summary.dsm_varprop</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/print.summary.dsm.var.html b/docs/reference/print.summary.dsm.var.html
new file mode 100644
index 0000000..a3e5235
--- /dev/null
+++ b/docs/reference/print.summary.dsm.var.html
@@ -0,0 +1,116 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="See summary.dsm.var for information."><title>Print summary of density surface model variance object — print.summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm.var"><meta property="og:description" content="See summary.dsm.var for information."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
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+</li>
+      </ul></div>
+
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+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Print summary of density surface model variance object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.summary.dsm.var.R" class="external-link"><code>R/print.summary.dsm.var.R</code></a></small>
+      <div class="d-none name"><code>print.summary.dsm.var.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>See <code><a href="summary.dsm.var.html">summary.dsm.var</a></code> for information.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for summary.dsm.var</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>a summary of <code>dsm</code> variance object</p></dd>
+
+
+<dt>...</dt>
+<dd><p>unspecified and unused arguments for S3 consistency</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p><code>NULL</code></p>
+
+
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="summary.dsm.var.html">summary.dsm.var</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/print.summary.dsm_varprop.html b/docs/reference/print.summary.dsm_varprop.html
new file mode 100644
index 0000000..55a3827
--- /dev/null
+++ b/docs/reference/print.summary.dsm_varprop.html
@@ -0,0 +1,116 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="See summary.dsm_varprop for information."><title>Print summary of density surface model variance object — print.summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm_varprop"><meta property="og:description" content="See summary.dsm_varprop for information."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
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+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
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+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Print summary of density surface model variance object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.summary.dsm_varprop.R" class="external-link"><code>R/print.summary.dsm_varprop.R</code></a></small>
+      <div class="d-none name"><code>print.summary.dsm_varprop.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>See <code><a href="summary.dsm_varprop.html">summary.dsm_varprop</a></code> for information.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for summary.dsm_varprop</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>a summary of <code>dsm</code> variance object</p></dd>
+
+
+<dt>...</dt>
+<dd><p>unspecified and unused arguments for S3 consistency</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p><code>NULL</code></p>
+
+
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="summary.dsm.var.html">summary.dsm.var</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/rqgam.check.html b/docs/reference/rqgam.check.html
new file mode 100644
index 0000000..aebe3a2
--- /dev/null
+++ b/docs/reference/rqgam.check.html
@@ -0,0 +1,100 @@
+<!DOCTYPE html>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Randomised quantile residuals check plot for GAMs/DSMs</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
+      <div class="d-none name"><code>rqgam.check.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function is deprecated, use <a href="rqgam_check.html">rqgam_check</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">rqgam.check</span><span class="op">(</span><span class="va">gam.obj</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>gam.obj</dt>
+<dd><p>a <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code> or <code><a href="dsm.html">dsm</a></code>
+object.</p></dd>
+
+
+<dt>...</dt>
+<dd><p>arguments passed on to all plotting functions</p></dd>
+
+</dl></div>
+
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diff --git a/docs/reference/rqgam_check-1.png b/docs/reference/rqgam_check-1.png
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diff --git a/docs/reference/rqgam_check.html b/docs/reference/rqgam_check.html
new file mode 100644
index 0000000..748ca03
--- /dev/null
+++ b/docs/reference/rqgam_check.html
@@ -0,0 +1,160 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Reproduces the "Resids vs. linear pred" plot from
+gam.check but using randomised quantile residuals, a la
+Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking
+for "funnel"-type structures in the points, which is much easier with
+randomised quantile residuals than with deviance residuals, when your model
+uses a count distribution as the response.'><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check"><meta property="og:description" content='Reproduces the "Resids vs. linear pred" plot from
+gam.check but using randomised quantile residuals, a la
+Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking
+for "funnel"-type structures in the points, which is much easier with
+randomised quantile residuals than with deviance residuals, when your model
+uses a count distribution as the response.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Randomised quantile residuals check plot for GAMs/DSMs</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/rqgam_check.R" class="external-link"><code>R/rqgam_check.R</code></a></small>
+      <div class="d-none name"><code>rqgam_check.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Reproduces the "Resids vs. linear pred" plot from
+<code><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></code> but using randomised quantile residuals, a la
+Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking
+for "funnel"-type structures in the points, which is much easier with
+randomised quantile residuals than with deviance residuals, when your model
+uses a count distribution as the response.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">rqgam_check</span><span class="op">(</span><span class="va">gam.obj</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>gam.obj</dt>
+<dd><p>a <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code> or <code><a href="dsm.html">dsm</a></code>
+object.</p></dd>
+
+
+<dt>...</dt>
+<dd><p>arguments passed on to all plotting functions</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>just plots!</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Note that this function only works with negative binomial and Tweedie
+response distributions.</p>
+<p>Earlier versions of this function produced the full
+<code><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></code> output, but this was confusing as only one of
+the plots was really useful.  Checks of <code>k</code> are not computed, these need to
+be done using <code><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>Based on code by Natalie Kelly, bugs added by Dave Miller</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \donttest{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">tweedie</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># load the Gulf of Mexico dolphin data (see ?mexdolphins)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># fit a detection function and look at the summary</span></span></span>
+<span class="r-in"><span><span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
+<span class="r-in"><span>               key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Fitting hazard-rate key function</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> AIC= 677.418</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> No survey area information supplied, only estimating detection function.</span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># fit a simple smooth of x and y with a Tweedie response with estimated</span></span></span>
+<span class="r-in"><span><span class="co">#  p parameter</span></span></span>
+<span class="r-in"><span><span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, family<span class="op">=</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Some observations are outside of detection function 1 truncation!</span>
+<span class="r-in"><span><span class="fu">rqgam_check</span><span class="op">(</span><span class="va">mod1</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="rqgam_check-1.png" alt="" width="700" height="433"></span>
+<span class="r-in"><span><span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/docs/reference/summary.dsm.html b/docs/reference/summary.dsm.html
new file mode 100644
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@@ -0,0 +1,114 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Gives a brief summary of a fitted dsm object."><title>Summarize a fitted density surface model — summary.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize a fitted density surface model — summary.dsm"><meta property="og:description" content="Gives a brief summary of a fitted dsm object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Summarize a fitted density surface model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/summary.dsm.R" class="external-link"><code>R/summary.dsm.R</code></a></small>
+      <div class="d-none name"><code>summary.dsm.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Gives a brief summary of a fitted <code><a href="dsm.html">dsm</a></code> object.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>object</dt>
+<dd><p>a model fitted by <code><a href="dsm.html">dsm</a></code></p></dd>
+
+
+<dt>...</dt>
+<dd><p>other arguments passed to <code><a href="https://rdrr.io/pkg/mgcv/man/summary.gam.html" class="external-link">summary.gam</a></code></p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a summary object</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="dsm.html">dsm</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
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+
diff --git a/docs/reference/summary.dsm.var.html b/docs/reference/summary.dsm.var.html
new file mode 100644
index 0000000..bf30bb6
--- /dev/null
+++ b/docs/reference/summary.dsm.var.html
@@ -0,0 +1,138 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Gives a brief summary of a fitted dsm variance object."><title>Summarize the variance of a density surface model — summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm.var"><meta property="og:description" content="Gives a brief summary of a fitted dsm variance object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
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+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
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+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Summarize the variance of a density surface model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/summary.dsm.var.R" class="external-link"><code>R/summary.dsm.var.R</code></a></small>
+      <div class="d-none name"><code>summary.dsm.var.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Gives a brief summary of a fitted <code>dsm</code> variance object.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm.var</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  alpha <span class="op">=</span> <span class="fl">0.05</span>,</span>
+<span>  boxplot.coef <span class="op">=</span> <span class="fl">1.5</span>,</span>
+<span>  bootstrap.subregions <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>object</dt>
+<dd><p>a <code>dsm.var</code> object</p></dd>
+
+
+<dt>alpha</dt>
+<dd><p>alpha level for confidence intervals (default 0.05 to give a
+95\<!-- % confidence internal, i.e. we generate \code{100*c(alpha/2, 1-alpha/2)} -->
+confidence intervals)</p></dd>
+
+
+<dt>boxplot.coef</dt>
+<dd><p>the value of <code>coef</code> used to calculate the outliers see
+<code><a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></code>.</p></dd>
+
+
+<dt>bootstrap.subregions</dt>
+<dd><p>list of vectors of logicals or indices for
+subregions for which variances need to be calculated (only for bootstraps
+(see <code><a href="dsm.var.prop.html">dsm.var.prop</a></code> for how to use subregions with variance
+propagation).</p></dd>
+
+
+<dt>...</dt>
+<dd><p>unused arguments for S3 compatibility</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a summary object</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="dsm.var.movblk.html">dsm.var.movblk</a></code>, <code><a href="dsm.var.prop.html">dsm.var.prop</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/summary.dsm_varprop.html b/docs/reference/summary.dsm_varprop.html
new file mode 100644
index 0000000..ad02a4c
--- /dev/null
+++ b/docs/reference/summary.dsm_varprop.html
@@ -0,0 +1,122 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Gives a brief summary of a fitted dsm_varprop variance
+object."><title>Summarize the variance of a density surface model — summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm_varprop"><meta property="og:description" content="Gives a brief summary of a fitted dsm_varprop variance
+object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
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+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
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+  <a class="nav-link" href="../reference/index.html">Reference</a>
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
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+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Summarize the variance of a density surface model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/summary.dsm_varprop.R" class="external-link"><code>R/summary.dsm_varprop.R</code></a></small>
+      <div class="d-none name"><code>summary.dsm_varprop.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Gives a brief summary of a fitted <code><a href="dsm_varprop.html">dsm_varprop</a></code> variance
+object.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm_varprop</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, alpha <span class="op">=</span> <span class="fl">0.05</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>object</dt>
+<dd><p>a <code>dsm.var</code> object</p></dd>
+
+
+<dt>alpha</dt>
+<dd><p>alpha level for confidence intervals (default 0.05 to give a
+95% confidence internal)</p></dd>
+
+
+<dt>...</dt>
+<dd><p>unused arguments for S3 compatibility</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>a summary object</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="dsm_varprop.html">dsm_varprop</a></code>, <code><a href="summary.dsm.var.html">summary.dsm.var</a></code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L. Miller</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/trim.var.html b/docs/reference/trim.var.html
new file mode 100644
index 0000000..bb6de9c
--- /dev/null
+++ b/docs/reference/trim.var.html
@@ -0,0 +1,117 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Trim the variance estimates from the bootstrap. This is defined as the
+percentage defined as amount necessary to bring median and trimmed mean
+within 8% of each other these are defined as 'outliers'."><title>Trimmed variance — trim.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Trimmed variance — trim.var"><meta property="og:description" content="Trim the variance estimates from the bootstrap. This is defined as the
+percentage defined as amount necessary to bring median and trimmed mean
+within 8% of each other these are defined as 'outliers'."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
+
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+</li>
+<li class="nav-item dropdown">
+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
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+</li>
+      </ul></div>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Trimmed variance</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/trim.var.R" class="external-link"><code>R/trim.var.R</code></a></small>
+      <div class="d-none name"><code>trim.var.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Trim the variance estimates from the bootstrap. This is defined as the
+percentage defined as amount necessary to bring median and trimmed mean
+within 8% of each other these are defined as 'outliers'.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">trim.var</span><span class="op">(</span><span class="va">untrimmed.bootstraps</span>, boxplot.coef <span class="op">=</span> <span class="fl">1.5</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>untrimmed.bootstraps</dt>
+<dd><p>(usually the <code>$study.area.total</code> element of a
+returned <code><a href="dsm.var.movblk.html">dsm.var.movblk</a></code> bootstrap object.</p></dd>
+
+
+<dt>boxplot.coef</dt>
+<dd><p>the value of <code>coef</code> used to calculate the outliers see
+<code><a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></code>.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>trimmed variance</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>Louise Burt</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/reference/vis.concurvity.html b/docs/reference/vis.concurvity.html
new file mode 100644
index 0000000..371aa8a
--- /dev/null
+++ b/docs/reference/vis.concurvity.html
@@ -0,0 +1,99 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use vis_concurvity."><title>Visualise concurvity between terms in a GAM — vis.concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis.concurvity"><meta property="og:description" content="This function is deprecated, use vis_concurvity."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+    
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+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
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+  <a class="nav-link" href="../news/index.html">Changelog</a>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Visualise concurvity between terms in a GAM</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
+      <div class="d-none name"><code>vis.concurvity.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function is deprecated, use <a href="vis_concurvity.html">vis_concurvity</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">vis.concurvity</span><span class="op">(</span><span class="va">model</span>, type <span class="op">=</span> <span class="st">"estimate"</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>model</dt>
+<dd><p>fitted model</p></dd>
+
+
+<dt>type</dt>
+<dd><p>concurvity measure to plot, see <code><a href="https://rdrr.io/pkg/mgcv/man/concurvity.html" class="external-link">concurvity</a></code></p></dd>
+
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
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+
diff --git a/docs/reference/vis_concurvity.html b/docs/reference/vis_concurvity.html
new file mode 100644
index 0000000..d5ab8b0
--- /dev/null
+++ b/docs/reference/vis_concurvity.html
@@ -0,0 +1,139 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Plot measures of how much one term in the model could be explained by
+another. When values are high, one should consider re-running variable
+selection with one of the offending variables removed to check for stability
+in term selection."><title>Visualise concurvity between terms in a GAM — vis_concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis_concurvity"><meta property="og:description" content="Plot measures of how much one term in the model could be explained by
+another. When values are high, one should consider re-running variable
+selection with one of the offending variables removed to check for stability
+in term selection."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">dsm</a>
+
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+
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+  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    <span class="fab fa fab fa-github fa-lg"></span>
+     
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+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Visualise concurvity between terms in a GAM</h1>
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/vis_concurvity.R" class="external-link"><code>R/vis_concurvity.R</code></a></small>
+      <div class="d-none name"><code>vis_concurvity.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Plot measures of how much one term in the model could be explained by
+another. When values are high, one should consider re-running variable
+selection with one of the offending variables removed to check for stability
+in term selection.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">vis_concurvity</span><span class="op">(</span><span class="va">model</span>, type <span class="op">=</span> <span class="st">"estimate"</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>model</dt>
+<dd><p>fitted model</p></dd>
+
+
+<dt>type</dt>
+<dd><p>concurvity measure to plot, see <code><a href="https://rdrr.io/pkg/mgcv/man/concurvity.html" class="external-link">concurvity</a></code></p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>These methods are considered somewhat experimental at this time. Consult
+<code><a href="https://rdrr.io/pkg/mgcv/man/concurvity.html" class="external-link">concurvity</a></code> for more information on how concurvity
+measures are calculated.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p>David L Miller</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># load the Gulf of Mexico dolphin data (see ?mexdolphins)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">mexdolphins</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># fit a detection function and look at the summary</span></span></span>
+<span class="r-in"><span><span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
+<span class="r-in"><span>               key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># fit a simple smooth of x and y to counts</span></span></span>
+<span class="r-in"><span><span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">depth</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># visualise concurvity using the "estimate" metric</span></span></span>
+<span class="r-in"><span><span class="fu">vis_concurvity</span><span class="op">(</span><span class="va">mod1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>We improve our site and software support by using Microsoft Clarity to see <br> how you use our website. By using our site, you agree that we and Microsoft <br> can collect and use this data. Clarity is GDPR compliant.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>If you wish to donate to development and maintenance</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/docs/search.json b/docs/search.json
new file mode 100644
index 0000000..7cded0a
--- /dev/null
+++ b/docs/search.json
@@ -0,0 +1 @@
+[{"path":"/articles/lines_gomex/mexico-analysis.html","id":"introduction","dir":"Articles > Lines_gomex","previous_headings":"","what":"Introduction","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"analysis based dataset observations pantropical dolphins Gulf Mexico (shipped Distance 6.0 later). convenience data bundled R-friendly format, although code necessary creating data Distance project files available github. OBIS-SEAMAP page data may found SEFSC GoMex Oceanic 1996 survey page. intention highlight features dsm package, rather perform full analysis data. reason, important steps fully explored. familiarity density surface modelling (Miller, Burt, Rexstad, & Thomas, 2013) (Hedley & Buckland, 2004) assumed.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"preamble","dir":"Articles > Lines_gomex","previous_headings":"","what":"Preamble","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"start, load dsm package (dependencies) set options: order run vignette, ’ll need install R packages. can done via following call install.packages:","code":"library(dsm) library(ggplot2)  # plotting options gg.opts <- theme(panel.grid.major=element_blank(),                  panel.grid.minor=element_blank(),                  panel.background=element_blank()) install.packages(c(\"dsm\", \"Distance\", \"knitr\", \"distill\", \"ggplot2\", \"rgdal\",                    \"maptools\", \"plyr\", \"tweedie\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"the-data","dir":"Articles > Lines_gomex","previous_headings":"","what":"The data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data need included dsm package, two additional objects needed plotting required can downloaded put directory file. data can loaded R using following code: add objects survey.area pred.polys environment.","code":"load(\"mexdolphins-extra.rda\")"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"observation-and-segment-data","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Observation and segment data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data analysis nicely pre-formatted shipped dsm. Loading data, can see four data.frames, first lines shown: segdata holds segment data: transects already “chopped” segments. distdata holds distance sampling data used fit detection function. obsdata links distance data segments. preddata holds prediction grid (includes necessary covariates). Typically (.e. datasets) necessary divide transects segments, allocate observations correct segments using GIS similar package1, starting analysis using dsm.","code":"data(mexdolphins) head(segdata) ##   longitude latitude        x        y Effort Transect.Label Sample.Label depth ## 1 -86.92712 29.94378 836105.9 -1011416  13800       19960417   19960417-1 135.0 ## 2 -86.83176 29.84030 846012.9 -1021407  14000       19960417   19960417-2 147.7 ## 3 -86.74445 29.75279 855022.6 -1029785  14000       19960417   19960417-3 152.1 ## 4 -86.65230 29.65522 864610.3 -1039168  13900       19960417   19960417-4 163.8 ## 5 -86.56648 29.56088 873598.1 -1048266  13800       19960417   19960417-5 179.7 ## 6 -86.49290 29.49000 881203.7 -1055004  13800       19960417   19960417-6 188.5 head(distdata) ##     object size  distance Effort detected beaufort latitude longitude ## 45      45   21 3296.6363  36300        1        4 27.72872 -86.00159 ## 61      61  150  929.1937  17800        1        4 25.99896 -87.62712 ## 63      63  125 6051.0009  21000        1        2 26.00693 -87.94881 ## 85      85   75 5499.6971  21800        1        1 27.50344 -90.44891 ## 114    114   50 7258.9837  13400        1        3 27.40568 -94.99483 ## 120    120   45 1454.7962  20900        1        5 26.01765 -95.97449 ##              x        y ## 45  948000.065 -1236192 ## 61  812161.653 -1436899 ## 63  780969.520 -1438985 ## 85  528656.807 -1297833 ## 114  95910.149 -1324562 ## 120   2477.665 -1473909 head(obsdata) ##     object Sample.Label size  distance Effort ## 45      45   19960421-9   21 3296.6363  36300 ## 61      61   19960423-7  150  929.1937  17800 ## 63      63   19960423-9  125 6051.0009  21000 ## 85      85   19960427-1   75 5499.6971  21800 ## 114    114   19960430-8   50 7258.9837  13400 ## 120    120   19960501-5   45 1454.7962  20900 head(preddata) ##   latitude longitude        x          y depth      area ## 1 30.08333 -87.58333 774402.9 -1002759.1    35 271236913 ## 2 30.08333 -87.41667 789688.6 -1001264.5    30 271236913 ## 3 30.08333 -87.25000 804971.3  -999740.6    27 271236913 ## 4 30.08333 -87.08333 820251.1  -998187.5    22 271236913 ## 5 30.08333 -86.91667 835528.0  -996605.2    46 271236913 ## 6 29.91667 -87.75000 760783.1 -1021810.3    14 271236913"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"shapefiles-and-converting-units","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Shapefiles and converting units","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often data spatial analysis comes many different sources. important ensure measurements used analysis compatible units, otherwise resulting estimates incorrect hard interpret. measurements SI units outset removes need conversion later, making life much easier. data already appropriate units (Northings Eastings: kilometres centroid, projected using North American Lambert Conformal Conic projection). extensive literature particular projections latitude longitude appropriate highly recommend reader review particular study area; (Bivand, Pebesma, & Gómez-Rubio, 2008) good starting point. data frames already measurements appropriately converted. convention directions named x y. Using latitude longitude performing spatial smoothing can problematic certain smoother bases used. particular bivariate isotropic bases used non-isotropic nature latitude longitude inconsistent (moving one degree one direction moving one degree ). give example projecting polygon defines survey area (simply read R using readShapeSpatial shapefile produced GIS). Next bit code works study.area outline, massaging use soap film smoother. using soap film smoothers, code extraneous. code generates plot, shows survey area transect lines overlaid (using data segdata).  Also note since projected prediction grid, “squares” look quite like squares. plotting use polygons saved, polygons (stored pred.polys) read shapefile created GIS, object class SpatialPolygons sp package. needs project proper plotting using functions sf package. predsf <- st_as_sf(pred.polys)  Tips plotting polygons available ggplot2 pkg_down.","code":"library(sf) library(plyr)  # tell R that the survey.area object is currently in lat/long sp::proj4string(survey.area) <- sp::CRS(\"+proj=longlat +datum=WGS84\")  predsf <- st_as_sf(pred.polys)  area.sf <- st_as_sf(survey.area) st_crs(area.sf) <- \"WGS84\" area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))  # Convert preddata to a spatial object preddata_sf <- st_as_sf(preddata, coords=c(\"x\", \"y\")) st_crs(preddata_sf) <- st_crs(area.sf.proj) # Perform the intersection preddata_sf <- st_intersection(preddata_sf, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries coords_preddata <- data.frame(st_coordinates(preddata_sf))  preddata_sf$x <- coords_preddata$X preddata_sf$y <- coords_preddata$Y # proj 4 string # using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/ lcc_proj4 <- sp::CRS(\"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs \")  # project using LCC survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)  # simplify the object survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords) names(survey.area) <- c(\"x\", \"y\") segdata_sf <- st_as_sf(segdata, coords = c(\"x\",\"y\")) st_crs(segdata_sf) <- st_crs(area.sf.proj) # study area outline and segment centres plot_segments <- ggplot() +   geom_sf(data = area.sf.proj, fill=\"lightblue\", color = \"blue\", linewidth=.1) +   geom_sf(data=segdata_sf, fill=NA, color=\"black\", linewidth=.3) +   labs(title=\"1996 SE Fisheries Science Center Gulf of Mexico cruise\",        subtitle = \"Points are segment centres\") +   scale_fill_viridis_c(option = \"magma\", guide = \"none\") plot_segments predsf <- st_as_sf(pred.polys) # plot as projected plot(st_geometry(predsf), axes=TRUE)"},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"distance-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Distance data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top panels EDA plots show histograms observed distances cluster size, bottom panels show relationship observed distance observed cluster size, relationship observed distance Beaufort sea state. plots show relationship cluster size observed distance (fewer smaller clusters seem seen larger distances). following code generates EDA plots:  Top row, left right: histograms distance cluster size; bottom row: plot distance cluster size plot distances Beaufort sea state.","code":"par(mfrow=c(2,2))  # histograms hist(distdata$distance,main=\"\",xlab=\"Distance (m)\") hist(distdata$size,main=\"\",xlab=\"Cluster size\")  # plots of distance vs. cluster size plot(distdata$distance, distdata$size, main=\"\", xlab=\"Distance (m)\",      ylab=\"Group size\", pch=19, cex=0.5, col=gray(0.7))  # lm fit l.dat <- data.frame(distance=seq(0,8000,len=1000)) lo <- lm(size~distance, data=distdata) lines(l.dat$distance, as.vector(predict(lo,l.dat)))  plot(distdata$distance,distdata$beaufort, main=\"\", xlab=\"Distance (m)\",      ylab=\"Beaufort sea state\", pch=19, cex=0.5, col=gray(0.7))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Spatial data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Looking separately spatial data without thinking distances, can plot observed group sizes space (point size proportional group size observation). Circle size indicates size group observation. rather large areas observations, might cause variance estimates abundance rather large. also see depth data use depth later explanatory covariate spatial model.","code":"prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid) cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries depth <- ggplot() +          geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +          labs(title = \"Spotted dolphins, Gulf of Mexico\",               subtitle = \"Depth in meters, size of detected dolphin groups\") +          xlab(\"Longitude\") + ylab(\"Latitude\") +          geom_point(aes(x, y, size=size), data=distdata, colour=\"red\",alpha=I(0.5)) +          scale_fill_viridis_c(option = \"viridis\", direction = 1) depth"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"estimating-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"","what":"Estimating the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"use ds function package Distance fit detection function. (Distance package intended make standard distance sampling R relatively straightforward. flexible complex alternative, see function ddf mrds library.) First, loading Distance library: can fit detection function hazard-rate key adjustment terms: Calling summary gives us information parameter estimates, probability detection, AIC, etc: following code generates plot fitted detection function (left) quantile-quantile plot (right):  quantile-quantile plot show relatively good goodness fit hazard-rate detection function.","code":"library(Distance) detfc.hr.null <- ds(distdata, max(distdata$distance), key=\"hr\", adjustment=NULL) ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). summary(detfc.hr.null) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  841.1841  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##             estimate        se ## (Intercept) 7.986391 0.5239151 ##  ## Shape coefficient(s):   ##                estimate         se ## (Intercept) 0.000237466 0.02958901 ##  ##                       Estimate         SE        CV ## Average p            0.5918795  0.1667006 0.2816462 ## N in covered region 79.4080596 23.5573051 0.2966614 plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2) gof_ds(detfc.hr.null) ##  ## Goodness of fit results for ddf object ##  ## Distance sampling Cramer-von Mises test (unweighted) ## Test statistic = 0.0976846 p-value = 0.596345"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-covariates-to-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Estimating the detection function","what":"Adding covariates to the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"common include covariates detection function (-called Multiple Covariate Distance Sampling MCDS). dataset two covariates collected individual: Beaufort sea state size. brevity fit hazard-rate detection functions sea state included factor covariate follows: looking summary, detection function covariates give lower AIC model without covariates (843.71 vs. 841.18 hazard-rate model without covariates). Looking back bottom-right panel EDA plots, can see discernible pattern plot Beaufort vs distance. brevity, detection function model selection omitted . practise fit many different forms detection function (select model based goodness fit testing AIC).","code":"detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),                   key=\"hr\", adjustment=NULL) summary(detfc.hr.beau) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  843.7115  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##                         estimate        se ## (Intercept)           7.66291936  1.078972 ## as.factor(beaufort)2  2.24809809 14.754677 ## as.factor(beaufort)3  0.28581820  1.020809 ## as.factor(beaufort)4  0.07133776  1.225360 ## as.factor(beaufort)5 -0.36421115  1.540324 ##  ## Shape coefficient(s):   ##              estimate        se ## (Intercept) 0.2994644 0.5184204 ##  ##                       Estimate         SE        CV ## Average p            0.5420677  0.1753555 0.3234937 ## N in covered region 86.7050383 29.4646397 0.3398262"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"fitting-a-dsm","dir":"Articles > Lines_gomex","previous_headings":"","what":"Fitting a DSM","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"fitting dsm model, data must segmented; consists chopping transects attributing counts segments. mentioned , data already segmented.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"a-simple-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"A simple model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"begin simple model. assume number individuals segment quasi-Poisson distributed smooth function spatial coordinates (note formula exactly one specify gam mgcv). setting group=TRUE, abundance clusters/groups rather individuals can estimated (though ignore ). Note set method=\"REML\" ensure smooth terms estimated reliably. Running model: can obtain summary fitted model: exact interpretation model summary results can found (S. N. Wood, 2017); can see various information smooth components fitted general model statistics. can use deviance explained compare models2. can also get rough idea smooth space looks like using vis.gam (white/yellow indicates high values, red low indicates low values):  type=\"response\" argument ensures plot scale abundance values relative (offsets set median values). means plot useful get idea general shape smooth interpreted directly.","code":"dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)   -18.20       0.53  -34.34   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##         edf Ref.df     F  p-value     ## s(x,y) 24.8  27.49 2.354 0.000733 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.121   Deviance explained = 43.4% ## -REML = 936.04  Scale est. = 94.367    n = 387 vis.gam(dsm.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-another-environmental-covariate-to-the-spatial-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Adding another environmental covariate to the spatial model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data set also contains depth covariate (plotted ). can include model simply: see drop deviance explained, perhaps model useful first. discuss setting k parameter Model checking, . Setting select=TRUE (argument gam) impose extra shrinkage terms smooth model (allowing smooth terms removed model fitting; see ?gam information). particularly useful , include . However many environmental predictors model can good way (along looking \\(p\\)-values) perform term selection. Simply calling plot model object allows us look relationship depth linear predictor:  Omitting argument select call plot plot smooth terms, one time.","code":"dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy.depth) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, k = 10) + s(depth, k = 20) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.741      1.236  -15.16   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F p-value   ## s(x,y)   6.062  7.371 0.923  0.5038   ## s(depth) 9.443 11.466 1.585  0.0824 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0909   Deviance explained = 34.3% ## -REML = 939.52  Scale est. = 124.9     n = 387 plot(dsm.xy.depth, select=2)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-models-when-there-are-covariates-in-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Spatial models when there are covariates in the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"code fit DSM covariates detection function similar models, . However since detection function observation-level covariates, must estimate abundance per segment using Horvitz-Thompson-like estimator modelling, change response abundance.est: can see, summary results rather similar: resulting spatial smooth (though resulting surface somewhat “amplified”):","code":"dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method=\"REML\") summary(dsm.est.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## abundance.est ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -18.0233     0.4739  -38.03   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value     ## s(x,y) 24.64  27.44 2.349 0.00029 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.129   Deviance explained = 42.2% ## -REML = 1043.6  Scale est. = 174.1     n = 387 vis.gam(dsm.est.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", zlim=c(0, 300), contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-response-distributions","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other response distributions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often quasi-Poisson distribution doesn’t give adequate flexibility capture overdispersion response data (see Model checking Model selection ), illustrate two additional distributions can used count data. models section, ’ll move back count response, though estimated abundance also work.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"tweedie","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Tweedie","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Response distributions quasi-Poisson can used, example Tweedie distribution. Tweedie distribution available dsm setting family=tw().","code":"dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method=\"REML\") summary(dsm.xy.tweedie) ##  ## Family: Tweedie(p=1.347)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -17.2651     0.2363  -73.08   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value   ## s(x,y) 12.82  17.13 1.628  0.0562 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0684   Deviance explained = 26.8% ## -REML = 351.49  Scale est. = 60.26     n = 387"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"negative-binomial","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Negative binomial","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Though used , similarly, two options negative binomial distribution: negbin nb. former requires user specification single parameter theta range values parameter (specified vector), latter estimates value theta model fitting process (generally faster). latter recommended users.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-spatial-modelling-options","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other spatial modelling options","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"large literature spatial modelling using GAMs, much can harnessed DSM context. highlights.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"soap-film-smoothing","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other spatial modelling options","what":"Soap film smoothing","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"account complex region (e.g., region includes peninsulae) can use soap film smoother (Simon N. Wood, Bravington, & Hedley, 2008). use soap film smoother spatial part model must create set knots smoother use. easily done using make.soapgrid() function dsm: second argument specifies number points (direction) grid used create knots (knots grid outside survey.area removed). saw exploratory analysis, transect lines outside survey area. cause soap film smoother fail, remove : Note soap_checker script available can useful ensuring boundary, data knots correct format use soap film smoother. can run model depth covariate along spatial (soap film) smooth. Note k argument now refers complexity boundary smooth soap film, complexity film controlled knots given xt argument.","code":"soap.knots <- make.soapgrid(survey.area,c(15,10)) x <- segdata$x; y<-segdata$y onoff <- inSide(x=x,y=y, bnd=as.list(survey.area)) rm(x,y) segdata.soap <- segdata[onoff,] dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs=\"so\", k=15, xt=list(bnd=list(survey.area))) +             s(depth),           family=tw(), method=\"REML\",           detfc.hr.null, segdata.soap, obsdata, knots=soap.knots) summary(dsm.xy.tweedie.soap) ##  ## Family: Tweedie(p=1.356)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, bs = \"so\", k = 15, xt = list(bnd = list(survey.area))) +  ##     s(depth) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.018      0.405  -44.49   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F  p-value     ## s(x,y)   3.050 72.000 0.064 0.114944     ## s(depth) 5.135  6.196 3.895 0.000793 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0653   Deviance explained = 33.1% ## -REML = 339.26  Scale est. = 54.022    n = 365"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-checking","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model checking","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Fitting models well good, ’d like confirm models reasonable; dsm provides functions model checking. can use gam.check generate diagnostic plots:  show deviation Q-Q plot. “line” points plot residuals vs. linear predictor plot corresponds zeros data. Note well plots, gam.check also produces information model fitting. particular interest us last lines tell us basis size. k parameter provided s (te) terms dsm controls complexity smooths model. setting k parameter specify largest complexity smooth term model; long high enough, can sure enough flexibility. output gam.check , can see “p-value” calculated size basis, can good guide whether basis size needs increased. ?choose.k manual page mgcv gives guidance technical details matter. can look model form Tweedie distribution specified response:  Q-Q plot now seems much better (closer \\(y=x\\) line). plots histogram residuals rather hard interpret due large number zeros data. guidance interpreting gam.check output can found (S. N. Wood, 2017).","code":"gam.check(dsm.xy) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 6 iterations. ## Gradient range [-7.105947e-08,-9.826417e-09] ## (score 936.0363 & scale 94.3674). ## Hessian positive definite, eigenvalue range [5.800328,192.5304]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 24.8    1.01    0.99 gam.check(dsm.xy.tweedie) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 8 iterations. ## Gradient range [-1.513175e-06,3.981813e-06] ## (score 351.486 & scale 60.26034). ## Hessian positive definite, eigenvalue range [0.6230615,103.5094]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 12.8    0.73    0.34"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"randomised-quantile-residuals","dir":"Articles > Lines_gomex","previous_headings":"Model checking","what":"Randomised quantile residuals","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top right panel gam.check plots residuals vs. linear predictor plot includes odd line predictions. artifact link function, showing exact zeros data. can misleading distracting, making difficult see whether residuals show heteroskedasticity. Randomised quantile residuals (Dunn & Smyth, 1996) avoid issue transforming residuals exactly normally distributed. makes residuals vs. linear predictor plot much easier interpret therefore doesn’t include artifacts generated link function. plots can produced using rqgam.check dsm:  can see issue heteroskedasticity (increase spread residuals vs. linear predictor plot increasing values linear predictor). One can also plot residuals covariate values check pattern residuals. Note general, plots “Resids vs. linear pred.” interpreted caution output rqgam.check residuals generated normal construction (example Q-Q plot histogram residuals always look fine).","code":"rqgam_check(dsm.xy.tweedie)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-selection","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model selection","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Assuming models “passed” checks gam.check, rqgam.check sufficiently flexible, may left choice model “best”. several methods choosing best model – AIC, REML/GCV scores, deviance explained, full cross-validation test data . Though document intend full analysis pantropical dolphin data, can create results table compare various models fitted far terms abundance estimates associated uncertainties. can use resulting data.frame build table results using kable function:","code":"# make a data.frame to print out mod_results <- data.frame(\"Model name\" = c(\"`dsm.xy`\", \"`dsm.xy.depth`\", \"`dsm.xy.tweedie`\", \"`dsm.xy.tweedie.soap`\",                                            \"`dsm.est.xy`\"),                           \"Description\" = c(\"Bivariate smooth of location, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie\",                                             \"Soap film smooth of location, smooth of depth, Tweedie\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function\"),                           \"Deviance explained\" = c(unlist(lapply(list(dsm.xy,                                                                       dsm.xy.depth,                                                                       dsm.xy.tweedie,                                                                       dsm.xy.tweedie.soap),                  function(x){paste0(round(summary(x)$dev.expl*100,2),\"%\")})),NA)) kable(mod_results, col.names=c(\"Model name\", \"Description\", \"Deviance explained\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"abundance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Abundance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"model checked selected, can make predictions grid calculate abundance. offset stored area column3. Given predicted abundance estimates cell prediction grid, connected prediction grid object mapping:  can calculate abundance survey area simply summing predictions: can compare plot predictions dsm.xy.depth:  can see inclusion depth model noticeable effect distribution (note difference legend scale two plots). can also look total abundance: see much change abundance, terms abundance alone isn’t much two models. Next ’ll go look variance next can see bigger differences models.","code":"dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area) prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid)  preddata_sf$Prediction_xy <- dsm.xy.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.pred) ## [1] 28430.76 dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)  preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, smooth of depth, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.depth.pred) ## [1] 27054.6"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"variance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Variance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Obviously point estimates abundance important, also calculate uncertainty around abundance estimates. Fortunately dsm provides functions perform calculations display resulting uncertainty estimates. two approaches estimating uncertainty abundance estimate dsm. dsm_var_gam assumes spatial model detection function parts model independent . case squared coefficients variation model component added. dsm_var_prop takes account fact detection probability may correlated spatial part model. uses methods described (Bravington, Miller, & Hedley, 2021). dsm_var_prop can applied covariate detection function varies level segments, recorded segment (example Beaufort). don’t situation , opt dsm_var_gam. methods estimate variance abundance element list provided pred.data. case wish obtain abundance prediction cells, use split chop data set list elements give dsm_var_gam (dsm_var_prop). Calling summary give information uncertainty estimation: can also make plot CVs (transect lines observations overlaid) using following code.  Note increase CV away transect lines. can revisit model included depth location smooths observe coefficient variation model larger model location smooth.","code":"preddata.var <- split(preddata, 1:nrow(preddata)) dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,                           off.set=preddata$area) summary(dsm.xy.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 15252.81 28430.76 52994.04  ## (Using log-Normal approximation) ##  ## Point estimate                 : 28430.76  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.164  ## Total standard error           : 9265.663  ## Total coefficient of variation : 0.3259 preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries CV <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, uncertainty (CV)\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) CV dsm.xy.depth.var <- dsm_var_gam(dsm.xy.depth, pred.data=preddata.var,                                 off.set=preddata$area) summary(dsm.xy.depth.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 14377.57 27054.60 50909.23  ## (Using log-Normal approximation) ##  ## Point estimate                 : 27054.6  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.1741  ## Total standard error           : 8958.444  ## Total coefficient of variation : 0.3311"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"conclusions","dir":"Articles > Lines_gomex","previous_headings":"","what":"Conclusions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"document outlined analysis spatially-explicit distance sampling data using dsm package. Note many possible models can fitted using dsm aim show just options. Results models can rather different, care must taken performing model selection, discrimination criticism.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"software","dir":"Articles > Lines_gomex","previous_headings":"","what":"Software","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Distance available http://github.com/DistanceDevelopment/Distance well CRAN. dsm available http://github.com/DistanceDevelopment/dsm, well CRAN.","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"David L. Miller. Author. Eric Rexstad. Contributor. Louise Burt. Contributor. Mark V. Bravington. Contributor. Sharon Hedley. Contributor. Megan Ferguson. Contributor. Natalie Kelly. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller DL (2024). dsm: Density Surface Modelling Distance Sampling Data. R package version 2.3.3.9001, https://github.com/DistanceDevelopment/dsm.","code":"@Manual{,   title = {dsm: Density Surface Modelling of Distance Sampling Data},   author = {David L. Miller},   year = {2024},   note = {R package version 2.3.3.9001},   url = {https://github.com/DistanceDevelopment/dsm}, }"},{"path":[]},{"path":"/index.html","id":"what-is-dsm","dir":"","previous_headings":"","what":"Density Surface Modelling of Distance Sampling Data","title":"Density Surface Modelling of Distance Sampling Data","text":"dsm provides Generalized Additive Model (GAM)-based approach calculate spatially-explicit estimates animal abundance distance sampling (also presence/absence strip transect) data. Several utility functions provided model checking, plotting variance estimation. Open Access paper describing methods: Miller, DL, ML Burt, EA Rexstad L Thomas (2013). Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution.","code":""},{"path":"/index.html","id":"using-dsm","dir":"","previous_headings":"","what":"Using dsm","title":"Density Surface Modelling of Distance Sampling Data","text":"Examples use dsm distance sampling R packages available examples.distancesampling.org.","code":""},{"path":"/reference/block.info.per.su.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the block information — block.info.per.su","title":"Find the block information — block.info.per.su","text":"Takes transect data works many blocks given size (segment terms) fit .","code":""},{"path":"/reference/block.info.per.su.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the block information — block.info.per.su","text":"","code":"block.info.per.su(block.size, data, name.su)"},{"path":"/reference/block.info.per.su.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the block information — block.info.per.su","text":"block.size number segments per block data data used build model name.su names sampling units (.e., transects)","code":""},{"path":"/reference/block.info.per.su.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the block information — block.info.per.su","text":"data.frame following columns name        sample unit name (e.g. transect label) num.seg     number segments transect num.block   number blocks available start.block block number first block end.block   block number last block num.req     number blocks needed unit","code":""},{"path":"/reference/check.cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names exist — check.cols","title":"Check column names exist — check.cols","text":"Internal function check supplied data.frames correct columns checks sample labels unique.","code":""},{"path":"/reference/check.cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names exist — check.cols","text":"","code":"check.cols(ddf.obj, segment.data, observation.data, segment.area)"},{"path":"/reference/check.cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names exist — check.cols","text":"ddf.obj ddf object mrds segment.data segment data defined dsm observation.data observation data defined dsm segment.area area segments","code":""},{"path":"/reference/check.cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names exist — check.cols","text":"nothing, throws error something went wrong","code":""},{"path":"/reference/check.cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check column names exist — check.cols","text":"David Lawrence Miller","code":""},{"path":"/reference/dsm-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Data format for DSM — dsm-data","title":"Data format for DSM — dsm-data","text":"Two data.frames must provided dsm. referred observation.data segment.data.","code":""},{"path":"/reference/dsm-data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data format for DSM — dsm-data","text":"segment.data table sample identifiers define segments, corresponding effort (line length) expended environmental covariates used model abundance/density. observation.data provides link table observations used detection function samples (segments), can aggregate observations segments (.e., observation.data \"look-table\" observations segments). observation.data - observation data.frame must (least) following columns: object unique object identifier Sample.Label identifier segment observation occurred size size observed group (e.g., 1 animals occurred individually) distance distance observation One can often also use observation.data fit detection function (additional columns detection function covariates allowed table). segment.data: segment data.frame must (least) following columns: Effort effort (terms length segment) Sample.Label identifier segment (unique!) ??? environmental covariates, example location (projected latitude longitude), relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).","code":""},{"path":"/reference/dsm-data.html","id":"multiple-detection-functions","dir":"Reference","previous_headings":"","what":"Multiple detection functions","title":"Data format for DSM — dsm-data","text":"multiple detection functions used, column named ddfobj must included observation.data segment.data. lets model know detection function observation . numeric ordered ddf.obj argument dsm, e.g., ddf.obj=list(ship_ddf, aerial_ddf) means ship detections ddfobj=1 aerial detections ddfobj=2 observation data.","code":""},{"path":"/reference/dsm-data.html","id":"mark-recapture-distance-sampling-models","dir":"Reference","previous_headings":"","what":"Mark-recapture distance sampling models","title":"Data format for DSM — dsm-data","text":"using mrds models include mark-recapture components (currently independent observer trial modes supported) format observation data needs checked ensure observations duplicated. observer column also required observation.data. Independent observer mode unique observations (unique object IDs) required. Trial mode observations made observer 1 required.","code":""},{"path":"/reference/dsm-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Density surface modelling — dsm-package","title":"Density surface modelling — dsm-package","text":"dsm implements spatial models distance sampling data. Models detectability can fitted using packages mrds Distance. dsm fits generalized additive models spatially-referenced data. See Miller et al (2013) introduction.","code":""},{"path":"/reference/dsm-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density surface modelling — dsm-package","text":"information distance sampling methods example code available http://distancesampling.org/R/. help distance sampling package, Google Group https://groups.google.com/forum/#!forum/distance-sampling. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density surface modelling — dsm-package","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm.cor","title":"Check for autocorrelation in residuals — dsm.cor","text":"function deprecated, use dsm_cor.","code":""},{"path":"/reference/dsm.cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm.cor","text":"","code":"dsm.cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm.cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm.cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Fits density surface model (DSM) detection adjusted counts spatially-referenced distance sampling analysis. dsm takes observations animals, allocates segments line (strip transects) optionally adjusts counts based detectability using supplied detection function model. generalized additive model, generalized mixed model generalized linear model used model adjusted counts based formula involving environmental covariates.","code":""},{"path":"/reference/dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"dsm(   formula,   ddf.obj,   segment.data,   observation.data,   engine = \"gam\",   convert.units = 1,   family = quasipoisson(link = \"log\"),   group = FALSE,   control = list(keepData = TRUE),   availability = 1,   segment.area = NULL,   weights = NULL,   method = \"REML\",   ... )"},{"path":"/reference/dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"formula formula surface. valid formula. See \"Details\", , define response. ddf.obj result call ddf ds. multiple detection functions required list can provided. strip/circle transects assumed objects observed, see dummy_ddf. Mark-recapture distance sampling (mrds) models type io (independent observers) trial allowed. segment.data segment data, see dsm-data. observation.data observation data, see dsm-data. engine fitting engine used DSM (\"glm\"/\"gam\"/\"gamm\"/\"bam\"). convert.units conversion factor multiply area segments . See 'Units' . family response distribution (popular choices include quasipoisson, Tweedie/tw negbin/nb). Defaults quasipoisson. group TRUE abundance groups calculated rather abundance individuals. Setting option TRUE equivalent setting size group 1. control usual control argument gam; keepData must TRUE variance estimation work (though option set GLMs GAMMs). availability estimate availability bias. count models used multiply effective strip width (must vector length 1 length number rows segment.data); estimated abundance/estimated density models used scale response (must vector length 1 length number rows observation.data). Uncertainty availability handled present. segment.area NULL (default) segment areas calculated multiplying Effort column segment.data (right minus left) truncation distance ddf.obj strip.width. Alternatively vector segment areas can provided (must length number rows segment.data) character string giving name column segment.data contains areas. segment.area specified takes precedent. weights weights observation used model fitting. default, weights=NULL, weights observation area (see Details). Setting scalar value (e.g., weights=1) observations equally weighted. method smoothing parameter estimation method. Default \"REML\", using Restricted Maximum Likelihood. See gam options. Ignored engine=\"glm\". ... anything else passed straight glm, gam, gamm bam.","code":""},{"path":"/reference/dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"glm, gam, gamm bam object, additional element, $ddf holds detection function object.","code":""},{"path":"/reference/dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"response (LHS formula) can one following (restrictions outlined ): count count segment abundance.est estimated abundance per segment, estimation via Horvitz-Thompson estimator density.est density per segment offset used model dependent response: count area segment multiplied average probability detection segment abundance.est area segment density zero count response can used detection function covariates vary segments/points (within). example, weather conditions (like visibility sea state) foliage cover usually acceptable change within segment, animal sex behaviour work. abundance.est response can used covariates detection function. density case, observations can weighted segment areas via weights= argument. default (weights=NULL), density estimated weights set segment areas (using segment.area calculated detection function object metadata Effort data). Alternatively weights=1 set weights equal. third alternative pass vector length equal number segments, containing appropriate weights. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"often case distances collected metres segment lengths recorded kilometres. dsm allows provide conversion factor (convert.units) multiply areas . example: distances metres segment lengths kilometres setting convert.units=1000 lead analysis metres. Setting convert.units=1/1000 lead analysis kilometres. conversion factor applied segment.area specified.","code":""},{"path":"/reference/dsm.html","id":"large-models","dir":"Reference","previous_headings":"","what":"Large models","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"large models, engine=\"bam\" method=\"fREML\" may useful. Models specified bam gam. Read bam using option; option considered EXPERIMENTAL moment. particular note default basis choice (thin plate regression splines) slow general fitting less stable using gam. negative binomial response, theta must specified using bam.","code":""},{"path":"/reference/dsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"David L. Miller","code":""},{"path":"/reference/dsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) summary(mod1)  # predict over a grid mod1.pred <- predict(mod1, preddata, preddata$area)  # calculate the predicted abundance over the grid sum(mod1.pred)  # plot the smooth plot(mod1) }"},{"path":"/reference/dsm.var.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm.var.gam","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"function deprecated, use dsm_var_gam.","code":""},{"path":"/reference/dsm.var.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"","code":"dsm.var.gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm.var.movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"function deprecated, use dsm_var_movblk.","code":""},{"path":"/reference/dsm.var.movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"","code":"dsm.var.movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm.var.movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL -- file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm.var.prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm.var.prop","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"function deprecated, use dsm_var_prop.","code":""},{"path":"/reference/dsm.var.prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"","code":"dsm.var.prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm_cor","title":"Check for autocorrelation in residuals — dsm_cor","text":"DSM fitted data, function can used check autocorrelation residuals.","code":""},{"path":"/reference/dsm_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"dsm_cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm_cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for autocorrelation in residuals — dsm_cor","text":"plot vector fun applied lags.","code":""},{"path":"/reference/dsm_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for autocorrelation in residuals — dsm_cor","text":"Within Transect.Label, segments sorted according Segment.Labels. may require time get right particular data. one multiple surveys transects revisited, example, one may want make Transect.Label unique transect-survey identifier. Neither label need included model, must just present $data field model. usually means segment data passed dsm. current iteration function plot correlations nicely, things can get function return data (assigning result object). NA values residuals correlogram calculated. usually occurs due NA values covariates (smoother fitted values ). Code like (.na(dsm.obj$data)) might helpful.","code":""},{"path":"/reference/dsm_cor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for autocorrelation in residuals — dsm_cor","text":"David L. Miller","code":""},{"path":"/reference/dsm_cor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"# \\donttest{  library(Distance) #>  #> Attaching package: 'Distance' #> The following object is masked from 'package:dsm': #>  #>     dummy_ddf #> The following object is masked from 'package:mrds': #>  #>     create.bins  library(dsm)   # load the data, see ?mexdolphins  data(mexdolphins)   # fit a model  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.418 #> No survey area information supplied, only estimating detection function.  mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) #> Warning: Some observations are outside of detection function 1 truncation!   # look at lag 1 differences up to a maximum of lag 9, using deviance  # residuals  dsm_cor(mod1, resid.type=\"deviance\", max.lag=9,          Segment.Label=\"Sample.Label\")  # }"},{"path":"/reference/dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance propagation for density surface models — dsm_varprop","title":"Variance propagation for density surface models — dsm_varprop","text":"Calculate uncertainty predictions fitted DSM, including uncertainty detection function.","code":""},{"path":"/reference/dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance propagation for density surface models — dsm_varprop","text":"","code":"dsm_varprop(   model,   newdata = NULL,   trace = FALSE,   var.type = \"Vp\",   var_type = NULL )"},{"path":"/reference/dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance propagation for density surface models — dsm_varprop","text":"model fitted dsm. newdata prediction grid. Set NULL avoid making predictions just return model objects. trace debugging, see scale parameter estimation going. var.type variance-covariance matrix used (\"Vp\" variance-covariance conditional smoothing parameter(s), \"Vc\" unconditional). See gamObject details/explanation. doubt, stick default, \"Vp\". var_type deprecated, use var.type instead.","code":""},{"path":"/reference/dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance propagation for density surface models — dsm_varprop","text":"list elements: old_model fitted model supplied function model refit refitted model object, extra term pred point estimates predictions newdata var total variance calculated newdata ses standard error prediction cell newdata newdata=NULL last three entries NA.","code":""},{"path":"/reference/dsm_varprop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance propagation for density surface models — dsm_varprop","text":"make predictions spatial model, also want know uncertainty abundance estimate. Since density surface models 2 () stage models, need incorporate uncertainty earlier stages (.e. detection function) \"final\" uncertainty estimate. function refit spatial model include Hessian offset extra term. Variance estimates using new model can used calculate variance predicted abundance estimates incorporate detection function uncertainty. Importantly requires detection function covariates, vary within segment (, example covariates like sex used). information construct prediction grid data.frame, newdata, see predict.dsm. routine useful detection function covariates used DSM. Note can use var.type=\"Vc\" (see gamObject), variance-covariance matrix spatial model, corrected smoothing parameter uncertainty. See Wood, Pya & S\\\"afken (2016) information. Models fixed scale parameters (e.g., negative binomial) require extra round optimisation.","code":""},{"path":"/reference/dsm_varprop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Variance propagation for density surface models — dsm_varprop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_varprop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance propagation for density surface models — dsm_varprop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535. Wood, S.N., Pya, N. S\\\"afken, B. (2016) Smoothing parameter model selection general smooth models. Journal American Statistical Association, 1-45.","code":""},{"path":"/reference/dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance propagation for density surface models — dsm_varprop","text":"David L. Miller, based code Mark V. Bravington Sharon L. Hedley.","code":""},{"path":"/reference/dsm_var_gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm_var_gam","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"one willing assume detection function spatial model independent, function produce estimates variance predictions abundance, using result squared coefficients variation add.","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"dsm_var_gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"list elements model fitted model object pred.var variance regions given pred.data. bootstrap logical, always FALSE model fitted model extra term dsm.object original model (dsm.obj )","code":""},{"path":"/reference/dsm_var_gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"David L. Miller","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"if (FALSE) {  library(Distance)  library(dsm)   # load the Gulf of Mexico dolphin data (see ?mexdolphins)  data(mexdolphins)   # fit a detection function and look at the summary  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL)  summary(hr.model)   # fit a simple smooth of x and y  mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)   # Calculate the variance  # this will give a summary over the whole area in mexdolphins$preddata  mod1.var <- dsm_var_gam(mod1, preddata, off.set=preddata$area) }"},{"path":"/reference/dsm_var_movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Estimate variance abundance area using moving block bootstrap. Two procedures implemented, one incorporating detection function uncertainty, one .","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"dsm_var_movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm_var_movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL -- file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm_var_movblk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Setting ds.uncertainty=TRUE incorporate detection function uncertainty directly bootstrap. done generating observations fitted detection function re-fitting new detection function (form), calculating new effective strip width. Rejection sampling used generate observations (except half-normal case) procedure can rather slow. Note currently supported covariates detection function. Setting ds.uncertainty=FALSE incorporate detection function uncertainty using delta method. assumes detection function spatial model INDEPENDENT. probably reasonable.","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata) summary(mod1)  # calculate the variance by 500 moving block bootstraps mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,    block.size = 3, samp.unit.name = \"Transect.Label\",    off.set = preddata$area,    bar = TRUE, bs.file = \"mexico-bs.csv\", ds.uncertainty = TRUE) }"},{"path":"/reference/dsm_var_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm_var_prop","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"ensure uncertainty detection function correctly propagated final variance estimate abundance, function uses method first detailed Williams et al (2011), explanation given Bravington et al. (2021).","code":""},{"path":"/reference/dsm_var_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"","code":"dsm_var_prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"list elements model fitted model object pred.var variance region given pred.data bootstrap logical, always FALSE pred.data .set model fitted model extra term dsm.object original model, model.check simple check subtracting coefficients two models see large difference deriv numerically calculated Hessian offset","code":""},{"path":"/reference/dsm_var_prop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"idea refit spatial model including extra random effect. random effect zero mean hence effect point estimates. variance Hessian detection function. Variance estimates incorporate detection function uncertainty. mathematical details given paper references . Many prediction grids can supplied supplying list data.frames function. Note routine simply calls dsm_varprop. require multiple prediction grids, routine probably faster. routine useful detection function covariates used DSM.","code":""},{"path":"/reference/dsm_var_prop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_var_prop.html","id":"limitations","dir":"Reference","previous_headings":"","what":"Limitations","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Note routine useful detection function used DSM. used abundance.est density.est responses used. Importantly requires detection function covariates, vary within segment (, example covariates like sex used).","code":""},{"path":"/reference/dsm_var_prop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535.","code":""},{"path":"/reference/dsm_var_prop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Mark V. Bravington, Sharon L. Hedley. Bugs added David L. Miller.","code":""},{"path":"/reference/dummy_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection function objects when detection is certain — dummy_ddf","title":"Detection function objects when detection is certain — dummy_ddf","text":"Create detection function object strip/plot surveys use density surface models.","code":""},{"path":"/reference/dummy_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection function objects when detection is certain — dummy_ddf","text":"","code":"dummy_ddf(object, size = 1, width, left = 0, transect = \"line\")"},{"path":"/reference/dummy_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection function objects when detection is certain — dummy_ddf","text":"object numeric vector object identifiers, relating object field observation data DSM. size group size observation (default groups size 1) width right truncation left left truncation (default 0, left truncation) transect \"line\" \"point\" transect","code":""},{"path":"/reference/dummy_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection function objects when detection is certain — dummy_ddf","text":"David L Miller","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data from a fitted detection function — generate.ds.uncertainty","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"using dsm.var.movblk ds.uncertainty=TRUE, procedure generates data fitted detection function (assuming correct).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"","code":"generate.ds.uncertainty(ds.object)"},{"path":"/reference/generate.ds.uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"ds.object fitted detection function object (returned call ddf.ds).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"function changes random number generator seed. avoid potential side-effects, use something like: seed <- get(\".Random.seed\",envir=.GlobalEnv) running code assign(\".Random.seed\",seed,envir=.GlobalEnv) .","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"David L. Miller","code":""},{"path":"/reference/generate.mb.sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Moving block bootstrap sampler — generate.mb.sample","title":"Moving block bootstrap sampler — generate.mb.sample","text":"usually used , called within dsm.var.movblk.","code":""},{"path":"/reference/generate.mb.sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Moving block bootstrap sampler — generate.mb.sample","text":"","code":"generate.mb.sample(   num.blocks.required,   block.size,   which.blocks,   dsm.data,   unit.info,   n.units )"},{"path":"/reference/generate.mb.sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Moving block bootstrap sampler — generate.mb.sample","text":"num.blocks.required number blocks need. block.size number segments per block. .blocks blocks sampled. dsm.data $data element result call dsm. unit.info result calling block.info.per.su. n.units number sampling units.","code":""},{"path":"/reference/generate.mb.sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Moving block bootstrap sampler — generate.mb.sample","text":"vector log-residuals","code":""},{"path":"/reference/latlong2km.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert latitude and longitude to Northings and Eastings — latlong2km","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Convert longitude latitude co-ordinates kilometres west-east south-north axes (lon0,lat0) using \"spherical law cosines\".","code":""},{"path":"/reference/latlong2km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"","code":"latlong2km(lon, lat, lon0 = sum(range(lon))/2, lat0 = sum(range(lat))/2)"},{"path":"/reference/latlong2km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"lon longitude lat latitude lon0 longitude reference point (defaults mean longitude) lat0 latitude reference point (defaults mean latitude)","code":""},{"path":"/reference/latlong2km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"list elements km.e km.n.","code":""},{"path":"/reference/latlong2km.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"WARNING: approximate procedure converting latitude/ longitude Northing/Easting. Consider using projection conversions available packages sp, sf rgdal better results.","code":""},{"path":"/reference/latlong2km.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Simon N. Wood","code":""},{"path":"/reference/make.soapgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"routine simply creates grid knots (correct format) used \"internal\" part soap film smoother","code":""},{"path":"/reference/make.soapgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"","code":"make.soapgrid(bnd, n.grid)"},{"path":"/reference/make.soapgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"bnd list elements x y give locations boundary vertices. first last elements . n.grid either one number giving number points along x y axes used create grid, vector giving number x direction, y direction.","code":""},{"path":"/reference/make.soapgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"list elements x y, containing knot locations.","code":""},{"path":"/reference/make.soapgrid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"David L Miller","code":""},{"path":"/reference/mexdolphins.html","id":null,"dir":"Reference","previous_headings":"","what":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Data combination several NOAA shipboard surveys conducted pan-tropical spotted dolphins Gulf Mexico. data consist 47 observations groups dolphins. group size recorded, well Beaufort sea state time observation. Coordinates observation bathymetry data also available covariates analysis. complete example analysis (description data) provided http://distancesampling.org/R/vignettes/mexico-analysis.html.","code":""},{"path":"/reference/mexdolphins.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Halpin, P.N., .J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, . Dimatteo, J. Cleary, C. Good, L.B. Crowder, K.D. Hyrenbach. 2009. OBIS-SEAMAP: world data center marine mammal, sea bird, sea turtle distributions. Oceanography 22(2):104-115 NOAA Southeast Fisheries Science Center. 1996. Report Cetacean Survey Oceanic Selected Continental Shelf Waters Northern Gulf Mexico aboard NOAA Ship Oregon II (Cruise 220)","code":""},{"path":"/reference/obs_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Observed versus expected diagnostics for fitted DSMs — obs_exp","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"Given covariate, calculate observed expected counts unique value covariate. can useful goodness fit check DSMs.","code":""},{"path":"/reference/obs_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"obs_exp(model, covar, cut = NULL)"},{"path":"/reference/obs_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"model fitted dsm model object covar covariate aggregate (character) cut vector cut points aggregate . supplied, unique values covar used.","code":""},{"path":"/reference/obs_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"data.frame values observed expected counts.","code":""},{"path":"/reference/obs_exp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"One strategy model checking calculate observed expected counts different aggregations variable. match well model fit good.","code":""},{"path":"/reference/obs_exp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"David L Miller, suggestion Mark Bravington.","code":""},{"path":"/reference/obs_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # example with the Gulf of Mexico dolphin data data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) }"},{"path":"/reference/plot.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a density surface model. — plot.dsm","title":"Plot a density surface model. — plot.dsm","text":"See plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a density surface model. — plot.dsm","text":"","code":"# S3 method for dsm plot(x, ...)"},{"path":"/reference/plot.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a density surface model. — plot.dsm","text":"x model fitted dsm ... arguments passed plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a density surface model. — plot.dsm","text":"plot!","code":""},{"path":[]},{"path":"/reference/plot.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a density surface model. — plot.dsm","text":"David L. Miller","code":""},{"path":"/reference/plot.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Create plots of abundance uncertainty — plot.dsm.var","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"Note prediction data set must x y columns even used model.","code":""},{"path":"/reference/plot.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"","code":"# S3 method for dsm.var plot(   x,   poly = NULL,   limits = NULL,   breaks = NULL,   legend.breaks = NULL,   xlab = \"x\",   ylab = \"y\",   observations = TRUE,   plot = TRUE,   boxplot.coef = 1.5,   x.name = \"x\",   y.name = \"y\",   gg.grad = NULL,   ... )"},{"path":"/reference/plot.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"x dsm.var object poly list data.frame columns x y, gives coordinates polygon draw. may also optionally column group, many polygons. limits limits fill colours breaks breaks colour fill legend.breaks breaks displayed xlab label x axis ylab label y axis observations observations plotted? plot actually plot map, just return ggplot2 object? boxplot.coef control trimming (summary.dsm.var), effect bootstrap file saved. x.name name variable plot x axis. y.name name variable plot y axis. gg.grad optional ggplot gradient object. ... arguments","code":""},{"path":"/reference/plot.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"plot","code":""},{"path":"/reference/plot.dsm.var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"order get plotting work dsm_var_prop dsm_var_gam, one must first format data correctly since functions designed compute general summaries. One summary calculated element list pred supplied dsm_var_prop dsm_var_gam. plot uncertainty prediction grid, pred (data.frame), say, can create correct format simply using pred.new <- split(pred,1:nrow(pred)).","code":""},{"path":[]},{"path":"/reference/plot.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/plot_pred_by_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially plot predictions per model term — plot_pred_by_term","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"Plot effect smooth model spatially. term model, plot effect space. Plots made scale, relative influence smooth can seen.","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"plot_pred_by_term(dsm.obj, data, location.cov = c(\"x\", \"y\"))"},{"path":"/reference/plot_pred_by_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"dsm.obj fitted dsm object data data use plot (often prediction grid), data also include width height columns plotting location.cov deprecated, use location.cov","code":""},{"path":"/reference/plot_pred_by_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"ggplot2 plot","code":""},{"path":"/reference/plot_pred_by_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"David L Miller (idea taken inlabru)","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data and fit a model data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y) + s(depth), hr.model, segdata, obsdata)  preddata$width <- preddata$height <- sqrt(preddata$area)  # make the plot plot_pred_by_term(mod1, preddata, c(\"x\",\"y\"))  # better plot would be # library(viridis) # plot_pred_by_term(mod1, preddata, c(\"x\",\"y\")) + scale_fill_viridis() }"},{"path":"/reference/predict.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from a fitted density surface model — predict.dsm","title":"Predict from a fitted density surface model — predict.dsm","text":"Make predictions density abundance outside (inside) covered area.","code":""},{"path":"/reference/predict.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from a fitted density surface model — predict.dsm","text":"","code":"# S3 method for dsm predict(object, newdata = NULL, off.set = NULL, type = \"response\", ...)"},{"path":"/reference/predict.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from a fitted density surface model — predict.dsm","text":"object fitted dsm object newdata spatially referenced covariates e.g. altitude, depth, distance shore, etc. Covariates data.frame must names identical variable names used fitting model .set area cells prediction grid. units segments/distances given dsm. Replaces column newdata called .set supplied. Ignored newdata supplied type scale results . default \"response\", see predict.gam explanation options (usually necessary) ... arguments passed predict.gam","code":""},{"path":"/reference/predict.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from a fitted density surface model — predict.dsm","text":"predicted values response scale default (unless type specified, case see predict.gam).","code":""},{"path":"/reference/predict.dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict from a fitted density surface model — predict.dsm","text":"newdata supplied, predictions made data built model. Note order results necessarily segdata (segment data) data.frame supplied dsm. area .set used argument supplied, otherwise look areas column named .set newdata. Either way link function (usually log) applied offsets, need log passing function.","code":""},{"path":[]},{"path":"/reference/predict.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict from a fitted density surface model — predict.dsm","text":"David L. Miller","code":""},{"path":"/reference/predict.fake_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for fake detection functions — predict.fake_ddf","title":"Prediction for fake detection functions — predict.fake_ddf","text":"Prediction function dummy detection functions. function returns many 1s rows newdata. esw=TRUE strip width returned.","code":""},{"path":"/reference/predict.fake_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for fake detection functions — predict.fake_ddf","text":"","code":"# S3 method for fake_ddf predict(   object,   newdata = NULL,   compute = FALSE,   int.range = NULL,   esw = FALSE,   ... )"},{"path":"/reference/predict.fake_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for fake detection functions — predict.fake_ddf","text":"object model object newdata many 1s return? compute unused, compatibility mrds::predict int.range unused, compatibility mrds::predict esw strip width returned? ... S3 consistency","code":""},{"path":"/reference/predict.fake_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction for fake detection functions — predict.fake_ddf","text":"David L Miller","code":""},{"path":"/reference/print.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model object — print.dsm","title":"Print a description of a density surface model object — print.dsm","text":"method just gives short description fitted model. Use summary.dsm method information.","code":""},{"path":"/reference/print.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model object — print.dsm","text":"","code":"# S3 method for dsm print(x, ...)"},{"path":"/reference/print.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model object — print.dsm","text":"x model fitted dsm ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model object — print.dsm","text":"NULL","code":""},{"path":"/reference/print.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model object — print.dsm","text":"David L. Miller","code":""},{"path":"/reference/print.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm.var","title":"Print a description of a density surface model variance object — print.dsm.var","text":"method provides short summary, use summary.dsm.var method information.","code":""},{"path":"/reference/print.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm.var","text":"","code":"# S3 method for dsm.var print(x, ...)"},{"path":"/reference/print.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm.var","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model variance object — print.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm_varprop","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"method provides short summary, see summary.dsm_varprop.","code":""},{"path":"/reference/print.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"","code":"# S3 method for dsm_varprop print(x, ...)"},{"path":"/reference/print.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":[]},{"path":"/reference/print.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm.var","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"See summary.dsm.var information.","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"","code":"# S3 method for summary.dsm.var print(x, ...)"},{"path":"/reference/print.summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm_varprop","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"See summary.dsm_varprop information.","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"","code":"# S3 method for summary.dsm_varprop print(x, ...)"},{"path":"/reference/print.summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/rqgam.check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"function deprecated, use rqgam_check.","code":""},{"path":"/reference/rqgam.check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"","code":"rqgam.check(gam.obj, ...)"},{"path":"/reference/rqgam.check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Reproduces \"Resids vs. linear pred\" plot gam.check using randomised quantile residuals, la Dunn Smyth (1996). Checks heteroskedasticity usual, looking \"funnel\"-type structures points, much easier randomised quantile residuals deviance residuals, model uses count distribution response.","code":""},{"path":"/reference/rqgam_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"rqgam_check(gam.obj, ...)"},{"path":"/reference/rqgam_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"just plots!","code":""},{"path":"/reference/rqgam_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Note function works negative binomial Tweedie response distributions. Earlier versions function produced full gam.check output, confusing one plots really useful.  Checks k computed, need done using gam.check.","code":""},{"path":"/reference/rqgam_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Based code Natalie Kelly, bugs added Dave Miller","code":""},{"path":"/reference/rqgam_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"# \\donttest{ library(Distance) library(dsm) library(tweedie)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.418 #> No survey area information supplied, only estimating detection function.  # fit a simple smooth of x and y with a Tweedie response with estimated #  p parameter mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw()) #> Warning: Some observations are outside of detection function 1 truncation! rqgam_check(mod1)  # }"},{"path":"/reference/summary.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted density surface model — summary.dsm","title":"Summarize a fitted density surface model — summary.dsm","text":"Gives brief summary fitted dsm object.","code":""},{"path":"/reference/summary.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted density surface model — summary.dsm","text":"","code":"# S3 method for dsm summary(object, ...)"},{"path":"/reference/summary.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted density surface model — summary.dsm","text":"object model fitted dsm ... arguments passed summary.gam","code":""},{"path":"/reference/summary.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted density surface model — summary.dsm","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize a fitted density surface model — summary.dsm","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm.var","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"Gives brief summary fitted dsm variance object.","code":""},{"path":"/reference/summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"","code":"# S3 method for dsm.var summary(   object,   alpha = 0.05,   boxplot.coef = 1.5,   bootstrap.subregions = NULL,   ... )"},{"path":"/reference/summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95\\ confidence intervals) boxplot.coef value coef used calculate outliers see boxplot. bootstrap.subregions list vectors logicals indices subregions variances need calculated (bootstraps (see dsm.var.prop use subregions variance propagation). ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm_varprop","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"Gives brief summary fitted dsm_varprop variance object.","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"","code":"# S3 method for dsm_varprop summary(object, alpha = 0.05, ...)"},{"path":"/reference/summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95% confidence internal) ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/trim.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimmed variance — trim.var","title":"Trimmed variance — trim.var","text":"Trim variance estimates bootstrap. defined percentage defined amount necessary bring median trimmed mean within 8% defined 'outliers'.","code":""},{"path":"/reference/trim.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimmed variance — trim.var","text":"","code":"trim.var(untrimmed.bootstraps, boxplot.coef = 1.5)"},{"path":"/reference/trim.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimmed variance — trim.var","text":"untrimmed.bootstraps (usually $study.area.total element returned dsm.var.movblk bootstrap object. boxplot.coef value coef used calculate outliers see boxplot.","code":""},{"path":"/reference/trim.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimmed variance — trim.var","text":"trimmed variance","code":""},{"path":"/reference/trim.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trimmed variance — trim.var","text":"Louise Burt","code":""},{"path":"/reference/vis.concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis.concurvity","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"function deprecated, use vis_concurvity.","code":""},{"path":"/reference/vis.concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"","code":"vis.concurvity(model, type = \"estimate\")"},{"path":"/reference/vis.concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis_concurvity","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"Plot measures much one term model explained another. values high, one consider re-running variable selection one offending variables removed check stability term selection.","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"vis_concurvity(model, type = \"estimate\")"},{"path":"/reference/vis_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"methods considered somewhat experimental time. Consult concurvity information concurvity measures calculated.","code":""},{"path":"/reference/vis_concurvity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"David L Miller","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata)  # visualise concurvity using the \"estimate\" metric vis_concurvity(mod1) }"},{"path":"/news/index.html","id":"dsm-234","dir":"Changelog","previous_headings":"","what":"dsm 2.3.4","title":"dsm 2.3.4","text":"fix bug predictions NA (e.g., predictions outside soap boundary), variances NA due propagation (#4)","code":""},{"path":"/news/index.html","id":"dsm-233","dir":"Changelog","previous_headings":"","what":"dsm 2.3.3","title":"dsm 2.3.3","text":"CRAN release: 2022-08-20 dsm.var.prop -> dsm_var_prop dsm.var.gam -> dsm_var_gam dsm.var.movblk -> dsm_var_movblk dsm.cor -> dsm_cor rqgam.check -> rqgam_check vis.concurvity -> vis_concurvity var_type -> var.type","code":""},{"path":"/news/index.html","id":"dsm-232","dir":"Changelog","previous_headings":"","what":"dsm 2.3.2","title":"dsm 2.3.2","text":"CRAN release: 2022-03-17 fixed bug offset calculation dsm_varprop se estimation offsets logged twice. Thanks Megan Ferguson spotting . documentation now rmarkdown format dsm 2.3.1 transect type (line point) now determined detection function rather using transect= argument multiple detection functions now possible, use list() fitted detection functions supplied ddf.obj= argument dsm(). dsm.var.gam, dsm.var.prop dsm_varprop compatible. Vignette coming soon. Thanks Dave Fifield Ewan Wakefield extensive testing suggestions. See ?“dsm-data” information set data. strip transects now handled using dummy_ddf() function. presence~ response type removed Response options “D”, “density”, “Dhat”, “N”, “Nhat” “n” deprecated. Now “count”, “abundance.est” “density.est” allowed. Make predict.dsm work tibbles .set column newdata (error thrown previous code’s check ). Thanks Dave Fifield reporting issue. Fix bug inconsistent predictions density.est used response. Code predict.dsm simplified documentation updated. Thanks Dave Fifield reporting issue. mrds independent observer (io) trial models allowed detection function.Variance can estimated via dsm_varprop (usual limitation covariates may vary segment level) dsm.var.gam. feature currently experimental. Thanks Doug Sigourney Megan Ferguson help suggestions. See ?“dsm-data” information set data. example multiddf mrds analysis dsm https://github.com/densitymodelling/nefsc_fin_mrds_dsm availability= argument now works differently count models. availability must now length number segments (nrow(segment.data)) multiply offset (effective area) rather number animals observed coherent detectability handled case. dsm_varprop longer requies newdata supplied. newdata isn’t present just refitted original models returned, entries NA.","code":""},{"path":"/news/index.html","id":"dsm-230","dir":"Changelog","previous_headings":"","what":"dsm 2.3.0","title":"dsm 2.3.0","text":"CRAN release: 2020-04-22 fixed dsm_varprop (gam.fixed.priors) response distributions fixed scale parameters. Negative binomial results now may incorrect!","code":""},{"path":"/news/index.html","id":"dsm-2218","dir":"Changelog","previous_headings":"","what":"dsm 2.2.18","title":"dsm 2.2.18","text":"Clarification predict.dsm return documentation. Thanks Dave Fifield suggestion. .set=NULL lead predictions 0 density used. Now just returns density. Thanks Dave Fifield catch.","code":""},{"path":"/news/index.html","id":"dsm-2217","dir":"Changelog","previous_headings":"","what":"dsm 2.2.17","title":"dsm 2.2.17","text":"CRAN release: 2019-01-19 Now Suggests package “sp” (needed parts mexdolphin example data)","code":""},{"path":"/news/index.html","id":"dsm-2216","dir":"Changelog","previous_headings":"","what":"dsm 2.2.16","title":"dsm 2.2.16","text":"CRAN release: 2018-06-27 check Sample.Labels match , none throw error. Thanks Madhura Davate bringing issue attention. gamma option removed, since now ignored gam(). using method=“GCV.Cp” gamma=1.4 still used (generally advise using REML rather GCV smoothing parameter selection) dsm now requires mgcv version 1.8-23 higher. dsm.var.prop (dsm_varprop) now throw error covariates detection function can’t anything useful new function obs_exp diagnostic function compare observed expected counts different covariate aggregations","code":""},{"path":"/news/index.html","id":"dsm-2215","dir":"Changelog","previous_headings":"","what":"dsm 2.2.15","title":"dsm 2.2.15","text":"CRAN release: 2017-07-03 mexdolphins loaded using data(), data longer need attach()ed dsm.var.prop throws error count N used response. dsm.var.prop now calls dsm_varprop, keeps scale parameter constant refitting models variance propagation. dsm_varprop provides lower-level interface variance propagation probably faster flexible. add vis.concurvity visually assess concurvity (colinearity extended smooth terms) model new model diagnostic function: plot_pred_by_term – plots effect smooth model spatially. remove “abundance” option response, use “count” dsm.var.prop dsm_varprop better diagnostics checking variance model hasn’t done something weird dsm.var.movblk discards replicates model didn’t converge fix potential issue negative binomial response used variance propagation (due scale parameterisations). dsm_varprop now refits nb() models negbin() advise users look deeply. Thanks Eric Rexstad spotting . fix bug predict.dsm() predictions “density” response including offset. Thanks Len Thomas spotting .","code":""},{"path":"/news/index.html","id":"dsm-2214","dir":"Changelog","previous_headings":"","what":"dsm 2.2.14","title":"dsm 2.2.14","text":"CRAN release: 2017-01-26 nasty bug predict.dsm fixed: logging offset twice newdata provided, leading odd checking plots. also effected results predict() newdata= specified. Thanks Megan Ferguson alerting us . add support point transects(!) rqgam.check() now produced linear predictor vs. randomised quantile residuals plot avoid confusion interpreting plots REML now default method smoothing parameter selection","code":""},{"path":"/news/index.html","id":"dsm-2213","dir":"Changelog","previous_headings":"","what":"dsm 2.2.13","title":"dsm 2.2.13","text":"CRAN release: 2016-10-03 dsm.var.gam now example dsm.var.prop now checks detection function model covariates","code":""},{"path":"/news/index.html","id":"dsm-2212","dir":"Changelog","previous_headings":"","what":"dsm 2.2.12","title":"dsm 2.2.12","text":"CRAN release: 2016-08-10 alpha argument now works analytical variance reported using dsm.var.prop dsm.var.gam print.summary summary methods. Thanks Jason Roberts Eric Rexstad spotting issue. dsm now uses numDeriv::grad calculate derivatives variance estimation – much faster. Thanks Jason Roberts fix. dsm now requires mrds version 2.1.15 higher, due internal changes fixed issue Effort column required even segment.area specified. Thanks Greg Lollback spotting issue. ensured ordering segments matched ordering observations aggregation observations correct. Thanks Dave Fifield reporting bug suggesting fix. check.cols longer checks see column called “distance” strip transects used","code":""},{"path":"/news/index.html","id":"dsm-2211","dir":"Changelog","previous_headings":"","what":"dsm 2.2.11","title":"dsm 2.2.11","text":"dsm.var.prop uses update() rather messing around eval() print.summary.dsm.var didn’t work GAM variance detection function specified, now fixed (thanks Natalia Dellabianca spotting ) Removed non-working plot() code variance examples – see Mexico pantropical spotted dolphins proper treatment plotting using projections (http://distancesampling.org/R/vignettes/mexico-analysis.html) plot.var.gam longer crashes default distance data doesn’t columns “x” “y”","code":""},{"path":"/news/index.html","id":"dsm-2210","dir":"Changelog","previous_headings":"","what":"dsm 2.2.10","title":"dsm 2.2.10","text":"corrected offsets examples dsm, dsm.var.prop, dsm.var.movblk updated mexdolphins data lat/long reversal prediction data non-list storage stopped use rqgam.check used non-negbin/Tweedie responses (thanks Phil Bouchet highlighting issue).","code":""},{"path":"/news/index.html","id":"dsm-229","dir":"Changelog","previous_headings":"","what":"dsm 2.2.9","title":"dsm 2.2.9","text":"CRAN release: 2015-07-29 Moving block bootstrap now works detection function (suggested Laura Williamson) Mexico dolphin data areas prediction grids segments re-projected. Thanks Phil Bouchet pointing error. Context: https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ","code":""},{"path":"/news/index.html","id":"dsm-228","dir":"Changelog","previous_headings":"","what":"dsm 2.2.8","title":"dsm 2.2.8","text":"Speed reduced memory usage variance calculations, thanks Filipe Dias asking . Better error observations matched detection function. Thanks Ricardo Lima highlighting . Check missing columns incorrect logic, now corrected tests. Thanks Julia Migné spotting .","code":""},{"path":"/news/index.html","id":"dsm-227","dir":"Changelog","previous_headings":"","what":"dsm 2.2.7","title":"dsm 2.2.7","text":"Plotting check plots using random quantile residuals (rqgam.check) didn’t work Tweedie tw family models, now fixed. Thanks Laura Mannocci Jason Roberts pointing . Plotting CV used matrix() coerce data data.frame – thus converting elements simplest type. Errors ensued. Thanks Adrian Schiavini finding bug!","code":""},{"path":"/news/index.html","id":"dsm-226","dir":"Changelog","previous_headings":"","what":"dsm 2.2.6","title":"dsm 2.2.6","text":"dsm.var.prop dsm.var.gam now allow supply single number offset, copy many times need. Thanks Filipe Dias suggesting .","code":""},{"path":"/news/index.html","id":"dsm-225","dir":"Changelog","previous_headings":"","what":"dsm 2.2.5","title":"dsm 2.2.5","text":"CRAN release: 2014-09-16 dsm.var.prop now throw error detection function dsm ’s trying calculate variance (thanks Adrián Schiavini finding ) variance/CV plotting threw error detection function supplied observation=TRUE. Now error sidestepped (, thanks Adrián Schiavini finding ).","code":""},{"path":"/news/index.html","id":"dsm-224","dir":"Changelog","previous_headings":"","what":"dsm 2.2.4","title":"dsm 2.2.4","text":"CRAN release: 2014-06-17 changed stop() warning() data detection function included observation data frame, automatically excluded later, bug found Eric Rexstad Anna Cucknell","code":""},{"path":"/news/index.html","id":"dsm-223","dir":"Changelog","previous_headings":"","what":"dsm 2.2.3","title":"dsm 2.2.3","text":"CRAN release: 2014-04-23 moving block boostrap using observed abundances residuals therefore gave wrong answers (Eric Rexstad found bug)","code":""},{"path":"/news/index.html","id":"dsm-222","dir":"Changelog","previous_headings":"","what":"dsm 2.2.2","title":"dsm 2.2.2","text":"version number reporting now works (Len Thomas found bug) documentation default density presence models now weighted area segment","code":""},{"path":"/news/index.html","id":"dsm-221","dir":"Changelog","previous_headings":"","what":"dsm 2.2.1","title":"dsm 2.2.1","text":"switch new testthat spec added new response variables (Dhat, density.est, count, n), suggestion Len Thomas","code":""},{"path":"/news/index.html","id":"dsm-214","dir":"Changelog","previous_headings":"","what":"dsm 2.1.4","title":"dsm 2.1.4","text":"check observed distances within width now throw error","code":""},{"path":"/news/index.html","id":"dsm-213","dir":"Changelog","previous_headings":"","what":"dsm 2.1.3","title":"dsm 2.1.3","text":"CRAN release: 2013-08-20 CRAN-compatibility fixes","code":""},{"path":"/news/index.html","id":"dsm-212","dir":"Changelog","previous_headings":"","what":"dsm 2.1.2","title":"dsm 2.1.2","text":"updates dsm.cor unified plotting variance estimation tweaks non-dsm models","code":""},{"path":"/news/index.html","id":"dsm-206","dir":"Changelog","previous_headings":"","what":"dsm 2.0.6","title":"dsm 2.0.6","text":"removed warning () comparison length make.soapgrid need pass bnd list() dsm.cor now handles gamm objects dsm.cor allows one check residual autocorrelation models presence/absence data now longer needs dummy detection function object","code":""},{"path":"/news/index.html","id":"dsm-205","dir":"Changelog","previous_headings":"","what":"dsm 2.0.5","title":"dsm 2.0.5","text":"started NEWS file fixed bug dsm.var.gam() didn’t return predictions plotting result fail silly typo docs check ’re using mgcv version >= 1.7-24 GAMM fitting warn ","code":""}]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
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From 1d857d4f92a149ec918fd2ed3f7e6914794dc293 Mon Sep 17 00:00:00 2001
From: erex <ericincrail@gmail.com>
Date: Mon, 13 May 2024 08:15:52 +0100
Subject: [PATCH 4/6] added data formatting vignette

---
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 .../data_format/dsm-data-formatting.html      | 652 ++++++++++++
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diff --git a/.Rhistory b/.Rhistory
index b6ec966..b8207cc 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,12 +1,3 @@
-# p <- p + coord_equal()
-# p <- p + geom_line(aes(x,y,group=Transect.Label),data=segdata)
-# p <- p + gg.opts
-# print(p)
-?CRS
-?proj4string
-?make.soapgrid
-# Chunk 1
-library(knitr)
 # Chunk 2
 knitr::opts_chunk$set(eval=TRUE, echo=TRUE, message=FALSE, warnings=FALSE, progress=FALSE)
 # Chunk 3: loadlibraries
@@ -510,3 +501,12 @@ pkgdown::build_news()
 getwd()
 setwd("~/GitHub/dsm")
 pkgdown::build_news()
+pkgdown::build_site()
+pkgdown::build_site()
+pkgdown::build_site()
+pkgdown::build_site()
+pkgdown::build_site()
+pkgdown::build_site()
+pkgdown::build_site()
+pkgdown::build_site()
+pkgdown::build_site()
diff --git a/docs/404.html b/docs/404.html
index 3d990f5..b304dbf 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -39,6 +39,7 @@
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+    <a class="dropdown-item" href="articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index d9502b5..5034a3b 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -24,6 +24,7 @@
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     <a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+      <img src="" class="logo" alt=""><h1>`dsm` data formatting example</h1>
+                        <h4 data-toc-skip class="author">David L Miller</h4>
+            <address class="author_afil">
+      CREEM, University of St Andrews<br><h4 data-toc-skip class="date">May 2024</h4>
+      
+      <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/vignettes/data_format/dsm-data-formatting.Rmd" class="external-link"><code>vignettes/data_format/dsm-data-formatting.Rmd</code></a></small>
+      <div class="d-none name"><code>dsm-data-formatting.Rmd</code></div>
+    </address>
+</div>
+
+    
+    
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+<p>The <code>dsm</code> package expects data to be in a specific format. In this example I’ll show how to get data into that format and give some explanation as to <em>why</em> each of these requirements exist.</p>
+<p>I recommend getting to grips with fitting models in <code>dsm</code> before reading this tutorial so you know what the end point will look like before trying to grapple with the data. An example for <a href="http://distancedevelopment.github.io/dsm/articles/lines_gomex/mexico-analysis.html" class="external-link">Gulf of Mexico pantropical spotted dolphins is available here</a>.</p>
+</div>
+<div class="section level2">
+<h2 id="prerequisites">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
+</h2>
+<p>To run the code below, you will need to have the <code>ggplot2</code> and <code>sf</code> libraries installed. You can do this with the following code:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ggplot2"</span>, <span class="st">"sf"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<p>The Rhode Island data required downloaded below:</p>
+<ul>
+<li><a href="data/loons.csv">loon observations</a></li>
+<li><a href="data/ri_coast.dbf">coastline dbf</a></li>
+<li><a href="data/ri_coast.prj">coastline projection</a></li>
+<li><a href="data/ri_coast.shp">coastline shapefile</a></li>
+<li><a href="data/ri_coast.shx">coastline shx</a></li>
+<li><a href="data/ri_transects.dbf">transects dbf</a></li>
+<li><a href="data/ri_transects.prj">transects projection</a></li>
+<li><a href="data/ri_transects.shp">transects shapefile</a></li>
+<li><a href="data/ri_transects.shx">transects shx</a></li>
+</ul>
+<p>These should be placed in a folder called <code>data</code> for the code below to work.</p>
+</div>
+<div class="section level2">
+<h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"></a>
+</h2>
+<p>When we collect distance sampling data, we need to record (1) the distance to an observation (along with other detectability covariates like weather), (2) the transect we are currently on, and (3) how much effort we expended on that transect (its length or number of visits).</p>
+<p>If we want to fit a DSM that information needs to be expanded to include where on the transect we are too, so we can build the spatial model. For point transects, this is simple as we are always at the point, but for line transects we need to know the position of the detection too (or equivalently, how far along the transect we are).</p>
+<p>More abstractly, we need information about both detections and effort. We also need to link these two. In <code>dsm</code> we use three objects to hold this information:</p>
+<ul>
+<li>the fitted detection function (argument <code>ddf.obj</code>) holds all the detections but ignores spatial information</li>
+<li>the “segments” (argument <code>segment.data</code>) holds the chunks of effort (parts of the transect or point visits) and their locations. It might also include environmental covariate information.</li>
+<li>a table linking the above two (argument <code>observation.data</code>), making sure each detection has a corresponding segment.</li>
+</ul>
+<div class="float">
+<img src="images/dsm_tables.png" style="width:100.0%" alt="Correspondence between dsm tables"><div class="figcaption">Correspondence between <code>dsm</code> tables</div>
+</div>
+<p>The above image gives an overview of how the different <code>data.frames</code> interact, along with how that corresponds to the real life situation of collecting data on a transect.</p>
+<p>The rest of this example goes through each table in turn, explaining their construction.</p>
+</div>
+<div class="section level2">
+<h2 id="table-construction">Table construction<a class="anchor" aria-label="anchor" href="#table-construction"></a>
+</h2>
+<div class="section level3">
+<h3 id="detection-data">Detection data<a class="anchor" aria-label="anchor" href="#detection-data"></a>
+</h3>
+<p>The data requirements are very similar to those for packages <code>mrds</code> and <code>Distance</code>. In both cases, each detection has a corresponding row. For an analysis using <code>dsm</code> we have additional requirements in the data (which can be auto-generated by <code>Distance</code> but we recommend against this to ensure that you know that records link up correctly).</p>
+<p>For <code>Distance</code> we need:</p>
+<ul>
+<li>
+<code>distance</code> : the distance to the detection</li>
+<li>
+<code>object</code> : a unique detection identifier (which will link to the observation table)</li>
+<li>
+<code>size</code> : the number of individuals in the detection (1 if objects occur alone)</li>
+</ul>
+<p><code>mrds</code> requires the following extra columns (to allow for double observer surveys):</p>
+<ul>
+<li>
+<code>detected</code> : 1 if detected by the observer and 0 if missed (always 1 for single observer)</li>
+<li>
+<code>observer</code> : observer number (1 or 2) (always 1 for single observer)</li>
+</ul>
+<p>If other covariates that affect detectability are recorded (e.g., sex or weather) then these can be included in this data for analysis in <code>mrds</code> or <code>Distance</code>.</p>
+<p>Once the model is fitted, we deal only with that fitted model object in the <code>dsm</code> analysis.</p>
+</div>
+<div class="section level3">
+<h3 id="segment-data">Segment data<a class="anchor" aria-label="anchor" href="#segment-data"></a>
+</h3>
+<p>I will use the term “segment” here to refer to both points for point transects and small chunks of transect for the line transect case. I’ll assume that you’ve already segmented your lines while first describing the data format, then go on to an example of how to segment line transect data.</p>
+<p>The <code>data.frame</code> required for the segments needs the following columns:</p>
+<ul>
+<li>
+<code>Effort</code> : the effort expended on this segment (either the length of the segment for lines, or the number of visits for points)</li>
+<li>
+<code>Sample.Label</code> : a unique identifier for the segment (if you are engaged in a complicated survey, this might take a format like “<code>YEAR</code>-<code>AREA</code>-<code>LINE</code>-<code>SEGMENT</code>”, so you have labels like “<code>2020-North-A-15</code>”, which can be useful to keep track of where data came from later).</li>
+</ul>
+<p>In addition, environmental covariates like location and relevant covariates (sea surface temperature, foliage type, altitude, bathymetry, etc) can be included in this <code>data.frame</code> if they are to be used in the spatial model.</p>
+<div class="section level4">
+<h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segmenting"></a>
+</h4>
+<p>If you have data as lines and you want to chop them up into segments, this section will give some sample R code to do this. How things work is extremely dependent on the input data, but hopefully this gives a template for what you want to do at least.</p>
+<p><em>Before you jump in</em>: If you are fluent in ArcGIS or QGIS I recommend doing this task in the software you are familiar with at first. A simple guide to doing this task in <a href="https://www.northrivergeographic.com/qgis-split-a-line-at-an-interval" class="external-link">QGIS is here</a> and for <a href="https://mgel.env.duke.edu/mget/" class="external-link">ArcGIS I recommend the MGET toolbox</a>.</p>
+<p>As an example, we will look at an aerial survey of seabirds off the coast of Rhode Island, USA. Data from these surveys were collected by the University of Rhode Island and analysed in <span class="citation">K. Winiarski, Miller, Paton, &amp; McWilliams (2013)</span>, <span class="citation">K. J. Winiarski, Burt, et al. (2014)</span> and <span class="citation">K. J. Winiarski, Miller, Paton, &amp; McWilliams (2014)</span>. I have the transects saved as a shapefile that I will then “chop-up” into segments. To start with let’s plot the data with the coastline:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># coast data, just for reference</span></span>
+<span><span class="va">coastline</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_read.html" class="external-link">read_sf</a></span><span class="op">(</span><span class="st">"data/ri_coast.shp"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># our transect lines</span></span>
+<span><span class="va">transects</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_read.html" class="external-link">read_sf</a></span><span class="op">(</span><span class="st">"data/ri_transects.shp"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># now make the plot</span></span>
+<span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># geom_sf knows what to do with spatial data</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">coastline</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">transects</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>colour<span class="op">=</span><span class="va">Transect</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># chop to be on a better scale</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">coord_sf</a></span><span class="op">(</span>xlim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">72</span>, <span class="op">-</span><span class="fl">70.8</span><span class="op">)</span>, ylim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">40.8</span>, <span class="fl">41.5</span><span class="op">)</span>, expand<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># make the plot look simpler</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
+<p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-4-1.png"><!-- --></p>
+<p>In the above code we have loaded the <a href="https://r-spatial.github.io/sf/" class="external-link"><code>sf</code> package</a>. This is the package we will use to do most of our spatial work in segmenting the data. It has most of the tools expected from a GIS, though the package is still being developed so its functionality is always increasing.</p>
+<p>Investigating the <code>transects</code> data we loaded, we can see it consists of a <code>data.frame</code> with some extra bits (the spatial information, including locations (<code>geometry</code>) and projection (<code>CRS</code>)):</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">transects</span></span></code></pre></div>
+<pre><code><span><span class="co">## Simple feature collection with 26 features and 1 field</span></span>
+<span><span class="co">## Geometry type: LINESTRING</span></span>
+<span><span class="co">## Dimension:     XY</span></span>
+<span><span class="co">## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521</span></span>
+<span><span class="co">## Geodetic CRS:  WGS 84</span></span>
+<span><span class="co">## <span style="color: #949494;"># A tibble: 26 × 2</span></span></span>
+<span><span class="co">##    Transect                                 geometry</span></span>
+<span><span class="co">##       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>                         <span style="color: #949494; font-style: italic;">&lt;LINESTRING [°]&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 1</span>        1 (-71.86948 41.29969, -71.86581 41.25891)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 2</span>        2  (-71.82872 41.30738, -71.82145 41.2258)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 3</span>        3 (-71.78805 41.31562, -71.75676 40.94846)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 4</span>        4 (-71.74674 41.31728, -71.71394 40.92971)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 5</span>        5  (-71.70589 41.3241, -71.67156 40.91612)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 6</span>        6 (-71.66578 41.33943, -71.62329 40.82944)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 7</span>        7   (-71.5435 41.36397, -71.5064 40.91516)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 8</span>        8  (-71.5015 41.35994, -71.45266 40.82978)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 9</span>        9 (-71.46131 41.37378, -71.42458 40.92496)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">10</span>       10   (-71.421 41.38723, -71.37562 40.83641)</span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 16 more rows</span></span></span></code></pre>
+<p>We have one row per transect (for a total of 24 transects), each of which consists of a line joining the start and end points that make up the transects. If we want we can plot individual rows via <code>plot(transects[1,])</code>; this can be useful to check that each row is a single line, or if further processing is needed (this can be tricky and is not covered in this tutorial but see “More information” below).</p>
+<p>This data looks in good shape, so we can use the <code>st_segmentize</code> function to take each transect and make segments from them. To make sure everything works correctly, we need to project the data first. Here I’m using an appropriate projected coordinate system (EPSG code 6348), which is the Rhode Island State Plane.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># project transects</span></span>
+<span><span class="va">transects</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">transects</span>, <span class="fl">6348</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># do the segmenting</span></span>
+<span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_unary.html" class="external-link">st_segmentize</a></span><span class="op">(</span><span class="va">transects</span>, dfMaxLength<span class="op">=</span><span class="fu">units</span><span class="fu">::</span><span class="fu"><a href="https://r-quantities.github.io/units/reference/units.html" class="external-link">set_units</a></span><span class="op">(</span><span class="fl">2000</span>, <span class="st">"metres"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># transform back to lat/long</span></span>
+<span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">4326</span><span class="op">)</span></span>
+<span><span class="va">transects</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">transects</span>, <span class="fl">4326</span><span class="op">)</span></span></code></pre></div>
+<p>Here we know the truncation used for the detection function was 1000m (the distances were collected in bins and this was the further bin), and since we’re trying to make our segments approximately square, we set the length to be twice that (since the truncation applies to either side of the transect), so 2000m.</p>
+<p>Looking at what that did:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
+<pre><code><span><span class="co">## Simple feature collection with 26 features and 1 field</span></span>
+<span><span class="co">## Geometry type: LINESTRING</span></span>
+<span><span class="co">## Dimension:     XY</span></span>
+<span><span class="co">## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521</span></span>
+<span><span class="co">## Geodetic CRS:  WGS 84</span></span>
+<span><span class="co">## <span style="color: #949494;"># A tibble: 26 × 2</span></span></span>
+<span><span class="co">##    Transect                                                             geometry</span></span>
+<span><span class="co">##  <span style="color: #BCBCBC;">*</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>                                                     <span style="color: #949494; font-style: italic;">&lt;LINESTRING [°]&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 1</span>        1 (-71.86948 41.29969, -71.86826 41.2861, -71.86703 41.2725, -71.8658…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 2</span>        2 (-71.82872 41.30738, -71.82727 41.29106, -71.82581 41.27475, -71.82…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 3</span>        3 (-71.78805 41.31562, -71.78654 41.29813, -71.78505 41.28065, -71.78…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 4</span>        4 (-71.74674 41.31728, -71.74524 41.29967, -71.74373 41.28205, -71.74…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 5</span>        5 (-71.70589 41.3241, -71.70438 41.30636, -71.70288 41.28863, -71.701…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 6</span>        6 (-71.66578 41.33943, -71.6643 41.32185, -71.66283 41.30426, -71.661…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 7</span>        7 (-71.5435 41.36397, -71.542 41.34602, -71.54051 41.32807, -71.53901…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 8</span>        8 (-71.5015 41.35994, -71.49986 41.34227, -71.49821 41.3246, -71.4965…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 9</span>        9 (-71.46131 41.37378, -71.45983 41.35583, -71.45835 41.33788, -71.45…</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">10</span>       10 (-71.421 41.38723, -71.41952 41.36946, -71.41804 41.3517, -71.41656…</span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 16 more rows</span></span></span></code></pre>
+<p>See now that each row has many coordinates attached to it, just looking at the first row (transect 1) and comparing the coordinates between <code>transects</code> and <code>segs</code></p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">transects</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##              X        Y L1</span></span>
+<span><span class="co">## [1,] -71.86948 41.29969  1</span></span>
+<span><span class="co">## [2,] -71.86581 41.25891  1</span></span></code></pre>
+<p><code>transects</code> has just two coordinates in it (the start and end points of the line). Whereas:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">segs</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##              X        Y L1</span></span>
+<span><span class="co">## [1,] -71.86948 41.29969  1</span></span>
+<span><span class="co">## [2,] -71.86826 41.28610  1</span></span>
+<span><span class="co">## [3,] -71.86703 41.27250  1</span></span>
+<span><span class="co">## [4,] -71.86581 41.25891  1</span></span></code></pre>
+<p><code>segs</code> has 5, corresponding to our segment cut points.</p>
+<p>We now need to break up the rows of <code>segs</code> into multiple rows, one per segment. This is a bit fiddly. We use a function provided by <a href="https://dieghernan.github.io" class="external-link"><code>dieghernan</code></a> on <a href="https://dieghernan.github.io/201905_Cast-to-subsegments/" class="external-link">their site</a> to do this. We first load the function (don’t worry about understanding the code!):</p>
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">stdh_cast_substring</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">to</span> <span class="op">=</span> <span class="st">"MULTILINESTRING"</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="va">ggg</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_geometry</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span></span>
+<span>  <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/unique.html" class="external-link">unique</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry_type.html" class="external-link">st_geometry_type</a></span><span class="op">(</span><span class="va">ggg</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://rdrr.io/r/base/match.html" class="external-link">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"POLYGON"</span>, <span class="st">"LINESTRING"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span>    <span class="kw"><a href="https://rdrr.io/r/base/stop.html" class="external-link">stop</a></span><span class="op">(</span><span class="st">"Input should be  LINESTRING or POLYGON"</span><span class="op">)</span></span>
+<span>  <span class="op">}</span></span>
+<span>  <span class="kw">for</span> <span class="op">(</span><span class="va">k</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_geometry</a></span><span class="op">(</span><span class="va">ggg</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span>    <span class="va">sub</span> <span class="op">&lt;-</span> <span class="va">ggg</span><span class="op">[</span><span class="va">k</span><span class="op">]</span></span>
+<span>    <span class="va">geom</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span></span>
+<span>      <span class="fl">1</span><span class="op">:</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">sub</span><span class="op">)</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span> <span class="op">-</span> <span class="fl">1</span><span class="op">)</span>,</span>
+<span>      <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span></span>
+<span>        <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span></span>
+<span>          <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">sub</span><span class="op">)</span><span class="op">[</span><span class="va">i</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span><span class="op">)</span>,</span>
+<span>          <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">sub</span><span class="op">)</span><span class="op">[</span><span class="va">i</span> <span class="op">+</span> <span class="fl">1</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span><span class="op">)</span></span>
+<span>        <span class="op">)</span></span>
+<span>    <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>      <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st.html" class="external-link">st_multilinestring</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>      <span class="fu"><a href="https://r-spatial.github.io/sf/reference/sfc.html" class="external-link">st_sfc</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span>    <span class="kw">if</span> <span class="op">(</span><span class="va">k</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">{</span></span>
+<span>      <span class="va">endgeom</span> <span class="op">&lt;-</span> <span class="va">geom</span></span>
+<span>    <span class="op">}</span></span>
+<span>    <span class="kw">else</span> <span class="op">{</span></span>
+<span>      <span class="va">endgeom</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">endgeom</span>, <span class="va">geom</span><span class="op">)</span></span>
+<span>    <span class="op">}</span></span>
+<span>  <span class="op">}</span></span>
+<span>  <span class="va">endgeom</span> <span class="op">&lt;-</span> <span class="va">endgeom</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/sfc.html" class="external-link">st_sfc</a></span><span class="op">(</span>crs <span class="op">=</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/class.html" class="external-link">class</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span> <span class="op">==</span> <span class="st">"sf"</span><span class="op">)</span> <span class="op">{</span></span>
+<span>    <span class="va">endgeom</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_set_geometry</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">endgeom</span><span class="op">)</span></span>
+<span>  <span class="op">}</span></span>
+<span>  <span class="kw">if</span> <span class="op">(</span><span class="va">to</span> <span class="op">==</span> <span class="st">"LINESTRING"</span><span class="op">)</span> <span class="op">{</span></span>
+<span>    <span class="va">endgeom</span> <span class="op">&lt;-</span> <span class="va">endgeom</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_cast.html" class="external-link">st_cast</a></span><span class="op">(</span><span class="st">"LINESTRING"</span><span class="op">)</span></span>
+<span>  <span class="op">}</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">endgeom</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<p>We can then use it to separate the segments and look at the results:</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu">stdh_cast_substring</span><span class="op">(</span><span class="va">segs</span>, to<span class="op">=</span><span class="st">"LINESTRING"</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning in st_cast.sf(., "LINESTRING"): repeating attributes for all</span></span>
+<span><span class="co">## sub-geometries for which they may not be constant</span></span></code></pre>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
+<pre><code><span><span class="co">## Simple feature collection with 590 features and 1 field</span></span>
+<span><span class="co">## Geometry type: LINESTRING</span></span>
+<span><span class="co">## Dimension:     XY</span></span>
+<span><span class="co">## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521</span></span>
+<span><span class="co">## Geodetic CRS:  WGS 84</span></span>
+<span><span class="co">## <span style="color: #949494;"># A tibble: 590 × 2</span></span></span>
+<span><span class="co">##    Transect                                 geometry</span></span>
+<span><span class="co">##       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>                         <span style="color: #949494; font-style: italic;">&lt;LINESTRING [°]&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 1</span>        1  (-71.86948 41.29969, -71.86826 41.2861)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 2</span>        1   (-71.86826 41.2861, -71.86703 41.2725)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 3</span>        1  (-71.86703 41.2725, -71.86581 41.25891)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 4</span>        2 (-71.82872 41.30738, -71.82727 41.29106)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 5</span>        2 (-71.82727 41.29106, -71.82581 41.27475)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 6</span>        2 (-71.82581 41.27475, -71.82436 41.25843)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 7</span>        2  (-71.82436 41.25843, -71.8229 41.24212)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 8</span>        2   (-71.8229 41.24212, -71.82145 41.2258)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 9</span>        3 (-71.78805 41.31562, -71.78654 41.29813)</span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">10</span>        3 (-71.78654 41.29813, -71.78505 41.28065)</span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
+<p>We now have 590 segments between 1km and 2km long. We can check the lengths using the <code>st_length</code> function and plot a histogram of the segment lengths:</p>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_measures.html" class="external-link">st_length</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>, xlab<span class="op">=</span><span class="st">"Segment lengths (m)"</span>, main<span class="op">=</span><span class="st">""</span><span class="op">)</span></span></code></pre></div>
+<p><img src="dsm-data-formatting_files/figure-html/length-segs-1.png"><!-- --></p>
+<p>Note that in setting the <code>dfMaxLength</code> argument to <code>st_segmentize</code> we are giving a rough guide to the segment length and the algorithm inside <code>st_segmentize</code> tries to get segments to be as equal in length as possible (note the <span class="math inline">\(x\)</span> axis in the histogram).</p>
+<p>Note also that the <code>Transect</code> column in <code>segs</code> now has duplicate entries as each transect has multiple segments in it. We can now create our required columns for <code>dsm</code>.</p>
+<p>First, the <code>Effort</code> column can be generated using <code>st_length</code>:</p>
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">segs</span><span class="op">$</span><span class="va">Effort</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_measures.html" class="external-link">st_length</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span></span></code></pre></div>
+<p>Then we can generate the <code>Sample.Labels</code>. Here I’ll use a more complicated naming scheme to show how that’s done, but one could simply write <code>segs$Sample.Label &lt;- 1:nrow(segs)</code> and that would be sufficient. Instead, I would like to have my <code>Sample.Label</code> be the “<code>YEAR</code>-<code>AREA</code>-<code>LINE</code>-<code>SEGMENT</code>” scheme I suggested above.</p>
+<p>There are fancier ways to do this, but for clarity, let’s use a <code>for</code> loop:</p>
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># create a dummy column that we can fill in as we go</span></span>
+<span><span class="va">segs</span><span class="op">$</span><span class="va">Sample.Label</span> <span class="op">&lt;-</span> <span class="cn">NA</span></span>
+<span></span>
+<span><span class="co"># set the year and area once for this data</span></span>
+<span><span class="va">year</span> <span class="op">&lt;-</span> <span class="fl">2010</span></span>
+<span><span class="va">area</span> <span class="op">&lt;-</span> <span class="st">"RI"</span></span>
+<span></span>
+<span><span class="co"># loop over the transect IDs</span></span>
+<span><span class="kw">for</span><span class="op">(</span><span class="va">this_transect</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html" class="external-link">unique</a></span><span class="op">(</span><span class="va">segs</span><span class="op">$</span><span class="va">Transect</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="co"># how many segments in this transect?</span></span>
+<span>  <span class="va">n_segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">segs</span>, <span class="va">Transect</span><span class="op">==</span><span class="va">this_transect</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="co"># generate the n_segs labels that we need</span></span>
+<span>  <span class="va">segs</span><span class="op">$</span><span class="va">Sample.Label</span><span class="op">[</span><span class="va">segs</span><span class="op">$</span><span class="va">Transect</span><span class="op">==</span><span class="va">this_transect</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="va">year</span>, <span class="va">area</span>, <span class="va">this_transect</span>,</span>
+<span>                                                           <span class="fl">1</span><span class="op">:</span><span class="va">n_segs</span>, sep<span class="op">=</span><span class="st">"-"</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<p>Now we can check that looks right:</p>
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
+<pre><code><span><span class="co">## Simple feature collection with 590 features and 3 fields</span></span>
+<span><span class="co">## Geometry type: LINESTRING</span></span>
+<span><span class="co">## Dimension:     XY</span></span>
+<span><span class="co">## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521</span></span>
+<span><span class="co">## Geodetic CRS:  WGS 84</span></span>
+<span><span class="co">## <span style="color: #949494;"># A tibble: 590 × 4</span></span></span>
+<span><span class="co">##    Transect                                 geometry Effort Sample.Label</span></span>
+<span><span class="co">##  <span style="color: #BCBCBC;">*</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>                         <span style="color: #949494; font-style: italic;">&lt;LINESTRING [°]&gt;</span>    <span style="color: #949494;">[m]</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 1</span>        1  (-71.86948 41.29969, -71.86826 41.2861)  <span style="text-decoration: underline;">1</span>515. 2010-RI-1-1 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 2</span>        1   (-71.86826 41.2861, -71.86703 41.2725)  <span style="text-decoration: underline;">1</span>515. 2010-RI-1-2 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 3</span>        1  (-71.86703 41.2725, -71.86581 41.25891)  <span style="text-decoration: underline;">1</span>515. 2010-RI-1-3 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 4</span>        2 (-71.82872 41.30738, -71.82727 41.29106)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-1 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 5</span>        2 (-71.82727 41.29106, -71.82581 41.27475)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-2 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 6</span>        2 (-71.82581 41.27475, -71.82436 41.25843)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-3 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 7</span>        2  (-71.82436 41.25843, -71.8229 41.24212)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-4 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 8</span>        2   (-71.8229 41.24212, -71.82145 41.2258)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-5 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 9</span>        3 (-71.78805 41.31562, -71.78654 41.29813)  <span style="text-decoration: underline;">1</span>948. 2010-RI-3-1 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">10</span>        3 (-71.78654 41.29813, -71.78505 41.28065)  <span style="text-decoration: underline;">1</span>948. 2010-RI-3-2 </span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
+<p>With the required columns taken care of, we can also calculate the centroids of our segments to get the locations of each segment to use in the spatial model. Handily we can use <code>st_centroid</code> to do this in one step and keep all our data at the same time.</p>
+<p>We get a warning about how centroids might not be calculated correctly when latitude/longitude are used. So again we need to project the data first (to Rhode Island State Plane) for this step using <code>st_transform</code>.</p>
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># save the line version of segs for plotting later</span></span>
+<span><span class="va">segs_lines</span> <span class="op">&lt;-</span> <span class="va">segs</span></span>
+<span></span>
+<span><span class="co"># project segs</span></span>
+<span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">6348</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># find centroids</span></span>
+<span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_unary.html" class="external-link">st_centroid</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning: st_centroid assumes attributes are constant over geometries</span></span></code></pre>
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># project back to lat/long</span></span>
+<span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">4326</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># how does that look?</span></span>
+<span><span class="va">segs</span></span></code></pre></div>
+<pre><code><span><span class="co">## Simple feature collection with 590 features and 3 fields</span></span>
+<span><span class="co">## Geometry type: POINT</span></span>
+<span><span class="co">## Dimension:     XY</span></span>
+<span><span class="co">## Bounding box:  xmin: -71.86887 ymin: 40.83823 xmax: -70.89506 ymax: 41.45646</span></span>
+<span><span class="co">## Geodetic CRS:  WGS 84</span></span>
+<span><span class="co">## <span style="color: #949494;"># A tibble: 590 × 4</span></span></span>
+<span><span class="co">##    Transect             geometry Effort Sample.Label</span></span>
+<span><span class="co">##  <span style="color: #BCBCBC;">*</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;POINT [°]&gt;</span>    <span style="color: #949494;">[m]</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 1</span>        1 (-71.86887 41.29289)  <span style="text-decoration: underline;">1</span>515. 2010-RI-1-1 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 2</span>        1  (-71.86764 41.2793)  <span style="text-decoration: underline;">1</span>515. 2010-RI-1-2 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 3</span>        1  (-71.86642 41.2657)  <span style="text-decoration: underline;">1</span>515. 2010-RI-1-3 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 4</span>        2 (-71.82799 41.29922)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-1 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 5</span>        2  (-71.82654 41.2829)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-2 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 6</span>        2 (-71.82508 41.26659)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-3 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 7</span>        2 (-71.82363 41.25028)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-4 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 8</span>        2 (-71.82218 41.23396)  <span style="text-decoration: underline;">1</span>818. 2010-RI-2-5 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 9</span>        3 (-71.78729 41.30687)  <span style="text-decoration: underline;">1</span>948. 2010-RI-3-1 </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">10</span>        3  (-71.7858 41.28939)  <span style="text-decoration: underline;">1</span>948. 2010-RI-3-2 </span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
+<p>Notice that our <code>geometry type</code> is now <code>POINT</code>. We can plot these centroids on our previous map:</p>
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># geom_sf knows what to do with spatial data</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">coastline</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">transects</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segs</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># chop to be on a better scale</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">coord_sf</a></span><span class="op">(</span>xlim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">72</span>, <span class="op">-</span><span class="fl">70.8</span><span class="op">)</span>, ylim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">40.8</span>, <span class="fl">41.5</span><span class="op">)</span>, expand<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># make the plot look simpler</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
+<p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-6-1.png"><!-- --></p>
+<p>At present, the <code>dsm</code> package doesn’t know how to talk to <code>sf</code> objects, so as a final step we need to simplify <code>segs</code> to be a regular <code>data.frame</code>, which <code>dsm</code> can interpret. We can extract the coordinates of the centroids using <code>st_coordinates</code> and then append them onto a <code>data.frame</code> version of the object with the geometry dropped, like so:</p>
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">segs_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_drop_geometry</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>              <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<p>We can double check that looks right:</p>
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">segs_df</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##   Transect       Effort Sample.Label         X        Y</span></span>
+<span><span class="co">## 1        1 1515.147 [m]  2010-RI-1-1 -71.86887 41.29289</span></span>
+<span><span class="co">## 2        1 1515.151 [m]  2010-RI-1-2 -71.86764 41.27930</span></span>
+<span><span class="co">## 3        1 1515.155 [m]  2010-RI-1-3 -71.86642 41.26570</span></span>
+<span><span class="co">## 4        2 1818.179 [m]  2010-RI-2-1 -71.82799 41.29922</span></span>
+<span><span class="co">## 5        2 1818.184 [m]  2010-RI-2-2 -71.82654 41.28290</span></span>
+<span><span class="co">## 6        2 1818.190 [m]  2010-RI-2-3 -71.82508 41.26659</span></span></code></pre>
+<p>and we are done. See the next section for how to link the segments and the detections.</p>
+<p>If we want to include spatial covariate information in the segment table, we could then use (for example) <a href="https://CRAN.R-project.org/package=rerddap" class="external-link"><code>rerrdap</code></a> to obtain remotely-sensed data. For <em>in situ</em> data (i.e., weather conditions recorded while on effort), this can be more complicated and will require the use of <code>summarize</code>, see <a href="https://github.com/r-spatial/sf/issues/321" class="external-link">this thread for more information</a> on how to deal with this situation.</p>
+</div>
+</div>
+<div class="section level3">
+<h3 id="linking-observations-and-segments">Linking observations and segments<a class="anchor" aria-label="anchor" href="#linking-observations-and-segments"></a>
+</h3>
+<p>To link together the data in the detection function and the spatial data giving the effort, we use the observation <code>data.frame</code>. This is really just a cross-reference between those two tables, so each detection lives in one segment.</p>
+<p>The observation <code>data.frame</code> must have (at least) the following columns:</p>
+<ul>
+<li>
+<code>object</code> : unique object identifier (corresponding to those identifiers in the detection function)</li>
+<li>
+<code>Sample.Label</code> : the identifier for the segment that the observation occurred in</li>
+<li>
+<code>size</code> : the size of each observed group (e.g., 1 if all animals occurred individually)</li>
+<li>
+<code>distance</code> : distance to observation</li>
+</ul>
+<p>The observation data should have as many rows as there are in the detection function.</p>
+<div class="section level4">
+<h4 id="relating-observations-and-segments-in-practice">Relating observations and segments in practice<a class="anchor" aria-label="anchor" href="#relating-observations-and-segments-in-practice"></a>
+</h4>
+<p>There are a few methods for building the observation table. Here I’ll illustrate one assigning detections to segments based on their distance (i.e., detections are associated with their closest segment centroid). This is appropriate when you don’t see forwards too far, so it’s unlikely that detections will be miss assigned. This is true, for instance, in an aerial survey where observers look out windows located on the sides of the plane but might be inappropriate for a shipboard survey where observers on the flying bridge can see way ahead of the ship.</p>
+<p>First loading the detection data for this survey (this would be what we use to fit the detection function, post-processing):</p>
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html" class="external-link">read.csv</a></span><span class="op">(</span><span class="st">"data/loons.csv"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##         Date     Time Bin      Lat      Long Observer object size</span></span>
+<span><span class="co">## 1 2011-04-19 18991230   B 40.85296 -71.45511       JV     46    1</span></span>
+<span><span class="co">## 2 2011-02-17 18991230   A 40.87022 -71.54842       JV    106    1</span></span>
+<span><span class="co">## 3 2011-05-02 18991230   A 40.87595 -71.30304       KW    121    1</span></span>
+<span><span class="co">## 4 2011-12-04 18991230   C 40.91753 -71.38273       KW    286    1</span></span>
+<span><span class="co">## 5 2011-02-23 18991230   A 40.94153 -71.38517       KW    446    1</span></span>
+<span><span class="co">## 6 2011-02-07 18991230   A 40.94225 -71.46438       KW    457    1</span></span></code></pre>
+<p>We can see there are columns for latitude and longitude (<code>Lat</code> and <code>Long</code>). We can make this <code>data.frame</code> be an <code>sf</code> object by telling <code>sf</code> that these columns contain spatial coordinates (in <code>sf</code> lingo, “geometry”):</p>
+<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">obs</span>, coords<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Long"</span>, <span class="st">"Lat"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co"># set the coordinate system</span></span>
+<span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fl">4326</span></span>
+<span><span class="va">obs</span></span></code></pre></div>
+<pre><code><span><span class="co">## Simple feature collection with 941 features and 6 fields</span></span>
+<span><span class="co">## Geometry type: POINT</span></span>
+<span><span class="co">## Dimension:     XY</span></span>
+<span><span class="co">## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472</span></span>
+<span><span class="co">## Geodetic CRS:  WGS 84</span></span>
+<span><span class="co">## First 10 features:</span></span>
+<span><span class="co">##          Date     Time Bin Observer object size                   geometry</span></span>
+<span><span class="co">## 1  2011-04-19 18991230   B       JV     46    1 POINT (-71.45511 40.85296)</span></span>
+<span><span class="co">## 2  2011-02-17 18991230   A       JV    106    1 POINT (-71.54842 40.87022)</span></span>
+<span><span class="co">## 3  2011-05-02 18991230   A       KW    121    1 POINT (-71.30304 40.87595)</span></span>
+<span><span class="co">## 4  2011-12-04 18991230   C       KW    286    1 POINT (-71.38273 40.91753)</span></span>
+<span><span class="co">## 5  2011-02-23 18991230   A       KW    446    1 POINT (-71.38517 40.94153)</span></span>
+<span><span class="co">## 6  2011-02-07 18991230   A       KW    457    1 POINT (-71.46438 40.94225)</span></span>
+<span><span class="co">## 7  2011-04-19 18991230   A       JV    500    2 POINT (-71.59495 40.94761)</span></span>
+<span><span class="co">## 8  2011-05-02 18991230   A       KW    506    1   POINT (-71.552 40.94795)</span></span>
+<span><span class="co">## 9  2011-02-23 18991230   B       JV    814    1 POINT (-71.51172 40.97912)</span></span>
+<span><span class="co">## 10 2011-04-19 18991230   A       KW    911    1 POINT (-71.59762 40.98571)</span></span></code></pre>
+<p>We can overlay this on our previous map of transects:</p>
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># geom_sf knows what to do with spatial data</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">coastline</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">transects</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">obs</span>, size<span class="op">=</span><span class="fl">0.5</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># chop to be on a better scale</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">coord_sf</a></span><span class="op">(</span>xlim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">72</span>, <span class="op">-</span><span class="fl">70.8</span><span class="op">)</span>, ylim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">40.8</span>, <span class="fl">41.5</span><span class="op">)</span>, expand<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># make the plot look simpler</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
+<p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-7-1.png"><!-- --></p>
+<p>Going back to our <code>segs</code> object above, we can use the <code>st_join</code> function, combined with the <code>st_nearest_feature</code> function to control how the tables are linked. Again, we need to project the data to avoid issues.</p>
+<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># project segs</span></span>
+<span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">6348</span><span class="op">)</span></span>
+<span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">obs</span>, <span class="fl">6348</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># do the join</span></span>
+<span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">obs</span>, <span class="va">segs</span>, join<span class="op">=</span><span class="va">st_nearest_feature</span><span class="op">)</span></span>
+<span></span>
+<span></span>
+<span><span class="co"># project back to lat/long</span></span>
+<span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">4326</span><span class="op">)</span></span>
+<span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">obs</span>, <span class="fl">4326</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># how does that look?</span></span>
+<span><span class="va">obs</span></span></code></pre></div>
+<pre><code><span><span class="co">## Simple feature collection with 941 features and 9 fields</span></span>
+<span><span class="co">## Geometry type: POINT</span></span>
+<span><span class="co">## Dimension:     XY</span></span>
+<span><span class="co">## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472</span></span>
+<span><span class="co">## Geodetic CRS:  WGS 84</span></span>
+<span><span class="co">## First 10 features:</span></span>
+<span><span class="co">##          Date     Time Bin Observer object size Transect       Effort</span></span>
+<span><span class="co">## 1  2011-04-19 18991230   B       JV     46    1        8 1969.873 [m]</span></span>
+<span><span class="co">## 2  2011-02-17 18991230   A       JV    106    1       26 1894.100 [m]</span></span>
+<span><span class="co">## 3  2011-05-02 18991230   A       KW    121    1       12 1988.809 [m]</span></span>
+<span><span class="co">## 4  2011-12-04 18991230   C       KW    286    1       10 1979.630 [m]</span></span>
+<span><span class="co">## 5  2011-02-23 18991230   A       KW    446    1       10 1979.624 [m]</span></span>
+<span><span class="co">## 6  2011-02-07 18991230   A       KW    457    1        8 1969.839 [m]</span></span>
+<span><span class="co">## 7  2011-04-19 18991230   A       JV    500    2       24 1923.218 [m]</span></span>
+<span><span class="co">## 8  2011-05-02 18991230   A       KW    506    1       26 1894.075 [m]</span></span>
+<span><span class="co">## 9  2011-02-23 18991230   B       JV    814    1        7 2000.139 [m]</span></span>
+<span><span class="co">## 10 2011-04-19 18991230   A       KW    911    1       24 1923.199 [m]</span></span>
+<span><span class="co">##     Sample.Label                   geometry</span></span>
+<span><span class="co">## 1   2010-RI-8-29 POINT (-71.45511 40.85296)</span></span>
+<span><span class="co">## 2  2010-RI-26-16 POINT (-71.54842 40.87022)</span></span>
+<span><span class="co">## 3  2010-RI-12-31 POINT (-71.30304 40.87595)</span></span>
+<span><span class="co">## 4  2010-RI-10-27 POINT (-71.38273 40.91753)</span></span>
+<span><span class="co">## 5  2010-RI-10-26 POINT (-71.38517 40.94153)</span></span>
+<span><span class="co">## 6   2010-RI-8-24 POINT (-71.46438 40.94225)</span></span>
+<span><span class="co">## 7  2010-RI-24-12 POINT (-71.59495 40.94761)</span></span>
+<span><span class="co">## 8  2010-RI-26-12   POINT (-71.552 40.94795)</span></span>
+<span><span class="co">## 9   2010-RI-7-22 POINT (-71.51172 40.97912)</span></span>
+<span><span class="co">## 10  2010-RI-24-9 POINT (-71.59762 40.98571)</span></span></code></pre>
+<p>Now <code>obs</code> has acquired additional columns from <code>segs</code>, including the <code>Sample.Label</code> (which we want) and <code>Effort</code> (which we don’t). To check this worked okay, we can plot the <code>obs</code> and <code>segs_lines</code> (the line version of the segments which we saved earlier) with colour coding for the <code>Sample.Label</code>. I randomized the colour order so it would be easier to tell if observations were misallocated.</p>
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># make a random colour palette to avoid similar colours being near each other</span></span>
+<span><span class="va">pal</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/grDevices/palettes.html" class="external-link">rainbow</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>, s<span class="op">=</span><span class="fl">.6</span>, v<span class="op">=</span><span class="fl">.9</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>,<span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span><span class="op">]</span></span>
+<span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># geom_sf knows what to do with spatial data</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">coastline</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segs_lines</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>colour<span class="op">=</span><span class="va">Sample.Label</span><span class="op">)</span>, pch<span class="op">=</span><span class="fl">21</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">obs</span>, size<span class="op">=</span><span class="fl">0.5</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>colour<span class="op">=</span><span class="va">Sample.Label</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># chop to be on a better scale</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">coord_sf</a></span><span class="op">(</span>xlim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">72</span>, <span class="op">-</span><span class="fl">70.8</span><span class="op">)</span>, ylim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">40.8</span>, <span class="fl">41.5</span><span class="op">)</span>, expand<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_manual.html" class="external-link">scale_colour_manual</a></span><span class="op">(</span>values<span class="op">=</span><span class="va">pal</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># make the plot look simpler</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html" class="external-link">theme</a></span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
+<p><img src="dsm-data-formatting_files/figure-html/plot-check-1.png"><!-- --></p>
+<p>As with <code>segs</code> we can remove unnecessary columns and geometry to get the required columns only:</p>
+<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># get rid of "spatialness"</span></span>
+<span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_drop_geometry</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span></span>
+<span><span class="co"># select the columns we need</span></span>
+<span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="va">obs</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"object"</span>, <span class="st">"Sample.Label"</span>, <span class="st">"size"</span>, <span class="st">"Bin"</span><span class="op">)</span><span class="op">]</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##   object  Sample.Label size Bin</span></span>
+<span><span class="co">## 1     46  2010-RI-8-29    1   B</span></span>
+<span><span class="co">## 2    106 2010-RI-26-16    1   A</span></span>
+<span><span class="co">## 3    121 2010-RI-12-31    1   A</span></span>
+<span><span class="co">## 4    286 2010-RI-10-27    1   C</span></span>
+<span><span class="co">## 5    446 2010-RI-10-26    1   A</span></span>
+<span><span class="co">## 6    457  2010-RI-8-24    1   A</span></span></code></pre>
+<p>(Note here since we have binned data, we just keep the <code>Bin</code> column and need to process this further later.)</p>
+<p>A different way to approach this would be if there were timestamps on waypoints from the GPS, which could be related to the segments. One could then look at whether an observation was made between the start and end times of a segment.</p>
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="more-information">More information<a class="anchor" aria-label="anchor" href="#more-information"></a>
+</h2>
+<p>We have provided some information about segmenting on <a href="https://osf.io/wgc4f/wiki/Splitting%20transects%20into%20segments/" class="external-link">this wiki page</a>, as part of the US Navy-funded project <a href="https://synergy.st-andrews.ac.uk/denmod/" class="external-link">DenMod</a>.</p>
+<p>This information is summarized at <code><a href="../../reference/dsm-data.html">?"dsm-data"</a></code> in the <code>dsm</code> package. It may also be useful to look at the data in the example dataset <code>mexdolphins</code> which can be loaded with <code>data(mexdolphins)</code>. The vignette for an analysis of those data <a href="http://distancedevelopment.github.io/dsm/articles/lines_gomex/mexico-analysis.html" class="external-link">is available here</a>.</p>
+<p>It is hard to give very general information on how to segment lines, as to some extent it depends on how the data were originally formatted (going all the way back to the make and model of the GPS unit used). More information on dealing with spatial data in R using the <code>sf</code> package is available at <a href="https://r-spatial.github.io/sf/" class="external-link">the R spatial website</a> (see the “Articles” drop down in the header).</p>
+<p>Another source of help is the <a href="https://groups.google.com/forum/#!topic/distance-sampling/" class="external-link">distance sampling mailing list</a>. Be sure to search the archives prior to posting as there have been several threads on segmenting previously that might be helpful.</p>
+</div>
+<div class="section level2">
+<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a>
+</h2>
+<p>Thanks to Phil Bouchet for providing helpful comments to an early version of this document and to Iúri Correia for finding an important bug.</p>
+</div>
+<div class="section level2">
+<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
+</h2>
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0" line-spacing="2">
+<div id="ref-winiarski_integrating_2014" class="csl-entry">
+Winiarski, K. J., Burt, M. L., Rexstad, E., Miller, D. L., Trocki, C. L., Paton, P. W. C., &amp; McWilliams, S. R. (2014). Integrating aerial and ship surveys of marine birds into a combined density surface model: <span>A</span> case study of wintering <span>Common</span> <span>Loons</span>. <em>The Condor</em>, <em>116</em>(2), 149–161. <a href="https://doi.org/10.1650/CONDOR-13-085.1" class="external-link">https://doi.org/10.1650/CONDOR-13-085.1</a>
+</div>
+<div id="ref-winiarski_spatial_2014" class="csl-entry">
+Winiarski, K. J., Miller, D. L., Paton, P. W. C., &amp; McWilliams, S. R. (2014). A spatial conservation prioritization approach for protecting marine birds given proposed offshore wind energy development. <em>Biological Conservation</em>, <em>169</em>, 79–88. <a href="https://doi.org/10.1016/j.biocon.2013.11.004" class="external-link">https://doi.org/10.1016/j.biocon.2013.11.004</a>
+</div>
+<div id="ref-winiarski_spatially_2013" class="csl-entry">
+Winiarski, K., Miller, D., Paton, P., &amp; McWilliams, S. (2013). Spatially explicit model of wintering common loons: Conservation implications. <em>Marine Ecology Progress Series</em>, <em>492</em>, 273–283. <a href="https://doi.org/10.3354/meps10492" class="external-link">https://doi.org/10.3354/meps10492</a>
+</div>
+</div>
+</div>
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diff --git a/docs/articles/index.html b/docs/articles/index.html
index fea592b..5404038 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -24,6 +24,7 @@
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   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
@@ -59,7 +60,9 @@
       <h3>All vignettes</h3>
       <div class="section-desc"></div>
 
-      <dl><dt><a href="lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></dt>
+      <dl><dt><a href="data_format/dsm-data-formatting.html">`dsm` data formatting example</a></dt>
+        <dd><p>‘Making sure your data is in the correct format for <code>dsm</code>.’</p>
+        </dd><dt><a href="lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></dt>
         <dd><p>Example spatial analysis of pantropical spotted dolphins.</p>
       </dd></dl></div>
   </main></div>
diff --git a/docs/articles/lines_gomex/mexico-analysis.html b/docs/articles/lines_gomex/mexico-analysis.html
index e6c21c8..3c7bdd3 100644
--- a/docs/articles/lines_gomex/mexico-analysis.html
+++ b/docs/articles/lines_gomex/mexico-analysis.html
@@ -43,6 +43,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/authors.html b/docs/authors.html
index 5a4b471..1612e35 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -24,6 +24,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/index.html b/docs/index.html
index abb3d3b..5caf680 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -41,6 +41,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/news/index.html b/docs/news/index.html
index 9a9fb9d..ed1e840 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -24,6 +24,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index ed7128a..c5e42bd 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -2,6 +2,7 @@ pandoc: 3.1.11
 pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
+  dsm-data-formatting: data_format/dsm-data-formatting.html
   mexico-analysis: lines_gomex/mexico-analysis.html
-last_built: 2024-05-12T14:52Z
+last_built: 2024-05-13T07:13Z
 
diff --git a/docs/reference/block.info.per.su.html b/docs/reference/block.info.per.su.html
index b455b6b..5686639 100644
--- a/docs/reference/block.info.per.su.html
+++ b/docs/reference/block.info.per.su.html
@@ -26,6 +26,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/check.cols.html b/docs/reference/check.cols.html
index 6e0f66e..43558ec 100644
--- a/docs/reference/check.cols.html
+++ b/docs/reference/check.cols.html
@@ -26,6 +26,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm-data.html b/docs/reference/dsm-data.html
index aae1bc0..c1ec2e2 100644
--- a/docs/reference/dsm-data.html
+++ b/docs/reference/dsm-data.html
@@ -26,6 +26,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm-package.html b/docs/reference/dsm-package.html
index ec1279d..e0a3ee3 100644
--- a/docs/reference/dsm-package.html
+++ b/docs/reference/dsm-package.html
@@ -30,6 +30,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm.cor.html b/docs/reference/dsm.cor.html
index 512d02a..dcfa216 100644
--- a/docs/reference/dsm.cor.html
+++ b/docs/reference/dsm.cor.html
@@ -24,6 +24,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm.html b/docs/reference/dsm.html
index c63ce81..10f0ced 100644
--- a/docs/reference/dsm.html
+++ b/docs/reference/dsm.html
@@ -36,6 +36,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm.var.gam.html b/docs/reference/dsm.var.gam.html
index a4550f8..cd19698 100644
--- a/docs/reference/dsm.var.gam.html
+++ b/docs/reference/dsm.var.gam.html
@@ -24,6 +24,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm.var.movblk.html b/docs/reference/dsm.var.movblk.html
index 73fd4f2..0f12548 100644
--- a/docs/reference/dsm.var.movblk.html
+++ b/docs/reference/dsm.var.movblk.html
@@ -24,6 +24,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm.var.prop.html b/docs/reference/dsm.var.prop.html
index d991d80..ad6ac3a 100644
--- a/docs/reference/dsm.var.prop.html
+++ b/docs/reference/dsm.var.prop.html
@@ -24,6 +24,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm_cor.html b/docs/reference/dsm_cor.html
index e84a790..6d63f74 100644
--- a/docs/reference/dsm_cor.html
+++ b/docs/reference/dsm_cor.html
@@ -26,6 +26,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/dsm_var_gam.html b/docs/reference/dsm_var_gam.html
index ea72169..cdc1c2f 100644
--- a/docs/reference/dsm_var_gam.html
+++ b/docs/reference/dsm_var_gam.html
@@ -30,6 +30,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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 </li>
diff --git a/docs/reference/dsm_var_movblk.html b/docs/reference/dsm_var_movblk.html
index 11cd000..f4fbf74 100644
--- a/docs/reference/dsm_var_movblk.html
+++ b/docs/reference/dsm_var_movblk.html
@@ -28,6 +28,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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 </li>
diff --git a/docs/reference/dsm_var_prop.html b/docs/reference/dsm_var_prop.html
index ca41079..e3357ad 100644
--- a/docs/reference/dsm_var_prop.html
+++ b/docs/reference/dsm_var_prop.html
@@ -30,6 +30,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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 </li>
diff --git a/docs/reference/dsm_varprop.html b/docs/reference/dsm_varprop.html
index 564d79e..c8882d2 100644
--- a/docs/reference/dsm_varprop.html
+++ b/docs/reference/dsm_varprop.html
@@ -26,6 +26,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/dummy_ddf.html b/docs/reference/dummy_ddf.html
index d4ece6a..18d9d6c 100644
--- a/docs/reference/dummy_ddf.html
+++ b/docs/reference/dummy_ddf.html
@@ -26,6 +26,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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 </li>
diff --git a/docs/reference/generate.ds.uncertainty.html b/docs/reference/generate.ds.uncertainty.html
index 4dd0005..ddcd2a8 100644
--- a/docs/reference/generate.ds.uncertainty.html
+++ b/docs/reference/generate.ds.uncertainty.html
@@ -28,6 +28,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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 </li>
diff --git a/docs/reference/generate.mb.sample.html b/docs/reference/generate.mb.sample.html
index 5c6d327..1f11fd6 100644
--- a/docs/reference/generate.mb.sample.html
+++ b/docs/reference/generate.mb.sample.html
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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 </li>
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 1a94dd1..f485577 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
   </div>
 </li>
diff --git a/docs/reference/latlong2km.html b/docs/reference/latlong2km.html
index 58908ac..1afcf45 100644
--- a/docs/reference/latlong2km.html
+++ b/docs/reference/latlong2km.html
@@ -28,6 +28,7 @@
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/make.soapgrid.html b/docs/reference/make.soapgrid.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/mexdolphins.html b/docs/reference/mexdolphins.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/obs_exp.html b/docs/reference/obs_exp.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/plot.dsm.html b/docs/reference/plot.dsm.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/plot.dsm.var.html b/docs/reference/plot.dsm.var.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/plot_pred_by_term.html b/docs/reference/plot_pred_by_term.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/predict.dsm.html b/docs/reference/predict.dsm.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/predict.fake_ddf.html b/docs/reference/predict.fake_ddf.html
index 96bf1f7..574e46a 100644
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/print.dsm.html b/docs/reference/print.dsm.html
index 476ccbb..5ee1e9b 100644
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/print.dsm.var.html b/docs/reference/print.dsm.var.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/print.dsm_varprop.html b/docs/reference/print.dsm_varprop.html
index 5b1610a..4148aa8 100644
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/print.summary.dsm.var.html b/docs/reference/print.summary.dsm.var.html
index a3e5235..c6f0422 100644
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/print.summary.dsm_varprop.html b/docs/reference/print.summary.dsm_varprop.html
index 55a3827..c00d93a 100644
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/rqgam.check.html b/docs/reference/rqgam.check.html
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/rqgam_check.html b/docs/reference/rqgam_check.html
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+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/summary.dsm.html b/docs/reference/summary.dsm.html
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/summary.dsm.var.html b/docs/reference/summary.dsm.var.html
index bf30bb6..432923c 100644
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/summary.dsm_varprop.html b/docs/reference/summary.dsm_varprop.html
index ad02a4c..76e417f 100644
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/trim.var.html b/docs/reference/trim.var.html
index bb6de9c..f41013f 100644
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/vis.concurvity.html b/docs/reference/vis.concurvity.html
index 371aa8a..acbf75c 100644
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/reference/vis_concurvity.html b/docs/reference/vis_concurvity.html
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
     <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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diff --git a/docs/search.json b/docs/search.json
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-[{"path":"/articles/lines_gomex/mexico-analysis.html","id":"introduction","dir":"Articles > Lines_gomex","previous_headings":"","what":"Introduction","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"analysis based dataset observations pantropical dolphins Gulf Mexico (shipped Distance 6.0 later). convenience data bundled R-friendly format, although code necessary creating data Distance project files available github. OBIS-SEAMAP page data may found SEFSC GoMex Oceanic 1996 survey page. intention highlight features dsm package, rather perform full analysis data. reason, important steps fully explored. familiarity density surface modelling (Miller, Burt, Rexstad, & Thomas, 2013) (Hedley & Buckland, 2004) assumed.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"preamble","dir":"Articles > Lines_gomex","previous_headings":"","what":"Preamble","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"start, load dsm package (dependencies) set options: order run vignette, ’ll need install R packages. can done via following call install.packages:","code":"library(dsm) library(ggplot2)  # plotting options gg.opts <- theme(panel.grid.major=element_blank(),                  panel.grid.minor=element_blank(),                  panel.background=element_blank()) install.packages(c(\"dsm\", \"Distance\", \"knitr\", \"distill\", \"ggplot2\", \"rgdal\",                    \"maptools\", \"plyr\", \"tweedie\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"the-data","dir":"Articles > Lines_gomex","previous_headings":"","what":"The data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data need included dsm package, two additional objects needed plotting required can downloaded put directory file. data can loaded R using following code: add objects survey.area pred.polys environment.","code":"load(\"mexdolphins-extra.rda\")"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"observation-and-segment-data","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Observation and segment data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data analysis nicely pre-formatted shipped dsm. Loading data, can see four data.frames, first lines shown: segdata holds segment data: transects already “chopped” segments. distdata holds distance sampling data used fit detection function. obsdata links distance data segments. preddata holds prediction grid (includes necessary covariates). Typically (.e. datasets) necessary divide transects segments, allocate observations correct segments using GIS similar package1, starting analysis using dsm.","code":"data(mexdolphins) head(segdata) ##   longitude latitude        x        y Effort Transect.Label Sample.Label depth ## 1 -86.92712 29.94378 836105.9 -1011416  13800       19960417   19960417-1 135.0 ## 2 -86.83176 29.84030 846012.9 -1021407  14000       19960417   19960417-2 147.7 ## 3 -86.74445 29.75279 855022.6 -1029785  14000       19960417   19960417-3 152.1 ## 4 -86.65230 29.65522 864610.3 -1039168  13900       19960417   19960417-4 163.8 ## 5 -86.56648 29.56088 873598.1 -1048266  13800       19960417   19960417-5 179.7 ## 6 -86.49290 29.49000 881203.7 -1055004  13800       19960417   19960417-6 188.5 head(distdata) ##     object size  distance Effort detected beaufort latitude longitude ## 45      45   21 3296.6363  36300        1        4 27.72872 -86.00159 ## 61      61  150  929.1937  17800        1        4 25.99896 -87.62712 ## 63      63  125 6051.0009  21000        1        2 26.00693 -87.94881 ## 85      85   75 5499.6971  21800        1        1 27.50344 -90.44891 ## 114    114   50 7258.9837  13400        1        3 27.40568 -94.99483 ## 120    120   45 1454.7962  20900        1        5 26.01765 -95.97449 ##              x        y ## 45  948000.065 -1236192 ## 61  812161.653 -1436899 ## 63  780969.520 -1438985 ## 85  528656.807 -1297833 ## 114  95910.149 -1324562 ## 120   2477.665 -1473909 head(obsdata) ##     object Sample.Label size  distance Effort ## 45      45   19960421-9   21 3296.6363  36300 ## 61      61   19960423-7  150  929.1937  17800 ## 63      63   19960423-9  125 6051.0009  21000 ## 85      85   19960427-1   75 5499.6971  21800 ## 114    114   19960430-8   50 7258.9837  13400 ## 120    120   19960501-5   45 1454.7962  20900 head(preddata) ##   latitude longitude        x          y depth      area ## 1 30.08333 -87.58333 774402.9 -1002759.1    35 271236913 ## 2 30.08333 -87.41667 789688.6 -1001264.5    30 271236913 ## 3 30.08333 -87.25000 804971.3  -999740.6    27 271236913 ## 4 30.08333 -87.08333 820251.1  -998187.5    22 271236913 ## 5 30.08333 -86.91667 835528.0  -996605.2    46 271236913 ## 6 29.91667 -87.75000 760783.1 -1021810.3    14 271236913"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"shapefiles-and-converting-units","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Shapefiles and converting units","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often data spatial analysis comes many different sources. important ensure measurements used analysis compatible units, otherwise resulting estimates incorrect hard interpret. measurements SI units outset removes need conversion later, making life much easier. data already appropriate units (Northings Eastings: kilometres centroid, projected using North American Lambert Conformal Conic projection). extensive literature particular projections latitude longitude appropriate highly recommend reader review particular study area; (Bivand, Pebesma, & Gómez-Rubio, 2008) good starting point. data frames already measurements appropriately converted. convention directions named x y. Using latitude longitude performing spatial smoothing can problematic certain smoother bases used. particular bivariate isotropic bases used non-isotropic nature latitude longitude inconsistent (moving one degree one direction moving one degree ). give example projecting polygon defines survey area (simply read R using readShapeSpatial shapefile produced GIS). Next bit code works study.area outline, massaging use soap film smoother. using soap film smoothers, code extraneous. code generates plot, shows survey area transect lines overlaid (using data segdata).  Also note since projected prediction grid, “squares” look quite like squares. plotting use polygons saved, polygons (stored pred.polys) read shapefile created GIS, object class SpatialPolygons sp package. needs project proper plotting using functions sf package. predsf <- st_as_sf(pred.polys)  Tips plotting polygons available ggplot2 pkg_down.","code":"library(sf) library(plyr)  # tell R that the survey.area object is currently in lat/long sp::proj4string(survey.area) <- sp::CRS(\"+proj=longlat +datum=WGS84\")  predsf <- st_as_sf(pred.polys)  area.sf <- st_as_sf(survey.area) st_crs(area.sf) <- \"WGS84\" area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))  # Convert preddata to a spatial object preddata_sf <- st_as_sf(preddata, coords=c(\"x\", \"y\")) st_crs(preddata_sf) <- st_crs(area.sf.proj) # Perform the intersection preddata_sf <- st_intersection(preddata_sf, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries coords_preddata <- data.frame(st_coordinates(preddata_sf))  preddata_sf$x <- coords_preddata$X preddata_sf$y <- coords_preddata$Y # proj 4 string # using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/ lcc_proj4 <- sp::CRS(\"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs \")  # project using LCC survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)  # simplify the object survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords) names(survey.area) <- c(\"x\", \"y\") segdata_sf <- st_as_sf(segdata, coords = c(\"x\",\"y\")) st_crs(segdata_sf) <- st_crs(area.sf.proj) # study area outline and segment centres plot_segments <- ggplot() +   geom_sf(data = area.sf.proj, fill=\"lightblue\", color = \"blue\", linewidth=.1) +   geom_sf(data=segdata_sf, fill=NA, color=\"black\", linewidth=.3) +   labs(title=\"1996 SE Fisheries Science Center Gulf of Mexico cruise\",        subtitle = \"Points are segment centres\") +   scale_fill_viridis_c(option = \"magma\", guide = \"none\") plot_segments predsf <- st_as_sf(pred.polys) # plot as projected plot(st_geometry(predsf), axes=TRUE)"},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"distance-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Distance data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top panels EDA plots show histograms observed distances cluster size, bottom panels show relationship observed distance observed cluster size, relationship observed distance Beaufort sea state. plots show relationship cluster size observed distance (fewer smaller clusters seem seen larger distances). following code generates EDA plots:  Top row, left right: histograms distance cluster size; bottom row: plot distance cluster size plot distances Beaufort sea state.","code":"par(mfrow=c(2,2))  # histograms hist(distdata$distance,main=\"\",xlab=\"Distance (m)\") hist(distdata$size,main=\"\",xlab=\"Cluster size\")  # plots of distance vs. cluster size plot(distdata$distance, distdata$size, main=\"\", xlab=\"Distance (m)\",      ylab=\"Group size\", pch=19, cex=0.5, col=gray(0.7))  # lm fit l.dat <- data.frame(distance=seq(0,8000,len=1000)) lo <- lm(size~distance, data=distdata) lines(l.dat$distance, as.vector(predict(lo,l.dat)))  plot(distdata$distance,distdata$beaufort, main=\"\", xlab=\"Distance (m)\",      ylab=\"Beaufort sea state\", pch=19, cex=0.5, col=gray(0.7))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Spatial data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Looking separately spatial data without thinking distances, can plot observed group sizes space (point size proportional group size observation). Circle size indicates size group observation. rather large areas observations, might cause variance estimates abundance rather large. also see depth data use depth later explanatory covariate spatial model.","code":"prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid) cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries depth <- ggplot() +          geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +          labs(title = \"Spotted dolphins, Gulf of Mexico\",               subtitle = \"Depth in meters, size of detected dolphin groups\") +          xlab(\"Longitude\") + ylab(\"Latitude\") +          geom_point(aes(x, y, size=size), data=distdata, colour=\"red\",alpha=I(0.5)) +          scale_fill_viridis_c(option = \"viridis\", direction = 1) depth"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"estimating-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"","what":"Estimating the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"use ds function package Distance fit detection function. (Distance package intended make standard distance sampling R relatively straightforward. flexible complex alternative, see function ddf mrds library.) First, loading Distance library: can fit detection function hazard-rate key adjustment terms: Calling summary gives us information parameter estimates, probability detection, AIC, etc: following code generates plot fitted detection function (left) quantile-quantile plot (right):  quantile-quantile plot show relatively good goodness fit hazard-rate detection function.","code":"library(Distance) detfc.hr.null <- ds(distdata, max(distdata$distance), key=\"hr\", adjustment=NULL) ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). summary(detfc.hr.null) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  841.1841  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##             estimate        se ## (Intercept) 7.986391 0.5239151 ##  ## Shape coefficient(s):   ##                estimate         se ## (Intercept) 0.000237466 0.02958901 ##  ##                       Estimate         SE        CV ## Average p            0.5918795  0.1667006 0.2816462 ## N in covered region 79.4080596 23.5573051 0.2966614 plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2) gof_ds(detfc.hr.null) ##  ## Goodness of fit results for ddf object ##  ## Distance sampling Cramer-von Mises test (unweighted) ## Test statistic = 0.0976846 p-value = 0.596345"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-covariates-to-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Estimating the detection function","what":"Adding covariates to the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"common include covariates detection function (-called Multiple Covariate Distance Sampling MCDS). dataset two covariates collected individual: Beaufort sea state size. brevity fit hazard-rate detection functions sea state included factor covariate follows: looking summary, detection function covariates give lower AIC model without covariates (843.71 vs. 841.18 hazard-rate model without covariates). Looking back bottom-right panel EDA plots, can see discernible pattern plot Beaufort vs distance. brevity, detection function model selection omitted . practise fit many different forms detection function (select model based goodness fit testing AIC).","code":"detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),                   key=\"hr\", adjustment=NULL) summary(detfc.hr.beau) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  843.7115  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##                         estimate        se ## (Intercept)           7.66291936  1.078972 ## as.factor(beaufort)2  2.24809809 14.754677 ## as.factor(beaufort)3  0.28581820  1.020809 ## as.factor(beaufort)4  0.07133776  1.225360 ## as.factor(beaufort)5 -0.36421115  1.540324 ##  ## Shape coefficient(s):   ##              estimate        se ## (Intercept) 0.2994644 0.5184204 ##  ##                       Estimate         SE        CV ## Average p            0.5420677  0.1753555 0.3234937 ## N in covered region 86.7050383 29.4646397 0.3398262"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"fitting-a-dsm","dir":"Articles > Lines_gomex","previous_headings":"","what":"Fitting a DSM","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"fitting dsm model, data must segmented; consists chopping transects attributing counts segments. mentioned , data already segmented.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"a-simple-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"A simple model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"begin simple model. assume number individuals segment quasi-Poisson distributed smooth function spatial coordinates (note formula exactly one specify gam mgcv). setting group=TRUE, abundance clusters/groups rather individuals can estimated (though ignore ). Note set method=\"REML\" ensure smooth terms estimated reliably. Running model: can obtain summary fitted model: exact interpretation model summary results can found (S. N. Wood, 2017); can see various information smooth components fitted general model statistics. can use deviance explained compare models2. can also get rough idea smooth space looks like using vis.gam (white/yellow indicates high values, red low indicates low values):  type=\"response\" argument ensures plot scale abundance values relative (offsets set median values). means plot useful get idea general shape smooth interpreted directly.","code":"dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)   -18.20       0.53  -34.34   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##         edf Ref.df     F  p-value     ## s(x,y) 24.8  27.49 2.354 0.000733 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.121   Deviance explained = 43.4% ## -REML = 936.04  Scale est. = 94.367    n = 387 vis.gam(dsm.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-another-environmental-covariate-to-the-spatial-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Adding another environmental covariate to the spatial model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data set also contains depth covariate (plotted ). can include model simply: see drop deviance explained, perhaps model useful first. discuss setting k parameter Model checking, . Setting select=TRUE (argument gam) impose extra shrinkage terms smooth model (allowing smooth terms removed model fitting; see ?gam information). particularly useful , include . However many environmental predictors model can good way (along looking \\(p\\)-values) perform term selection. Simply calling plot model object allows us look relationship depth linear predictor:  Omitting argument select call plot plot smooth terms, one time.","code":"dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy.depth) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, k = 10) + s(depth, k = 20) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.741      1.236  -15.16   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F p-value   ## s(x,y)   6.062  7.371 0.923  0.5038   ## s(depth) 9.443 11.466 1.585  0.0824 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0909   Deviance explained = 34.3% ## -REML = 939.52  Scale est. = 124.9     n = 387 plot(dsm.xy.depth, select=2)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-models-when-there-are-covariates-in-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Spatial models when there are covariates in the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"code fit DSM covariates detection function similar models, . However since detection function observation-level covariates, must estimate abundance per segment using Horvitz-Thompson-like estimator modelling, change response abundance.est: can see, summary results rather similar: resulting spatial smooth (though resulting surface somewhat “amplified”):","code":"dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method=\"REML\") summary(dsm.est.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## abundance.est ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -18.0233     0.4739  -38.03   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value     ## s(x,y) 24.64  27.44 2.349 0.00029 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.129   Deviance explained = 42.2% ## -REML = 1043.6  Scale est. = 174.1     n = 387 vis.gam(dsm.est.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", zlim=c(0, 300), contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-response-distributions","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other response distributions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often quasi-Poisson distribution doesn’t give adequate flexibility capture overdispersion response data (see Model checking Model selection ), illustrate two additional distributions can used count data. models section, ’ll move back count response, though estimated abundance also work.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"tweedie","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Tweedie","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Response distributions quasi-Poisson can used, example Tweedie distribution. Tweedie distribution available dsm setting family=tw().","code":"dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method=\"REML\") summary(dsm.xy.tweedie) ##  ## Family: Tweedie(p=1.347)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -17.2651     0.2363  -73.08   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value   ## s(x,y) 12.82  17.13 1.628  0.0562 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0684   Deviance explained = 26.8% ## -REML = 351.49  Scale est. = 60.26     n = 387"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"negative-binomial","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Negative binomial","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Though used , similarly, two options negative binomial distribution: negbin nb. former requires user specification single parameter theta range values parameter (specified vector), latter estimates value theta model fitting process (generally faster). latter recommended users.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-spatial-modelling-options","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other spatial modelling options","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"large literature spatial modelling using GAMs, much can harnessed DSM context. highlights.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"soap-film-smoothing","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other spatial modelling options","what":"Soap film smoothing","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"account complex region (e.g., region includes peninsulae) can use soap film smoother (Simon N. Wood, Bravington, & Hedley, 2008). use soap film smoother spatial part model must create set knots smoother use. easily done using make.soapgrid() function dsm: second argument specifies number points (direction) grid used create knots (knots grid outside survey.area removed). saw exploratory analysis, transect lines outside survey area. cause soap film smoother fail, remove : Note soap_checker script available can useful ensuring boundary, data knots correct format use soap film smoother. can run model depth covariate along spatial (soap film) smooth. Note k argument now refers complexity boundary smooth soap film, complexity film controlled knots given xt argument.","code":"soap.knots <- make.soapgrid(survey.area,c(15,10)) x <- segdata$x; y<-segdata$y onoff <- inSide(x=x,y=y, bnd=as.list(survey.area)) rm(x,y) segdata.soap <- segdata[onoff,] dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs=\"so\", k=15, xt=list(bnd=list(survey.area))) +             s(depth),           family=tw(), method=\"REML\",           detfc.hr.null, segdata.soap, obsdata, knots=soap.knots) summary(dsm.xy.tweedie.soap) ##  ## Family: Tweedie(p=1.356)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, bs = \"so\", k = 15, xt = list(bnd = list(survey.area))) +  ##     s(depth) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.018      0.405  -44.49   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F  p-value     ## s(x,y)   3.050 72.000 0.064 0.114944     ## s(depth) 5.135  6.196 3.895 0.000793 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0653   Deviance explained = 33.1% ## -REML = 339.26  Scale est. = 54.022    n = 365"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-checking","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model checking","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Fitting models well good, ’d like confirm models reasonable; dsm provides functions model checking. can use gam.check generate diagnostic plots:  show deviation Q-Q plot. “line” points plot residuals vs. linear predictor plot corresponds zeros data. Note well plots, gam.check also produces information model fitting. particular interest us last lines tell us basis size. k parameter provided s (te) terms dsm controls complexity smooths model. setting k parameter specify largest complexity smooth term model; long high enough, can sure enough flexibility. output gam.check , can see “p-value” calculated size basis, can good guide whether basis size needs increased. ?choose.k manual page mgcv gives guidance technical details matter. can look model form Tweedie distribution specified response:  Q-Q plot now seems much better (closer \\(y=x\\) line). plots histogram residuals rather hard interpret due large number zeros data. guidance interpreting gam.check output can found (S. N. Wood, 2017).","code":"gam.check(dsm.xy) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 6 iterations. ## Gradient range [-7.105947e-08,-9.826417e-09] ## (score 936.0363 & scale 94.3674). ## Hessian positive definite, eigenvalue range [5.800328,192.5304]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 24.8    1.01    0.99 gam.check(dsm.xy.tweedie) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 8 iterations. ## Gradient range [-1.513175e-06,3.981813e-06] ## (score 351.486 & scale 60.26034). ## Hessian positive definite, eigenvalue range [0.6230615,103.5094]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 12.8    0.73    0.34"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"randomised-quantile-residuals","dir":"Articles > Lines_gomex","previous_headings":"Model checking","what":"Randomised quantile residuals","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top right panel gam.check plots residuals vs. linear predictor plot includes odd line predictions. artifact link function, showing exact zeros data. can misleading distracting, making difficult see whether residuals show heteroskedasticity. Randomised quantile residuals (Dunn & Smyth, 1996) avoid issue transforming residuals exactly normally distributed. makes residuals vs. linear predictor plot much easier interpret therefore doesn’t include artifacts generated link function. plots can produced using rqgam.check dsm:  can see issue heteroskedasticity (increase spread residuals vs. linear predictor plot increasing values linear predictor). One can also plot residuals covariate values check pattern residuals. Note general, plots “Resids vs. linear pred.” interpreted caution output rqgam.check residuals generated normal construction (example Q-Q plot histogram residuals always look fine).","code":"rqgam_check(dsm.xy.tweedie)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-selection","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model selection","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Assuming models “passed” checks gam.check, rqgam.check sufficiently flexible, may left choice model “best”. several methods choosing best model – AIC, REML/GCV scores, deviance explained, full cross-validation test data . Though document intend full analysis pantropical dolphin data, can create results table compare various models fitted far terms abundance estimates associated uncertainties. can use resulting data.frame build table results using kable function:","code":"# make a data.frame to print out mod_results <- data.frame(\"Model name\" = c(\"`dsm.xy`\", \"`dsm.xy.depth`\", \"`dsm.xy.tweedie`\", \"`dsm.xy.tweedie.soap`\",                                            \"`dsm.est.xy`\"),                           \"Description\" = c(\"Bivariate smooth of location, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie\",                                             \"Soap film smooth of location, smooth of depth, Tweedie\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function\"),                           \"Deviance explained\" = c(unlist(lapply(list(dsm.xy,                                                                       dsm.xy.depth,                                                                       dsm.xy.tweedie,                                                                       dsm.xy.tweedie.soap),                  function(x){paste0(round(summary(x)$dev.expl*100,2),\"%\")})),NA)) kable(mod_results, col.names=c(\"Model name\", \"Description\", \"Deviance explained\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"abundance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Abundance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"model checked selected, can make predictions grid calculate abundance. offset stored area column3. Given predicted abundance estimates cell prediction grid, connected prediction grid object mapping:  can calculate abundance survey area simply summing predictions: can compare plot predictions dsm.xy.depth:  can see inclusion depth model noticeable effect distribution (note difference legend scale two plots). can also look total abundance: see much change abundance, terms abundance alone isn’t much two models. Next ’ll go look variance next can see bigger differences models.","code":"dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area) prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid)  preddata_sf$Prediction_xy <- dsm.xy.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.pred) ## [1] 28430.76 dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)  preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, smooth of depth, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.depth.pred) ## [1] 27054.6"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"variance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Variance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Obviously point estimates abundance important, also calculate uncertainty around abundance estimates. Fortunately dsm provides functions perform calculations display resulting uncertainty estimates. two approaches estimating uncertainty abundance estimate dsm. dsm_var_gam assumes spatial model detection function parts model independent . case squared coefficients variation model component added. dsm_var_prop takes account fact detection probability may correlated spatial part model. uses methods described (Bravington, Miller, & Hedley, 2021). dsm_var_prop can applied covariate detection function varies level segments, recorded segment (example Beaufort). don’t situation , opt dsm_var_gam. methods estimate variance abundance element list provided pred.data. case wish obtain abundance prediction cells, use split chop data set list elements give dsm_var_gam (dsm_var_prop). Calling summary give information uncertainty estimation: can also make plot CVs (transect lines observations overlaid) using following code.  Note increase CV away transect lines. can revisit model included depth location smooths observe coefficient variation model larger model location smooth.","code":"preddata.var <- split(preddata, 1:nrow(preddata)) dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,                           off.set=preddata$area) summary(dsm.xy.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 15252.81 28430.76 52994.04  ## (Using log-Normal approximation) ##  ## Point estimate                 : 28430.76  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.164  ## Total standard error           : 9265.663  ## Total coefficient of variation : 0.3259 preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries CV <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, uncertainty (CV)\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) CV dsm.xy.depth.var <- dsm_var_gam(dsm.xy.depth, pred.data=preddata.var,                                 off.set=preddata$area) summary(dsm.xy.depth.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 14377.57 27054.60 50909.23  ## (Using log-Normal approximation) ##  ## Point estimate                 : 27054.6  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.1741  ## Total standard error           : 8958.444  ## Total coefficient of variation : 0.3311"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"conclusions","dir":"Articles > Lines_gomex","previous_headings":"","what":"Conclusions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"document outlined analysis spatially-explicit distance sampling data using dsm package. Note many possible models can fitted using dsm aim show just options. Results models can rather different, care must taken performing model selection, discrimination criticism.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"software","dir":"Articles > Lines_gomex","previous_headings":"","what":"Software","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Distance available http://github.com/DistanceDevelopment/Distance well CRAN. dsm available http://github.com/DistanceDevelopment/dsm, well CRAN.","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"David L. Miller. Author. Eric Rexstad. Contributor. Louise Burt. Contributor. Mark V. Bravington. Contributor. Sharon Hedley. Contributor. Megan Ferguson. Contributor. Natalie Kelly. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller DL (2024). dsm: Density Surface Modelling Distance Sampling Data. R package version 2.3.3.9001, https://github.com/DistanceDevelopment/dsm.","code":"@Manual{,   title = {dsm: Density Surface Modelling of Distance Sampling Data},   author = {David L. Miller},   year = {2024},   note = {R package version 2.3.3.9001},   url = {https://github.com/DistanceDevelopment/dsm}, }"},{"path":[]},{"path":"/index.html","id":"what-is-dsm","dir":"","previous_headings":"","what":"Density Surface Modelling of Distance Sampling Data","title":"Density Surface Modelling of Distance Sampling Data","text":"dsm provides Generalized Additive Model (GAM)-based approach calculate spatially-explicit estimates animal abundance distance sampling (also presence/absence strip transect) data. Several utility functions provided model checking, plotting variance estimation. Open Access paper describing methods: Miller, DL, ML Burt, EA Rexstad L Thomas (2013). Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution.","code":""},{"path":"/index.html","id":"using-dsm","dir":"","previous_headings":"","what":"Using dsm","title":"Density Surface Modelling of Distance Sampling Data","text":"Examples use dsm distance sampling R packages available examples.distancesampling.org.","code":""},{"path":"/reference/block.info.per.su.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the block information — block.info.per.su","title":"Find the block information — block.info.per.su","text":"Takes transect data works many blocks given size (segment terms) fit .","code":""},{"path":"/reference/block.info.per.su.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the block information — block.info.per.su","text":"","code":"block.info.per.su(block.size, data, name.su)"},{"path":"/reference/block.info.per.su.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the block information — block.info.per.su","text":"block.size number segments per block data data used build model name.su names sampling units (.e., transects)","code":""},{"path":"/reference/block.info.per.su.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the block information — block.info.per.su","text":"data.frame following columns name        sample unit name (e.g. transect label) num.seg     number segments transect num.block   number blocks available start.block block number first block end.block   block number last block num.req     number blocks needed unit","code":""},{"path":"/reference/check.cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names exist — check.cols","title":"Check column names exist — check.cols","text":"Internal function check supplied data.frames correct columns checks sample labels unique.","code":""},{"path":"/reference/check.cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names exist — check.cols","text":"","code":"check.cols(ddf.obj, segment.data, observation.data, segment.area)"},{"path":"/reference/check.cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names exist — check.cols","text":"ddf.obj ddf object mrds segment.data segment data defined dsm observation.data observation data defined dsm segment.area area segments","code":""},{"path":"/reference/check.cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names exist — check.cols","text":"nothing, throws error something went wrong","code":""},{"path":"/reference/check.cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check column names exist — check.cols","text":"David Lawrence Miller","code":""},{"path":"/reference/dsm-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Data format for DSM — dsm-data","title":"Data format for DSM — dsm-data","text":"Two data.frames must provided dsm. referred observation.data segment.data.","code":""},{"path":"/reference/dsm-data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data format for DSM — dsm-data","text":"segment.data table sample identifiers define segments, corresponding effort (line length) expended environmental covariates used model abundance/density. observation.data provides link table observations used detection function samples (segments), can aggregate observations segments (.e., observation.data \"look-table\" observations segments). observation.data - observation data.frame must (least) following columns: object unique object identifier Sample.Label identifier segment observation occurred size size observed group (e.g., 1 animals occurred individually) distance distance observation One can often also use observation.data fit detection function (additional columns detection function covariates allowed table). segment.data: segment data.frame must (least) following columns: Effort effort (terms length segment) Sample.Label identifier segment (unique!) ??? environmental covariates, example location (projected latitude longitude), relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).","code":""},{"path":"/reference/dsm-data.html","id":"multiple-detection-functions","dir":"Reference","previous_headings":"","what":"Multiple detection functions","title":"Data format for DSM — dsm-data","text":"multiple detection functions used, column named ddfobj must included observation.data segment.data. lets model know detection function observation . numeric ordered ddf.obj argument dsm, e.g., ddf.obj=list(ship_ddf, aerial_ddf) means ship detections ddfobj=1 aerial detections ddfobj=2 observation data.","code":""},{"path":"/reference/dsm-data.html","id":"mark-recapture-distance-sampling-models","dir":"Reference","previous_headings":"","what":"Mark-recapture distance sampling models","title":"Data format for DSM — dsm-data","text":"using mrds models include mark-recapture components (currently independent observer trial modes supported) format observation data needs checked ensure observations duplicated. observer column also required observation.data. Independent observer mode unique observations (unique object IDs) required. Trial mode observations made observer 1 required.","code":""},{"path":"/reference/dsm-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Density surface modelling — dsm-package","title":"Density surface modelling — dsm-package","text":"dsm implements spatial models distance sampling data. Models detectability can fitted using packages mrds Distance. dsm fits generalized additive models spatially-referenced data. See Miller et al (2013) introduction.","code":""},{"path":"/reference/dsm-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density surface modelling — dsm-package","text":"information distance sampling methods example code available http://distancesampling.org/R/. help distance sampling package, Google Group https://groups.google.com/forum/#!forum/distance-sampling. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density surface modelling — dsm-package","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm.cor","title":"Check for autocorrelation in residuals — dsm.cor","text":"function deprecated, use dsm_cor.","code":""},{"path":"/reference/dsm.cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm.cor","text":"","code":"dsm.cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm.cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm.cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Fits density surface model (DSM) detection adjusted counts spatially-referenced distance sampling analysis. dsm takes observations animals, allocates segments line (strip transects) optionally adjusts counts based detectability using supplied detection function model. generalized additive model, generalized mixed model generalized linear model used model adjusted counts based formula involving environmental covariates.","code":""},{"path":"/reference/dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"dsm(   formula,   ddf.obj,   segment.data,   observation.data,   engine = \"gam\",   convert.units = 1,   family = quasipoisson(link = \"log\"),   group = FALSE,   control = list(keepData = TRUE),   availability = 1,   segment.area = NULL,   weights = NULL,   method = \"REML\",   ... )"},{"path":"/reference/dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"formula formula surface. valid formula. See \"Details\", , define response. ddf.obj result call ddf ds. multiple detection functions required list can provided. strip/circle transects assumed objects observed, see dummy_ddf. Mark-recapture distance sampling (mrds) models type io (independent observers) trial allowed. segment.data segment data, see dsm-data. observation.data observation data, see dsm-data. engine fitting engine used DSM (\"glm\"/\"gam\"/\"gamm\"/\"bam\"). convert.units conversion factor multiply area segments . See 'Units' . family response distribution (popular choices include quasipoisson, Tweedie/tw negbin/nb). Defaults quasipoisson. group TRUE abundance groups calculated rather abundance individuals. Setting option TRUE equivalent setting size group 1. control usual control argument gam; keepData must TRUE variance estimation work (though option set GLMs GAMMs). availability estimate availability bias. count models used multiply effective strip width (must vector length 1 length number rows segment.data); estimated abundance/estimated density models used scale response (must vector length 1 length number rows observation.data). Uncertainty availability handled present. segment.area NULL (default) segment areas calculated multiplying Effort column segment.data (right minus left) truncation distance ddf.obj strip.width. Alternatively vector segment areas can provided (must length number rows segment.data) character string giving name column segment.data contains areas. segment.area specified takes precedent. weights weights observation used model fitting. default, weights=NULL, weights observation area (see Details). Setting scalar value (e.g., weights=1) observations equally weighted. method smoothing parameter estimation method. Default \"REML\", using Restricted Maximum Likelihood. See gam options. Ignored engine=\"glm\". ... anything else passed straight glm, gam, gamm bam.","code":""},{"path":"/reference/dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"glm, gam, gamm bam object, additional element, $ddf holds detection function object.","code":""},{"path":"/reference/dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"response (LHS formula) can one following (restrictions outlined ): count count segment abundance.est estimated abundance per segment, estimation via Horvitz-Thompson estimator density.est density per segment offset used model dependent response: count area segment multiplied average probability detection segment abundance.est area segment density zero count response can used detection function covariates vary segments/points (within). example, weather conditions (like visibility sea state) foliage cover usually acceptable change within segment, animal sex behaviour work. abundance.est response can used covariates detection function. density case, observations can weighted segment areas via weights= argument. default (weights=NULL), density estimated weights set segment areas (using segment.area calculated detection function object metadata Effort data). Alternatively weights=1 set weights equal. third alternative pass vector length equal number segments, containing appropriate weights. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"often case distances collected metres segment lengths recorded kilometres. dsm allows provide conversion factor (convert.units) multiply areas . example: distances metres segment lengths kilometres setting convert.units=1000 lead analysis metres. Setting convert.units=1/1000 lead analysis kilometres. conversion factor applied segment.area specified.","code":""},{"path":"/reference/dsm.html","id":"large-models","dir":"Reference","previous_headings":"","what":"Large models","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"large models, engine=\"bam\" method=\"fREML\" may useful. Models specified bam gam. Read bam using option; option considered EXPERIMENTAL moment. particular note default basis choice (thin plate regression splines) slow general fitting less stable using gam. negative binomial response, theta must specified using bam.","code":""},{"path":"/reference/dsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"David L. Miller","code":""},{"path":"/reference/dsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) summary(mod1)  # predict over a grid mod1.pred <- predict(mod1, preddata, preddata$area)  # calculate the predicted abundance over the grid sum(mod1.pred)  # plot the smooth plot(mod1) }"},{"path":"/reference/dsm.var.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm.var.gam","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"function deprecated, use dsm_var_gam.","code":""},{"path":"/reference/dsm.var.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"","code":"dsm.var.gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm.var.movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"function deprecated, use dsm_var_movblk.","code":""},{"path":"/reference/dsm.var.movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"","code":"dsm.var.movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm.var.movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL -- file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm.var.prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm.var.prop","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"function deprecated, use dsm_var_prop.","code":""},{"path":"/reference/dsm.var.prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"","code":"dsm.var.prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm_cor","title":"Check for autocorrelation in residuals — dsm_cor","text":"DSM fitted data, function can used check autocorrelation residuals.","code":""},{"path":"/reference/dsm_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"dsm_cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm_cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for autocorrelation in residuals — dsm_cor","text":"plot vector fun applied lags.","code":""},{"path":"/reference/dsm_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for autocorrelation in residuals — dsm_cor","text":"Within Transect.Label, segments sorted according Segment.Labels. may require time get right particular data. one multiple surveys transects revisited, example, one may want make Transect.Label unique transect-survey identifier. Neither label need included model, must just present $data field model. usually means segment data passed dsm. current iteration function plot correlations nicely, things can get function return data (assigning result object). NA values residuals correlogram calculated. usually occurs due NA values covariates (smoother fitted values ). Code like (.na(dsm.obj$data)) might helpful.","code":""},{"path":"/reference/dsm_cor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for autocorrelation in residuals — dsm_cor","text":"David L. Miller","code":""},{"path":"/reference/dsm_cor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"# \\donttest{  library(Distance) #>  #> Attaching package: 'Distance' #> The following object is masked from 'package:dsm': #>  #>     dummy_ddf #> The following object is masked from 'package:mrds': #>  #>     create.bins  library(dsm)   # load the data, see ?mexdolphins  data(mexdolphins)   # fit a model  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.418 #> No survey area information supplied, only estimating detection function.  mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) #> Warning: Some observations are outside of detection function 1 truncation!   # look at lag 1 differences up to a maximum of lag 9, using deviance  # residuals  dsm_cor(mod1, resid.type=\"deviance\", max.lag=9,          Segment.Label=\"Sample.Label\")  # }"},{"path":"/reference/dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance propagation for density surface models — dsm_varprop","title":"Variance propagation for density surface models — dsm_varprop","text":"Calculate uncertainty predictions fitted DSM, including uncertainty detection function.","code":""},{"path":"/reference/dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance propagation for density surface models — dsm_varprop","text":"","code":"dsm_varprop(   model,   newdata = NULL,   trace = FALSE,   var.type = \"Vp\",   var_type = NULL )"},{"path":"/reference/dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance propagation for density surface models — dsm_varprop","text":"model fitted dsm. newdata prediction grid. Set NULL avoid making predictions just return model objects. trace debugging, see scale parameter estimation going. var.type variance-covariance matrix used (\"Vp\" variance-covariance conditional smoothing parameter(s), \"Vc\" unconditional). See gamObject details/explanation. doubt, stick default, \"Vp\". var_type deprecated, use var.type instead.","code":""},{"path":"/reference/dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance propagation for density surface models — dsm_varprop","text":"list elements: old_model fitted model supplied function model refit refitted model object, extra term pred point estimates predictions newdata var total variance calculated newdata ses standard error prediction cell newdata newdata=NULL last three entries NA.","code":""},{"path":"/reference/dsm_varprop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance propagation for density surface models — dsm_varprop","text":"make predictions spatial model, also want know uncertainty abundance estimate. Since density surface models 2 () stage models, need incorporate uncertainty earlier stages (.e. detection function) \"final\" uncertainty estimate. function refit spatial model include Hessian offset extra term. Variance estimates using new model can used calculate variance predicted abundance estimates incorporate detection function uncertainty. Importantly requires detection function covariates, vary within segment (, example covariates like sex used). information construct prediction grid data.frame, newdata, see predict.dsm. routine useful detection function covariates used DSM. Note can use var.type=\"Vc\" (see gamObject), variance-covariance matrix spatial model, corrected smoothing parameter uncertainty. See Wood, Pya & S\\\"afken (2016) information. Models fixed scale parameters (e.g., negative binomial) require extra round optimisation.","code":""},{"path":"/reference/dsm_varprop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Variance propagation for density surface models — dsm_varprop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_varprop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance propagation for density surface models — dsm_varprop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535. Wood, S.N., Pya, N. S\\\"afken, B. (2016) Smoothing parameter model selection general smooth models. Journal American Statistical Association, 1-45.","code":""},{"path":"/reference/dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance propagation for density surface models — dsm_varprop","text":"David L. Miller, based code Mark V. Bravington Sharon L. Hedley.","code":""},{"path":"/reference/dsm_var_gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm_var_gam","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"one willing assume detection function spatial model independent, function produce estimates variance predictions abundance, using result squared coefficients variation add.","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"dsm_var_gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"list elements model fitted model object pred.var variance regions given pred.data. bootstrap logical, always FALSE model fitted model extra term dsm.object original model (dsm.obj )","code":""},{"path":"/reference/dsm_var_gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"David L. Miller","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"if (FALSE) {  library(Distance)  library(dsm)   # load the Gulf of Mexico dolphin data (see ?mexdolphins)  data(mexdolphins)   # fit a detection function and look at the summary  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL)  summary(hr.model)   # fit a simple smooth of x and y  mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)   # Calculate the variance  # this will give a summary over the whole area in mexdolphins$preddata  mod1.var <- dsm_var_gam(mod1, preddata, off.set=preddata$area) }"},{"path":"/reference/dsm_var_movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Estimate variance abundance area using moving block bootstrap. Two procedures implemented, one incorporating detection function uncertainty, one .","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"dsm_var_movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm_var_movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL -- file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm_var_movblk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Setting ds.uncertainty=TRUE incorporate detection function uncertainty directly bootstrap. done generating observations fitted detection function re-fitting new detection function (form), calculating new effective strip width. Rejection sampling used generate observations (except half-normal case) procedure can rather slow. Note currently supported covariates detection function. Setting ds.uncertainty=FALSE incorporate detection function uncertainty using delta method. assumes detection function spatial model INDEPENDENT. probably reasonable.","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata) summary(mod1)  # calculate the variance by 500 moving block bootstraps mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,    block.size = 3, samp.unit.name = \"Transect.Label\",    off.set = preddata$area,    bar = TRUE, bs.file = \"mexico-bs.csv\", ds.uncertainty = TRUE) }"},{"path":"/reference/dsm_var_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm_var_prop","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"ensure uncertainty detection function correctly propagated final variance estimate abundance, function uses method first detailed Williams et al (2011), explanation given Bravington et al. (2021).","code":""},{"path":"/reference/dsm_var_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"","code":"dsm_var_prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"list elements model fitted model object pred.var variance region given pred.data bootstrap logical, always FALSE pred.data .set model fitted model extra term dsm.object original model, model.check simple check subtracting coefficients two models see large difference deriv numerically calculated Hessian offset","code":""},{"path":"/reference/dsm_var_prop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"idea refit spatial model including extra random effect. random effect zero mean hence effect point estimates. variance Hessian detection function. Variance estimates incorporate detection function uncertainty. mathematical details given paper references . Many prediction grids can supplied supplying list data.frames function. Note routine simply calls dsm_varprop. require multiple prediction grids, routine probably faster. routine useful detection function covariates used DSM.","code":""},{"path":"/reference/dsm_var_prop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_var_prop.html","id":"limitations","dir":"Reference","previous_headings":"","what":"Limitations","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Note routine useful detection function used DSM. used abundance.est density.est responses used. Importantly requires detection function covariates, vary within segment (, example covariates like sex used).","code":""},{"path":"/reference/dsm_var_prop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535.","code":""},{"path":"/reference/dsm_var_prop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Mark V. Bravington, Sharon L. Hedley. Bugs added David L. Miller.","code":""},{"path":"/reference/dummy_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection function objects when detection is certain — dummy_ddf","title":"Detection function objects when detection is certain — dummy_ddf","text":"Create detection function object strip/plot surveys use density surface models.","code":""},{"path":"/reference/dummy_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection function objects when detection is certain — dummy_ddf","text":"","code":"dummy_ddf(object, size = 1, width, left = 0, transect = \"line\")"},{"path":"/reference/dummy_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection function objects when detection is certain — dummy_ddf","text":"object numeric vector object identifiers, relating object field observation data DSM. size group size observation (default groups size 1) width right truncation left left truncation (default 0, left truncation) transect \"line\" \"point\" transect","code":""},{"path":"/reference/dummy_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection function objects when detection is certain — dummy_ddf","text":"David L Miller","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data from a fitted detection function — generate.ds.uncertainty","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"using dsm.var.movblk ds.uncertainty=TRUE, procedure generates data fitted detection function (assuming correct).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"","code":"generate.ds.uncertainty(ds.object)"},{"path":"/reference/generate.ds.uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"ds.object fitted detection function object (returned call ddf.ds).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"function changes random number generator seed. avoid potential side-effects, use something like: seed <- get(\".Random.seed\",envir=.GlobalEnv) running code assign(\".Random.seed\",seed,envir=.GlobalEnv) .","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"David L. Miller","code":""},{"path":"/reference/generate.mb.sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Moving block bootstrap sampler — generate.mb.sample","title":"Moving block bootstrap sampler — generate.mb.sample","text":"usually used , called within dsm.var.movblk.","code":""},{"path":"/reference/generate.mb.sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Moving block bootstrap sampler — generate.mb.sample","text":"","code":"generate.mb.sample(   num.blocks.required,   block.size,   which.blocks,   dsm.data,   unit.info,   n.units )"},{"path":"/reference/generate.mb.sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Moving block bootstrap sampler — generate.mb.sample","text":"num.blocks.required number blocks need. block.size number segments per block. .blocks blocks sampled. dsm.data $data element result call dsm. unit.info result calling block.info.per.su. n.units number sampling units.","code":""},{"path":"/reference/generate.mb.sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Moving block bootstrap sampler — generate.mb.sample","text":"vector log-residuals","code":""},{"path":"/reference/latlong2km.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert latitude and longitude to Northings and Eastings — latlong2km","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Convert longitude latitude co-ordinates kilometres west-east south-north axes (lon0,lat0) using \"spherical law cosines\".","code":""},{"path":"/reference/latlong2km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"","code":"latlong2km(lon, lat, lon0 = sum(range(lon))/2, lat0 = sum(range(lat))/2)"},{"path":"/reference/latlong2km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"lon longitude lat latitude lon0 longitude reference point (defaults mean longitude) lat0 latitude reference point (defaults mean latitude)","code":""},{"path":"/reference/latlong2km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"list elements km.e km.n.","code":""},{"path":"/reference/latlong2km.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"WARNING: approximate procedure converting latitude/ longitude Northing/Easting. Consider using projection conversions available packages sp, sf rgdal better results.","code":""},{"path":"/reference/latlong2km.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Simon N. Wood","code":""},{"path":"/reference/make.soapgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"routine simply creates grid knots (correct format) used \"internal\" part soap film smoother","code":""},{"path":"/reference/make.soapgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"","code":"make.soapgrid(bnd, n.grid)"},{"path":"/reference/make.soapgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"bnd list elements x y give locations boundary vertices. first last elements . n.grid either one number giving number points along x y axes used create grid, vector giving number x direction, y direction.","code":""},{"path":"/reference/make.soapgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"list elements x y, containing knot locations.","code":""},{"path":"/reference/make.soapgrid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"David L Miller","code":""},{"path":"/reference/mexdolphins.html","id":null,"dir":"Reference","previous_headings":"","what":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Data combination several NOAA shipboard surveys conducted pan-tropical spotted dolphins Gulf Mexico. data consist 47 observations groups dolphins. group size recorded, well Beaufort sea state time observation. Coordinates observation bathymetry data also available covariates analysis. complete example analysis (description data) provided http://distancesampling.org/R/vignettes/mexico-analysis.html.","code":""},{"path":"/reference/mexdolphins.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Halpin, P.N., .J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, . Dimatteo, J. Cleary, C. Good, L.B. Crowder, K.D. Hyrenbach. 2009. OBIS-SEAMAP: world data center marine mammal, sea bird, sea turtle distributions. Oceanography 22(2):104-115 NOAA Southeast Fisheries Science Center. 1996. Report Cetacean Survey Oceanic Selected Continental Shelf Waters Northern Gulf Mexico aboard NOAA Ship Oregon II (Cruise 220)","code":""},{"path":"/reference/obs_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Observed versus expected diagnostics for fitted DSMs — obs_exp","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"Given covariate, calculate observed expected counts unique value covariate. can useful goodness fit check DSMs.","code":""},{"path":"/reference/obs_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"obs_exp(model, covar, cut = NULL)"},{"path":"/reference/obs_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"model fitted dsm model object covar covariate aggregate (character) cut vector cut points aggregate . supplied, unique values covar used.","code":""},{"path":"/reference/obs_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"data.frame values observed expected counts.","code":""},{"path":"/reference/obs_exp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"One strategy model checking calculate observed expected counts different aggregations variable. match well model fit good.","code":""},{"path":"/reference/obs_exp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"David L Miller, suggestion Mark Bravington.","code":""},{"path":"/reference/obs_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # example with the Gulf of Mexico dolphin data data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) }"},{"path":"/reference/plot.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a density surface model. — plot.dsm","title":"Plot a density surface model. — plot.dsm","text":"See plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a density surface model. — plot.dsm","text":"","code":"# S3 method for dsm plot(x, ...)"},{"path":"/reference/plot.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a density surface model. — plot.dsm","text":"x model fitted dsm ... arguments passed plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a density surface model. — plot.dsm","text":"plot!","code":""},{"path":[]},{"path":"/reference/plot.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a density surface model. — plot.dsm","text":"David L. Miller","code":""},{"path":"/reference/plot.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Create plots of abundance uncertainty — plot.dsm.var","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"Note prediction data set must x y columns even used model.","code":""},{"path":"/reference/plot.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"","code":"# S3 method for dsm.var plot(   x,   poly = NULL,   limits = NULL,   breaks = NULL,   legend.breaks = NULL,   xlab = \"x\",   ylab = \"y\",   observations = TRUE,   plot = TRUE,   boxplot.coef = 1.5,   x.name = \"x\",   y.name = \"y\",   gg.grad = NULL,   ... )"},{"path":"/reference/plot.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"x dsm.var object poly list data.frame columns x y, gives coordinates polygon draw. may also optionally column group, many polygons. limits limits fill colours breaks breaks colour fill legend.breaks breaks displayed xlab label x axis ylab label y axis observations observations plotted? plot actually plot map, just return ggplot2 object? boxplot.coef control trimming (summary.dsm.var), effect bootstrap file saved. x.name name variable plot x axis. y.name name variable plot y axis. gg.grad optional ggplot gradient object. ... arguments","code":""},{"path":"/reference/plot.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"plot","code":""},{"path":"/reference/plot.dsm.var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"order get plotting work dsm_var_prop dsm_var_gam, one must first format data correctly since functions designed compute general summaries. One summary calculated element list pred supplied dsm_var_prop dsm_var_gam. plot uncertainty prediction grid, pred (data.frame), say, can create correct format simply using pred.new <- split(pred,1:nrow(pred)).","code":""},{"path":[]},{"path":"/reference/plot.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/plot_pred_by_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially plot predictions per model term — plot_pred_by_term","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"Plot effect smooth model spatially. term model, plot effect space. Plots made scale, relative influence smooth can seen.","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"plot_pred_by_term(dsm.obj, data, location.cov = c(\"x\", \"y\"))"},{"path":"/reference/plot_pred_by_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"dsm.obj fitted dsm object data data use plot (often prediction grid), data also include width height columns plotting location.cov deprecated, use location.cov","code":""},{"path":"/reference/plot_pred_by_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"ggplot2 plot","code":""},{"path":"/reference/plot_pred_by_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"David L Miller (idea taken inlabru)","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data and fit a model data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y) + s(depth), hr.model, segdata, obsdata)  preddata$width <- preddata$height <- sqrt(preddata$area)  # make the plot plot_pred_by_term(mod1, preddata, c(\"x\",\"y\"))  # better plot would be # library(viridis) # plot_pred_by_term(mod1, preddata, c(\"x\",\"y\")) + scale_fill_viridis() }"},{"path":"/reference/predict.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from a fitted density surface model — predict.dsm","title":"Predict from a fitted density surface model — predict.dsm","text":"Make predictions density abundance outside (inside) covered area.","code":""},{"path":"/reference/predict.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from a fitted density surface model — predict.dsm","text":"","code":"# S3 method for dsm predict(object, newdata = NULL, off.set = NULL, type = \"response\", ...)"},{"path":"/reference/predict.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from a fitted density surface model — predict.dsm","text":"object fitted dsm object newdata spatially referenced covariates e.g. altitude, depth, distance shore, etc. Covariates data.frame must names identical variable names used fitting model .set area cells prediction grid. units segments/distances given dsm. Replaces column newdata called .set supplied. Ignored newdata supplied type scale results . default \"response\", see predict.gam explanation options (usually necessary) ... arguments passed predict.gam","code":""},{"path":"/reference/predict.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from a fitted density surface model — predict.dsm","text":"predicted values response scale default (unless type specified, case see predict.gam).","code":""},{"path":"/reference/predict.dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict from a fitted density surface model — predict.dsm","text":"newdata supplied, predictions made data built model. Note order results necessarily segdata (segment data) data.frame supplied dsm. area .set used argument supplied, otherwise look areas column named .set newdata. Either way link function (usually log) applied offsets, need log passing function.","code":""},{"path":[]},{"path":"/reference/predict.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict from a fitted density surface model — predict.dsm","text":"David L. Miller","code":""},{"path":"/reference/predict.fake_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for fake detection functions — predict.fake_ddf","title":"Prediction for fake detection functions — predict.fake_ddf","text":"Prediction function dummy detection functions. function returns many 1s rows newdata. esw=TRUE strip width returned.","code":""},{"path":"/reference/predict.fake_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for fake detection functions — predict.fake_ddf","text":"","code":"# S3 method for fake_ddf predict(   object,   newdata = NULL,   compute = FALSE,   int.range = NULL,   esw = FALSE,   ... )"},{"path":"/reference/predict.fake_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for fake detection functions — predict.fake_ddf","text":"object model object newdata many 1s return? compute unused, compatibility mrds::predict int.range unused, compatibility mrds::predict esw strip width returned? ... S3 consistency","code":""},{"path":"/reference/predict.fake_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction for fake detection functions — predict.fake_ddf","text":"David L Miller","code":""},{"path":"/reference/print.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model object — print.dsm","title":"Print a description of a density surface model object — print.dsm","text":"method just gives short description fitted model. Use summary.dsm method information.","code":""},{"path":"/reference/print.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model object — print.dsm","text":"","code":"# S3 method for dsm print(x, ...)"},{"path":"/reference/print.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model object — print.dsm","text":"x model fitted dsm ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model object — print.dsm","text":"NULL","code":""},{"path":"/reference/print.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model object — print.dsm","text":"David L. Miller","code":""},{"path":"/reference/print.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm.var","title":"Print a description of a density surface model variance object — print.dsm.var","text":"method provides short summary, use summary.dsm.var method information.","code":""},{"path":"/reference/print.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm.var","text":"","code":"# S3 method for dsm.var print(x, ...)"},{"path":"/reference/print.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm.var","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model variance object — print.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm_varprop","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"method provides short summary, see summary.dsm_varprop.","code":""},{"path":"/reference/print.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"","code":"# S3 method for dsm_varprop print(x, ...)"},{"path":"/reference/print.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":[]},{"path":"/reference/print.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm.var","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"See summary.dsm.var information.","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"","code":"# S3 method for summary.dsm.var print(x, ...)"},{"path":"/reference/print.summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm_varprop","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"See summary.dsm_varprop information.","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"","code":"# S3 method for summary.dsm_varprop print(x, ...)"},{"path":"/reference/print.summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/rqgam.check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"function deprecated, use rqgam_check.","code":""},{"path":"/reference/rqgam.check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"","code":"rqgam.check(gam.obj, ...)"},{"path":"/reference/rqgam.check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Reproduces \"Resids vs. linear pred\" plot gam.check using randomised quantile residuals, la Dunn Smyth (1996). Checks heteroskedasticity usual, looking \"funnel\"-type structures points, much easier randomised quantile residuals deviance residuals, model uses count distribution response.","code":""},{"path":"/reference/rqgam_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"rqgam_check(gam.obj, ...)"},{"path":"/reference/rqgam_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"just plots!","code":""},{"path":"/reference/rqgam_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Note function works negative binomial Tweedie response distributions. Earlier versions function produced full gam.check output, confusing one plots really useful.  Checks k computed, need done using gam.check.","code":""},{"path":"/reference/rqgam_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Based code Natalie Kelly, bugs added Dave Miller","code":""},{"path":"/reference/rqgam_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"# \\donttest{ library(Distance) library(dsm) library(tweedie)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.418 #> No survey area information supplied, only estimating detection function.  # fit a simple smooth of x and y with a Tweedie response with estimated #  p parameter mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw()) #> Warning: Some observations are outside of detection function 1 truncation! rqgam_check(mod1)  # }"},{"path":"/reference/summary.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted density surface model — summary.dsm","title":"Summarize a fitted density surface model — summary.dsm","text":"Gives brief summary fitted dsm object.","code":""},{"path":"/reference/summary.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted density surface model — summary.dsm","text":"","code":"# S3 method for dsm summary(object, ...)"},{"path":"/reference/summary.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted density surface model — summary.dsm","text":"object model fitted dsm ... arguments passed summary.gam","code":""},{"path":"/reference/summary.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted density surface model — summary.dsm","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize a fitted density surface model — summary.dsm","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm.var","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"Gives brief summary fitted dsm variance object.","code":""},{"path":"/reference/summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"","code":"# S3 method for dsm.var summary(   object,   alpha = 0.05,   boxplot.coef = 1.5,   bootstrap.subregions = NULL,   ... )"},{"path":"/reference/summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95\\ confidence intervals) boxplot.coef value coef used calculate outliers see boxplot. bootstrap.subregions list vectors logicals indices subregions variances need calculated (bootstraps (see dsm.var.prop use subregions variance propagation). ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm_varprop","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"Gives brief summary fitted dsm_varprop variance object.","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"","code":"# S3 method for dsm_varprop summary(object, alpha = 0.05, ...)"},{"path":"/reference/summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95% confidence internal) ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/trim.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimmed variance — trim.var","title":"Trimmed variance — trim.var","text":"Trim variance estimates bootstrap. defined percentage defined amount necessary bring median trimmed mean within 8% defined 'outliers'.","code":""},{"path":"/reference/trim.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimmed variance — trim.var","text":"","code":"trim.var(untrimmed.bootstraps, boxplot.coef = 1.5)"},{"path":"/reference/trim.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimmed variance — trim.var","text":"untrimmed.bootstraps (usually $study.area.total element returned dsm.var.movblk bootstrap object. boxplot.coef value coef used calculate outliers see boxplot.","code":""},{"path":"/reference/trim.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimmed variance — trim.var","text":"trimmed variance","code":""},{"path":"/reference/trim.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trimmed variance — trim.var","text":"Louise Burt","code":""},{"path":"/reference/vis.concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis.concurvity","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"function deprecated, use vis_concurvity.","code":""},{"path":"/reference/vis.concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"","code":"vis.concurvity(model, type = \"estimate\")"},{"path":"/reference/vis.concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis_concurvity","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"Plot measures much one term model explained another. values high, one consider re-running variable selection one offending variables removed check stability term selection.","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"vis_concurvity(model, type = \"estimate\")"},{"path":"/reference/vis_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"methods considered somewhat experimental time. Consult concurvity information concurvity measures calculated.","code":""},{"path":"/reference/vis_concurvity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"David L Miller","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata)  # visualise concurvity using the \"estimate\" metric vis_concurvity(mod1) }"},{"path":"/news/index.html","id":"dsm-234","dir":"Changelog","previous_headings":"","what":"dsm 2.3.4","title":"dsm 2.3.4","text":"fix bug predictions NA (e.g., predictions outside soap boundary), variances NA due propagation (#4)","code":""},{"path":"/news/index.html","id":"dsm-233","dir":"Changelog","previous_headings":"","what":"dsm 2.3.3","title":"dsm 2.3.3","text":"CRAN release: 2022-08-20 dsm.var.prop -> dsm_var_prop dsm.var.gam -> dsm_var_gam dsm.var.movblk -> dsm_var_movblk dsm.cor -> dsm_cor rqgam.check -> rqgam_check vis.concurvity -> vis_concurvity var_type -> var.type","code":""},{"path":"/news/index.html","id":"dsm-232","dir":"Changelog","previous_headings":"","what":"dsm 2.3.2","title":"dsm 2.3.2","text":"CRAN release: 2022-03-17 fixed bug offset calculation dsm_varprop se estimation offsets logged twice. Thanks Megan Ferguson spotting . documentation now rmarkdown format dsm 2.3.1 transect type (line point) now determined detection function rather using transect= argument multiple detection functions now possible, use list() fitted detection functions supplied ddf.obj= argument dsm(). dsm.var.gam, dsm.var.prop dsm_varprop compatible. Vignette coming soon. Thanks Dave Fifield Ewan Wakefield extensive testing suggestions. See ?“dsm-data” information set data. strip transects now handled using dummy_ddf() function. presence~ response type removed Response options “D”, “density”, “Dhat”, “N”, “Nhat” “n” deprecated. Now “count”, “abundance.est” “density.est” allowed. Make predict.dsm work tibbles .set column newdata (error thrown previous code’s check ). Thanks Dave Fifield reporting issue. Fix bug inconsistent predictions density.est used response. Code predict.dsm simplified documentation updated. Thanks Dave Fifield reporting issue. mrds independent observer (io) trial models allowed detection function.Variance can estimated via dsm_varprop (usual limitation covariates may vary segment level) dsm.var.gam. feature currently experimental. Thanks Doug Sigourney Megan Ferguson help suggestions. See ?“dsm-data” information set data. example multiddf mrds analysis dsm https://github.com/densitymodelling/nefsc_fin_mrds_dsm availability= argument now works differently count models. availability must now length number segments (nrow(segment.data)) multiply offset (effective area) rather number animals observed coherent detectability handled case. dsm_varprop longer requies newdata supplied. newdata isn’t present just refitted original models returned, entries NA.","code":""},{"path":"/news/index.html","id":"dsm-230","dir":"Changelog","previous_headings":"","what":"dsm 2.3.0","title":"dsm 2.3.0","text":"CRAN release: 2020-04-22 fixed dsm_varprop (gam.fixed.priors) response distributions fixed scale parameters. Negative binomial results now may incorrect!","code":""},{"path":"/news/index.html","id":"dsm-2218","dir":"Changelog","previous_headings":"","what":"dsm 2.2.18","title":"dsm 2.2.18","text":"Clarification predict.dsm return documentation. Thanks Dave Fifield suggestion. .set=NULL lead predictions 0 density used. Now just returns density. Thanks Dave Fifield catch.","code":""},{"path":"/news/index.html","id":"dsm-2217","dir":"Changelog","previous_headings":"","what":"dsm 2.2.17","title":"dsm 2.2.17","text":"CRAN release: 2019-01-19 Now Suggests package “sp” (needed parts mexdolphin example data)","code":""},{"path":"/news/index.html","id":"dsm-2216","dir":"Changelog","previous_headings":"","what":"dsm 2.2.16","title":"dsm 2.2.16","text":"CRAN release: 2018-06-27 check Sample.Labels match , none throw error. Thanks Madhura Davate bringing issue attention. gamma option removed, since now ignored gam(). using method=“GCV.Cp” gamma=1.4 still used (generally advise using REML rather GCV smoothing parameter selection) dsm now requires mgcv version 1.8-23 higher. dsm.var.prop (dsm_varprop) now throw error covariates detection function can’t anything useful new function obs_exp diagnostic function compare observed expected counts different covariate aggregations","code":""},{"path":"/news/index.html","id":"dsm-2215","dir":"Changelog","previous_headings":"","what":"dsm 2.2.15","title":"dsm 2.2.15","text":"CRAN release: 2017-07-03 mexdolphins loaded using data(), data longer need attach()ed dsm.var.prop throws error count N used response. dsm.var.prop now calls dsm_varprop, keeps scale parameter constant refitting models variance propagation. dsm_varprop provides lower-level interface variance propagation probably faster flexible. add vis.concurvity visually assess concurvity (colinearity extended smooth terms) model new model diagnostic function: plot_pred_by_term – plots effect smooth model spatially. remove “abundance” option response, use “count” dsm.var.prop dsm_varprop better diagnostics checking variance model hasn’t done something weird dsm.var.movblk discards replicates model didn’t converge fix potential issue negative binomial response used variance propagation (due scale parameterisations). dsm_varprop now refits nb() models negbin() advise users look deeply. Thanks Eric Rexstad spotting . fix bug predict.dsm() predictions “density” response including offset. Thanks Len Thomas spotting .","code":""},{"path":"/news/index.html","id":"dsm-2214","dir":"Changelog","previous_headings":"","what":"dsm 2.2.14","title":"dsm 2.2.14","text":"CRAN release: 2017-01-26 nasty bug predict.dsm fixed: logging offset twice newdata provided, leading odd checking plots. also effected results predict() newdata= specified. Thanks Megan Ferguson alerting us . add support point transects(!) rqgam.check() now produced linear predictor vs. randomised quantile residuals plot avoid confusion interpreting plots REML now default method smoothing parameter selection","code":""},{"path":"/news/index.html","id":"dsm-2213","dir":"Changelog","previous_headings":"","what":"dsm 2.2.13","title":"dsm 2.2.13","text":"CRAN release: 2016-10-03 dsm.var.gam now example dsm.var.prop now checks detection function model covariates","code":""},{"path":"/news/index.html","id":"dsm-2212","dir":"Changelog","previous_headings":"","what":"dsm 2.2.12","title":"dsm 2.2.12","text":"CRAN release: 2016-08-10 alpha argument now works analytical variance reported using dsm.var.prop dsm.var.gam print.summary summary methods. Thanks Jason Roberts Eric Rexstad spotting issue. dsm now uses numDeriv::grad calculate derivatives variance estimation – much faster. Thanks Jason Roberts fix. dsm now requires mrds version 2.1.15 higher, due internal changes fixed issue Effort column required even segment.area specified. Thanks Greg Lollback spotting issue. ensured ordering segments matched ordering observations aggregation observations correct. Thanks Dave Fifield reporting bug suggesting fix. check.cols longer checks see column called “distance” strip transects used","code":""},{"path":"/news/index.html","id":"dsm-2211","dir":"Changelog","previous_headings":"","what":"dsm 2.2.11","title":"dsm 2.2.11","text":"dsm.var.prop uses update() rather messing around eval() print.summary.dsm.var didn’t work GAM variance detection function specified, now fixed (thanks Natalia Dellabianca spotting ) Removed non-working plot() code variance examples – see Mexico pantropical spotted dolphins proper treatment plotting using projections (http://distancesampling.org/R/vignettes/mexico-analysis.html) plot.var.gam longer crashes default distance data doesn’t columns “x” “y”","code":""},{"path":"/news/index.html","id":"dsm-2210","dir":"Changelog","previous_headings":"","what":"dsm 2.2.10","title":"dsm 2.2.10","text":"corrected offsets examples dsm, dsm.var.prop, dsm.var.movblk updated mexdolphins data lat/long reversal prediction data non-list storage stopped use rqgam.check used non-negbin/Tweedie responses (thanks Phil Bouchet highlighting issue).","code":""},{"path":"/news/index.html","id":"dsm-229","dir":"Changelog","previous_headings":"","what":"dsm 2.2.9","title":"dsm 2.2.9","text":"CRAN release: 2015-07-29 Moving block bootstrap now works detection function (suggested Laura Williamson) Mexico dolphin data areas prediction grids segments re-projected. Thanks Phil Bouchet pointing error. Context: https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ","code":""},{"path":"/news/index.html","id":"dsm-228","dir":"Changelog","previous_headings":"","what":"dsm 2.2.8","title":"dsm 2.2.8","text":"Speed reduced memory usage variance calculations, thanks Filipe Dias asking . Better error observations matched detection function. Thanks Ricardo Lima highlighting . Check missing columns incorrect logic, now corrected tests. Thanks Julia Migné spotting .","code":""},{"path":"/news/index.html","id":"dsm-227","dir":"Changelog","previous_headings":"","what":"dsm 2.2.7","title":"dsm 2.2.7","text":"Plotting check plots using random quantile residuals (rqgam.check) didn’t work Tweedie tw family models, now fixed. Thanks Laura Mannocci Jason Roberts pointing . Plotting CV used matrix() coerce data data.frame – thus converting elements simplest type. Errors ensued. Thanks Adrian Schiavini finding bug!","code":""},{"path":"/news/index.html","id":"dsm-226","dir":"Changelog","previous_headings":"","what":"dsm 2.2.6","title":"dsm 2.2.6","text":"dsm.var.prop dsm.var.gam now allow supply single number offset, copy many times need. Thanks Filipe Dias suggesting .","code":""},{"path":"/news/index.html","id":"dsm-225","dir":"Changelog","previous_headings":"","what":"dsm 2.2.5","title":"dsm 2.2.5","text":"CRAN release: 2014-09-16 dsm.var.prop now throw error detection function dsm ’s trying calculate variance (thanks Adrián Schiavini finding ) variance/CV plotting threw error detection function supplied observation=TRUE. Now error sidestepped (, thanks Adrián Schiavini finding ).","code":""},{"path":"/news/index.html","id":"dsm-224","dir":"Changelog","previous_headings":"","what":"dsm 2.2.4","title":"dsm 2.2.4","text":"CRAN release: 2014-06-17 changed stop() warning() data detection function included observation data frame, automatically excluded later, bug found Eric Rexstad Anna Cucknell","code":""},{"path":"/news/index.html","id":"dsm-223","dir":"Changelog","previous_headings":"","what":"dsm 2.2.3","title":"dsm 2.2.3","text":"CRAN release: 2014-04-23 moving block boostrap using observed abundances residuals therefore gave wrong answers (Eric Rexstad found bug)","code":""},{"path":"/news/index.html","id":"dsm-222","dir":"Changelog","previous_headings":"","what":"dsm 2.2.2","title":"dsm 2.2.2","text":"version number reporting now works (Len Thomas found bug) documentation default density presence models now weighted area segment","code":""},{"path":"/news/index.html","id":"dsm-221","dir":"Changelog","previous_headings":"","what":"dsm 2.2.1","title":"dsm 2.2.1","text":"switch new testthat spec added new response variables (Dhat, density.est, count, n), suggestion Len Thomas","code":""},{"path":"/news/index.html","id":"dsm-214","dir":"Changelog","previous_headings":"","what":"dsm 2.1.4","title":"dsm 2.1.4","text":"check observed distances within width now throw error","code":""},{"path":"/news/index.html","id":"dsm-213","dir":"Changelog","previous_headings":"","what":"dsm 2.1.3","title":"dsm 2.1.3","text":"CRAN release: 2013-08-20 CRAN-compatibility fixes","code":""},{"path":"/news/index.html","id":"dsm-212","dir":"Changelog","previous_headings":"","what":"dsm 2.1.2","title":"dsm 2.1.2","text":"updates dsm.cor unified plotting variance estimation tweaks non-dsm models","code":""},{"path":"/news/index.html","id":"dsm-206","dir":"Changelog","previous_headings":"","what":"dsm 2.0.6","title":"dsm 2.0.6","text":"removed warning () comparison length make.soapgrid need pass bnd list() dsm.cor now handles gamm objects dsm.cor allows one check residual autocorrelation models presence/absence data now longer needs dummy detection function object","code":""},{"path":"/news/index.html","id":"dsm-205","dir":"Changelog","previous_headings":"","what":"dsm 2.0.5","title":"dsm 2.0.5","text":"started NEWS file fixed bug dsm.var.gam() didn’t return predictions plotting result fail silly typo docs check ’re using mgcv version >= 1.7-24 GAMM fitting warn ","code":""}]
+[{"path":"/articles/data_format/dsm-data-formatting.html","id":"introduction","dir":"Articles > Data_format","previous_headings":"","what":"Introduction","title":"`dsm` data formatting example","text":"dsm package expects data specific format. example ’ll show get data format give explanation requirements exist. recommend getting grips fitting models dsm reading tutorial know end point look like trying grapple data. example Gulf Mexico pantropical spotted dolphins available .","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"prerequisites","dir":"Articles > Data_format","previous_headings":"","what":"Prerequisites","title":"`dsm` data formatting example","text":"run code , need ggplot2 sf libraries installed. can following code: Rhode Island data required downloaded : loon observations coastline dbf coastline projection coastline shapefile coastline shx transects dbf transects projection transects shapefile transects shx placed folder called data code work.","code":"install.packages(c(\"ggplot2\", \"sf\"))"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"overview","dir":"Articles > Data_format","previous_headings":"","what":"Overview","title":"`dsm` data formatting example","text":"collect distance sampling data, need record (1) distance observation (along detectability covariates like weather), (2) transect currently , (3) much effort expended transect (length number visits). want fit DSM information needs expanded include transect , can build spatial model. point transects, simple always point, line transects need know position detection (equivalently, far along transect ). abstractly, need information detections effort. also need link two. dsm use three objects hold information: fitted detection function (argument ddf.obj) holds detections ignores spatial information “segments” (argument segment.data) holds chunks effort (parts transect point visits) locations. might also include environmental covariate information. table linking two (argument observation.data), making sure detection corresponding segment. image gives overview different data.frames interact, along corresponds real life situation collecting data transect. rest example goes table turn, explaining construction.","code":""},{"path":[]},{"path":"/articles/data_format/dsm-data-formatting.html","id":"detection-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Detection data","title":"`dsm` data formatting example","text":"data requirements similar packages mrds Distance. cases, detection corresponding row. analysis using dsm additional requirements data (can auto-generated Distance recommend ensure know records link correctly). Distance need: distance : distance detection object : unique detection identifier (link observation table) size : number individuals detection (1 objects occur alone) mrds requires following extra columns (allow double observer surveys): detected : 1 detected observer 0 missed (always 1 single observer) observer : observer number (1 2) (always 1 single observer) covariates affect detectability recorded (e.g., sex weather) can included data analysis mrds Distance. model fitted, deal fitted model object dsm analysis.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segment-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Segment data","title":"`dsm` data formatting example","text":"use term “segment” refer points point transects small chunks transect line transect case. ’ll assume ’ve already segmented lines first describing data format, go example segment line transect data. data.frame required segments needs following columns: Effort : effort expended segment (either length segment lines, number visits points) Sample.Label : unique identifier segment (engaged complicated survey, might take format like “YEAR-AREA-LINE-SEGMENT”, labels like “2020-North--15”, can useful keep track data came later). addition, environmental covariates like location relevant covariates (sea surface temperature, foliage type, altitude, bathymetry, etc) can included data.frame used spatial model.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segmenting","dir":"Articles > Data_format","previous_headings":"Table construction > Segment data","what":"Segmenting","title":"`dsm` data formatting example","text":"data lines want chop segments, section give sample R code . things work extremely dependent input data, hopefully gives template want least. jump : fluent ArcGIS QGIS recommend task software familiar first. simple guide task QGIS ArcGIS recommend MGET toolbox. example, look aerial survey seabirds coast Rhode Island, USA. Data surveys collected University Rhode Island analysed K. Winiarski, Miller, Paton, & McWilliams (2013), K. J. Winiarski, Burt, et al. (2014) K. J. Winiarski, Miller, Paton, & McWilliams (2014). transects saved shapefile “chop-” segments. start let’s plot data coastline:  code loaded sf package. package use spatial work segmenting data. tools expected GIS, though package still developed functionality always increasing. Investigating transects data loaded, can see consists data.frame extra bits (spatial information, including locations (geometry) projection (CRS)): one row per transect (total 24 transects), consists line joining start end points make transects. want can plot individual rows via plot(transects[1,]); can useful check row single line, processing needed (can tricky covered tutorial see “information” ). data looks good shape, can use st_segmentize function take transect make segments . make sure everything works correctly, need project data first. ’m using appropriate projected coordinate system (EPSG code 6348), Rhode Island State Plane. know truncation used detection function 1000m (distances collected bins bin), since ’re trying make segments approximately square, set length twice (since truncation applies either side transect), 2000m. Looking : See now row many coordinates attached , just looking first row (transect 1) comparing coordinates transects segs transects just two coordinates (start end points line). Whereas: segs 5, corresponding segment cut points. now need break rows segs multiple rows, one per segment. bit fiddly. use function provided dieghernan site . first load function (don’t worry understanding code!): can use separate segments look results: now 590 segments 1km 2km long. can check lengths using st_length function plot histogram segment lengths:  Note setting dfMaxLength argument st_segmentize giving rough guide segment length algorithm inside st_segmentize tries get segments equal length possible (note \\(x\\) axis histogram). Note also Transect column segs now duplicate entries transect multiple segments . can now create required columns dsm. First, Effort column can generated using st_length: can generate Sample.Labels. ’ll use complicated naming scheme show ’s done, one simply write segs$Sample.Label <- 1:nrow(segs) sufficient. Instead, like Sample.Label “YEAR-AREA-LINE-SEGMENT” scheme suggested . fancier ways , clarity, let’s use loop: Now can check looks right: required columns taken care , can also calculate centroids segments get locations segment use spatial model. Handily can use st_centroid one step keep data time. get warning centroids might calculated correctly latitude/longitude used. need project data first (Rhode Island State Plane) step using st_transform. Notice geometry type now POINT. can plot centroids previous map:  present, dsm package doesn’t know talk sf objects, final step need simplify segs regular data.frame, dsm can interpret. can extract coordinates centroids using st_coordinates append onto data.frame version object geometry dropped, like : can double check looks right: done. See next section link segments detections. want include spatial covariate information segment table, use (example) rerrdap obtain remotely-sensed data. situ data (.e., weather conditions recorded effort), can complicated require use summarize, see thread information deal situation.","code":"library(ggplot2) library(sf)  # coast data, just for reference coastline <- read_sf(\"data/ri_coast.shp\")  # our transect lines transects <- read_sf(\"data/ri_transects.shp\")  # now make the plot p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects, aes(colour=Transect)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal()  print(p) transects ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86581 41.25891) ##  2        2  (-71.82872 41.30738, -71.82145 41.2258) ##  3        3 (-71.78805 41.31562, -71.75676 40.94846) ##  4        4 (-71.74674 41.31728, -71.71394 40.92971) ##  5        5  (-71.70589 41.3241, -71.67156 40.91612) ##  6        6 (-71.66578 41.33943, -71.62329 40.82944) ##  7        7   (-71.5435 41.36397, -71.5064 40.91516) ##  8        8  (-71.5015 41.35994, -71.45266 40.82978) ##  9        9 (-71.46131 41.37378, -71.42458 40.92496) ## 10       10   (-71.421 41.38723, -71.37562 40.83641) ## # ℹ 16 more rows # project transects transects <- st_transform(transects, 6348)  # do the segmenting segs <- st_segmentize(transects, dfMaxLength=units::set_units(2000, \"metres\"))  # transform back to lat/long segs <- st_transform(segs, 4326) transects <- st_transform(transects, 4326) segs ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                                             geometry ##  *    <dbl>                                                     <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86826 41.2861, -71.86703 41.2725, -71.8658… ##  2        2 (-71.82872 41.30738, -71.82727 41.29106, -71.82581 41.27475, -71.82… ##  3        3 (-71.78805 41.31562, -71.78654 41.29813, -71.78505 41.28065, -71.78… ##  4        4 (-71.74674 41.31728, -71.74524 41.29967, -71.74373 41.28205, -71.74… ##  5        5 (-71.70589 41.3241, -71.70438 41.30636, -71.70288 41.28863, -71.701… ##  6        6 (-71.66578 41.33943, -71.6643 41.32185, -71.66283 41.30426, -71.661… ##  7        7 (-71.5435 41.36397, -71.542 41.34602, -71.54051 41.32807, -71.53901… ##  8        8 (-71.5015 41.35994, -71.49986 41.34227, -71.49821 41.3246, -71.4965… ##  9        9 (-71.46131 41.37378, -71.45983 41.35583, -71.45835 41.33788, -71.45… ## 10       10 (-71.421 41.38723, -71.41952 41.36946, -71.41804 41.3517, -71.41656… ## # ℹ 16 more rows st_coordinates(transects[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86581 41.25891  1 st_coordinates(segs[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86826 41.28610  1 ## [3,] -71.86703 41.27250  1 ## [4,] -71.86581 41.25891  1 stdh_cast_substring <- function(x, to = \"MULTILINESTRING\") {   ggg <- st_geometry(x)    if (!unique(st_geometry_type(ggg)) %in% c(\"POLYGON\", \"LINESTRING\")) {     stop(\"Input should be  LINESTRING or POLYGON\")   }   for (k in 1:length(st_geometry(ggg))) {     sub <- ggg[k]     geom <- lapply(       1:(length(st_coordinates(sub)[, 1]) - 1),       function(i)         rbind(           as.numeric(st_coordinates(sub)[i, 1:2]),           as.numeric(st_coordinates(sub)[i + 1, 1:2])         )     ) %>%       st_multilinestring() %>%       st_sfc()      if (k == 1) {       endgeom <- geom     }     else {       endgeom <- rbind(endgeom, geom)     }   }   endgeom <- endgeom %>% st_sfc(crs = st_crs(x))   if (class(x)[1] == \"sf\") {     endgeom <- st_set_geometry(x, endgeom)   }   if (to == \"LINESTRING\") {     endgeom <- endgeom %>% st_cast(\"LINESTRING\")   }   return(endgeom) } segs <- stdh_cast_substring(segs, to=\"LINESTRING\") ## Warning in st_cast.sf(., \"LINESTRING\"): repeating attributes for all ## sub-geometries for which they may not be constant segs ## Simple feature collection with 590 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1  (-71.86948 41.29969, -71.86826 41.2861) ##  2        1   (-71.86826 41.2861, -71.86703 41.2725) ##  3        1  (-71.86703 41.2725, -71.86581 41.25891) ##  4        2 (-71.82872 41.30738, -71.82727 41.29106) ##  5        2 (-71.82727 41.29106, -71.82581 41.27475) ##  6        2 (-71.82581 41.27475, -71.82436 41.25843) ##  7        2  (-71.82436 41.25843, -71.8229 41.24212) ##  8        2   (-71.8229 41.24212, -71.82145 41.2258) ##  9        3 (-71.78805 41.31562, -71.78654 41.29813) ## 10        3 (-71.78654 41.29813, -71.78505 41.28065) ## # ℹ 580 more rows hist(st_length(segs), xlab=\"Segment lengths (m)\", main=\"\") segs$Effort <- st_length(segs) # create a dummy column that we can fill in as we go segs$Sample.Label <- NA  # set the year and area once for this data year <- 2010 area <- \"RI\"  # loop over the transect IDs for(this_transect in unique(segs$Transect)){   # how many segments in this transect?   n_segs <- nrow(subset(segs, Transect==this_transect))   # generate the n_segs labels that we need   segs$Sample.Label[segs$Transect==this_transect] <- paste(year, area, this_transect,                                                            1:n_segs, sep=\"-\") } segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect                                 geometry Effort Sample.Label ##  *    <dbl>                         <LINESTRING [°]>    [m] <chr>        ##  1        1  (-71.86948 41.29969, -71.86826 41.2861)  1515. 2010-RI-1-1  ##  2        1   (-71.86826 41.2861, -71.86703 41.2725)  1515. 2010-RI-1-2  ##  3        1  (-71.86703 41.2725, -71.86581 41.25891)  1515. 2010-RI-1-3  ##  4        2 (-71.82872 41.30738, -71.82727 41.29106)  1818. 2010-RI-2-1  ##  5        2 (-71.82727 41.29106, -71.82581 41.27475)  1818. 2010-RI-2-2  ##  6        2 (-71.82581 41.27475, -71.82436 41.25843)  1818. 2010-RI-2-3  ##  7        2  (-71.82436 41.25843, -71.8229 41.24212)  1818. 2010-RI-2-4  ##  8        2   (-71.8229 41.24212, -71.82145 41.2258)  1818. 2010-RI-2-5  ##  9        3 (-71.78805 41.31562, -71.78654 41.29813)  1948. 2010-RI-3-1  ## 10        3 (-71.78654 41.29813, -71.78505 41.28065)  1948. 2010-RI-3-2  ## # ℹ 580 more rows # save the line version of segs for plotting later segs_lines <- segs  # project segs segs <- st_transform(segs, 6348)  # find centroids segs <- st_centroid(segs) ## Warning: st_centroid assumes attributes are constant over geometries # project back to lat/long segs <- st_transform(segs, 4326)  # how does that look? segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86887 ymin: 40.83823 xmax: -70.89506 ymax: 41.45646 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect             geometry Effort Sample.Label ##  *    <dbl>          <POINT [°]>    [m] <chr>        ##  1        1 (-71.86887 41.29289)  1515. 2010-RI-1-1  ##  2        1  (-71.86764 41.2793)  1515. 2010-RI-1-2  ##  3        1  (-71.86642 41.2657)  1515. 2010-RI-1-3  ##  4        2 (-71.82799 41.29922)  1818. 2010-RI-2-1  ##  5        2  (-71.82654 41.2829)  1818. 2010-RI-2-2  ##  6        2 (-71.82508 41.26659)  1818. 2010-RI-2-3  ##  7        2 (-71.82363 41.25028)  1818. 2010-RI-2-4  ##  8        2 (-71.82218 41.23396)  1818. 2010-RI-2-5  ##  9        3 (-71.78729 41.30687)  1948. 2010-RI-3-1  ## 10        3  (-71.7858 41.28939)  1948. 2010-RI-3-2  ## # ℹ 580 more rows p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=segs) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) segs_df <- cbind(as.data.frame(st_drop_geometry(segs)),               st_coordinates(segs)) head(segs_df) ##   Transect       Effort Sample.Label         X        Y ## 1        1 1515.147 [m]  2010-RI-1-1 -71.86887 41.29289 ## 2        1 1515.151 [m]  2010-RI-1-2 -71.86764 41.27930 ## 3        1 1515.155 [m]  2010-RI-1-3 -71.86642 41.26570 ## 4        2 1818.179 [m]  2010-RI-2-1 -71.82799 41.29922 ## 5        2 1818.184 [m]  2010-RI-2-2 -71.82654 41.28290 ## 6        2 1818.190 [m]  2010-RI-2-3 -71.82508 41.26659"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"linking-observations-and-segments","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Linking observations and segments","title":"`dsm` data formatting example","text":"link together data detection function spatial data giving effort, use observation data.frame. really just cross-reference two tables, detection lives one segment. observation data.frame must (least) following columns: object : unique object identifier (corresponding identifiers detection function) Sample.Label : identifier segment observation occurred size : size observed group (e.g., 1 animals occurred individually) distance : distance observation observation data many rows detection function.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"relating-observations-and-segments-in-practice","dir":"Articles > Data_format","previous_headings":"Table construction > Linking observations and segments","what":"Relating observations and segments in practice","title":"`dsm` data formatting example","text":"methods building observation table. ’ll illustrate one assigning detections segments based distance (.e., detections associated closest segment centroid). appropriate don’t see forwards far, ’s unlikely detections miss assigned. true, instance, aerial survey observers look windows located sides plane might inappropriate shipboard survey observers flying bridge can see way ahead ship. First loading detection data survey (use fit detection function, post-processing): can see columns latitude longitude (Lat Long). can make data.frame sf object telling sf columns contain spatial coordinates (sf lingo, “geometry”): can overlay previous map transects:  Going back segs object , can use st_join function, combined st_nearest_feature function control tables linked. , need project data avoid issues. Now obs acquired additional columns segs, including Sample.Label (want) Effort (don’t). check worked okay, can plot obs segs_lines (line version segments saved earlier) colour coding Sample.Label. randomized colour order easier tell observations misallocated.  segs can remove unnecessary columns geometry get required columns : (Note since binned data, just keep Bin column need process later.) different way approach timestamps waypoints GPS, related segments. One look whether observation made start end times segment.","code":"obs <- read.csv(\"data/loons.csv\") head(obs) ##         Date     Time Bin      Lat      Long Observer object size ## 1 2011-04-19 18991230   B 40.85296 -71.45511       JV     46    1 ## 2 2011-02-17 18991230   A 40.87022 -71.54842       JV    106    1 ## 3 2011-05-02 18991230   A 40.87595 -71.30304       KW    121    1 ## 4 2011-12-04 18991230   C 40.91753 -71.38273       KW    286    1 ## 5 2011-02-23 18991230   A 40.94153 -71.38517       KW    446    1 ## 6 2011-02-07 18991230   A 40.94225 -71.46438       KW    457    1 obs <- st_as_sf(obs, coords=c(\"Long\", \"Lat\")) # set the coordinate system st_crs(obs) <- 4326 obs ## Simple feature collection with 941 features and 6 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size                   geometry ## 1  2011-04-19 18991230   B       JV     46    1 POINT (-71.45511 40.85296) ## 2  2011-02-17 18991230   A       JV    106    1 POINT (-71.54842 40.87022) ## 3  2011-05-02 18991230   A       KW    121    1 POINT (-71.30304 40.87595) ## 4  2011-12-04 18991230   C       KW    286    1 POINT (-71.38273 40.91753) ## 5  2011-02-23 18991230   A       KW    446    1 POINT (-71.38517 40.94153) ## 6  2011-02-07 18991230   A       KW    457    1 POINT (-71.46438 40.94225) ## 7  2011-04-19 18991230   A       JV    500    2 POINT (-71.59495 40.94761) ## 8  2011-05-02 18991230   A       KW    506    1   POINT (-71.552 40.94795) ## 9  2011-02-23 18991230   B       JV    814    1 POINT (-71.51172 40.97912) ## 10 2011-04-19 18991230   A       KW    911    1 POINT (-71.59762 40.98571) p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=obs, size=0.5) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) # project segs segs <- st_transform(segs, 6348) obs <- st_transform(obs, 6348)  # do the join obs <- st_join(obs, segs, join=st_nearest_feature)   # project back to lat/long segs <- st_transform(segs, 4326) obs <- st_transform(obs, 4326)  # how does that look? obs ## Simple feature collection with 941 features and 9 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size Transect       Effort ## 1  2011-04-19 18991230   B       JV     46    1        8 1969.873 [m] ## 2  2011-02-17 18991230   A       JV    106    1       26 1894.100 [m] ## 3  2011-05-02 18991230   A       KW    121    1       12 1988.809 [m] ## 4  2011-12-04 18991230   C       KW    286    1       10 1979.630 [m] ## 5  2011-02-23 18991230   A       KW    446    1       10 1979.624 [m] ## 6  2011-02-07 18991230   A       KW    457    1        8 1969.839 [m] ## 7  2011-04-19 18991230   A       JV    500    2       24 1923.218 [m] ## 8  2011-05-02 18991230   A       KW    506    1       26 1894.075 [m] ## 9  2011-02-23 18991230   B       JV    814    1        7 2000.139 [m] ## 10 2011-04-19 18991230   A       KW    911    1       24 1923.199 [m] ##     Sample.Label                   geometry ## 1   2010-RI-8-29 POINT (-71.45511 40.85296) ## 2  2010-RI-26-16 POINT (-71.54842 40.87022) ## 3  2010-RI-12-31 POINT (-71.30304 40.87595) ## 4  2010-RI-10-27 POINT (-71.38273 40.91753) ## 5  2010-RI-10-26 POINT (-71.38517 40.94153) ## 6   2010-RI-8-24 POINT (-71.46438 40.94225) ## 7  2010-RI-24-12 POINT (-71.59495 40.94761) ## 8  2010-RI-26-12   POINT (-71.552 40.94795) ## 9   2010-RI-7-22 POINT (-71.51172 40.97912) ## 10  2010-RI-24-9 POINT (-71.59762 40.98571) # make a random colour palette to avoid similar colours being near each other pal <- rainbow(nrow(segs), s=.6, v=.9)[sample(1:nrow(segs),nrow(segs))] p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=segs_lines, aes(colour=Sample.Label), pch=21) +   geom_sf(data=obs, size=0.5, aes(colour=Sample.Label)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   scale_colour_manual(values=pal) +   # make the plot look simpler   theme_minimal() +   theme(legend.position = \"none\") print(p) # get rid of \"spatialness\" obs <- st_drop_geometry(obs) # select the columns we need obs <- obs[, c(\"object\", \"Sample.Label\", \"size\", \"Bin\")] head(obs) ##   object  Sample.Label size Bin ## 1     46  2010-RI-8-29    1   B ## 2    106 2010-RI-26-16    1   A ## 3    121 2010-RI-12-31    1   A ## 4    286 2010-RI-10-27    1   C ## 5    446 2010-RI-10-26    1   A ## 6    457  2010-RI-8-24    1   A"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"more-information","dir":"Articles > Data_format","previous_headings":"","what":"More information","title":"`dsm` data formatting example","text":"provided information segmenting wiki page, part US Navy-funded project DenMod. information summarized ?\"dsm-data\" dsm package. may also useful look data example dataset mexdolphins can loaded data(mexdolphins). vignette analysis data available . hard give general information segment lines, extent depends data originally formatted (going way back make model GPS unit used). information dealing spatial data R using sf package available R spatial website (see “Articles” drop header). Another source help distance sampling mailing list. sure search archives prior posting several threads segmenting previously might helpful.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"acknowledgements","dir":"Articles > Data_format","previous_headings":"","what":"Acknowledgements","title":"`dsm` data formatting example","text":"Thanks Phil Bouchet providing helpful comments early version document Iúri Correia finding important bug.","code":""},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"introduction","dir":"Articles > Lines_gomex","previous_headings":"","what":"Introduction","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"analysis based dataset observations pantropical dolphins Gulf Mexico (shipped Distance 6.0 later). convenience data bundled R-friendly format, although code necessary creating data Distance project files available github. OBIS-SEAMAP page data may found SEFSC GoMex Oceanic 1996 survey page. intention highlight features dsm package, rather perform full analysis data. reason, important steps fully explored. familiarity density surface modelling (Miller, Burt, Rexstad, & Thomas, 2013) (Hedley & Buckland, 2004) assumed.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"preamble","dir":"Articles > Lines_gomex","previous_headings":"","what":"Preamble","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"start, load dsm package (dependencies) set options: order run vignette, ’ll need install R packages. can done via following call install.packages:","code":"library(dsm) library(ggplot2)  # plotting options gg.opts <- theme(panel.grid.major=element_blank(),                  panel.grid.minor=element_blank(),                  panel.background=element_blank()) install.packages(c(\"dsm\", \"Distance\", \"knitr\", \"distill\", \"ggplot2\", \"rgdal\",                    \"maptools\", \"plyr\", \"tweedie\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"the-data","dir":"Articles > Lines_gomex","previous_headings":"","what":"The data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data need included dsm package, two additional objects needed plotting required can downloaded put directory file. data can loaded R using following code: add objects survey.area pred.polys environment.","code":"load(\"mexdolphins-extra.rda\")"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"observation-and-segment-data","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Observation and segment data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data analysis nicely pre-formatted shipped dsm. Loading data, can see four data.frames, first lines shown: segdata holds segment data: transects already “chopped” segments. distdata holds distance sampling data used fit detection function. obsdata links distance data segments. preddata holds prediction grid (includes necessary covariates). Typically (.e. datasets) necessary divide transects segments, allocate observations correct segments using GIS similar package1, starting analysis using dsm.","code":"data(mexdolphins) head(segdata) ##   longitude latitude        x        y Effort Transect.Label Sample.Label depth ## 1 -86.92712 29.94378 836105.9 -1011416  13800       19960417   19960417-1 135.0 ## 2 -86.83176 29.84030 846012.9 -1021407  14000       19960417   19960417-2 147.7 ## 3 -86.74445 29.75279 855022.6 -1029785  14000       19960417   19960417-3 152.1 ## 4 -86.65230 29.65522 864610.3 -1039168  13900       19960417   19960417-4 163.8 ## 5 -86.56648 29.56088 873598.1 -1048266  13800       19960417   19960417-5 179.7 ## 6 -86.49290 29.49000 881203.7 -1055004  13800       19960417   19960417-6 188.5 head(distdata) ##     object size  distance Effort detected beaufort latitude longitude ## 45      45   21 3296.6363  36300        1        4 27.72872 -86.00159 ## 61      61  150  929.1937  17800        1        4 25.99896 -87.62712 ## 63      63  125 6051.0009  21000        1        2 26.00693 -87.94881 ## 85      85   75 5499.6971  21800        1        1 27.50344 -90.44891 ## 114    114   50 7258.9837  13400        1        3 27.40568 -94.99483 ## 120    120   45 1454.7962  20900        1        5 26.01765 -95.97449 ##              x        y ## 45  948000.065 -1236192 ## 61  812161.653 -1436899 ## 63  780969.520 -1438985 ## 85  528656.807 -1297833 ## 114  95910.149 -1324562 ## 120   2477.665 -1473909 head(obsdata) ##     object Sample.Label size  distance Effort ## 45      45   19960421-9   21 3296.6363  36300 ## 61      61   19960423-7  150  929.1937  17800 ## 63      63   19960423-9  125 6051.0009  21000 ## 85      85   19960427-1   75 5499.6971  21800 ## 114    114   19960430-8   50 7258.9837  13400 ## 120    120   19960501-5   45 1454.7962  20900 head(preddata) ##   latitude longitude        x          y depth      area ## 1 30.08333 -87.58333 774402.9 -1002759.1    35 271236913 ## 2 30.08333 -87.41667 789688.6 -1001264.5    30 271236913 ## 3 30.08333 -87.25000 804971.3  -999740.6    27 271236913 ## 4 30.08333 -87.08333 820251.1  -998187.5    22 271236913 ## 5 30.08333 -86.91667 835528.0  -996605.2    46 271236913 ## 6 29.91667 -87.75000 760783.1 -1021810.3    14 271236913"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"shapefiles-and-converting-units","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Shapefiles and converting units","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often data spatial analysis comes many different sources. important ensure measurements used analysis compatible units, otherwise resulting estimates incorrect hard interpret. measurements SI units outset removes need conversion later, making life much easier. data already appropriate units (Northings Eastings: kilometres centroid, projected using North American Lambert Conformal Conic projection). extensive literature particular projections latitude longitude appropriate highly recommend reader review particular study area; (Bivand, Pebesma, & Gómez-Rubio, 2008) good starting point. data frames already measurements appropriately converted. convention directions named x y. Using latitude longitude performing spatial smoothing can problematic certain smoother bases used. particular bivariate isotropic bases used non-isotropic nature latitude longitude inconsistent (moving one degree one direction moving one degree ). give example projecting polygon defines survey area (simply read R using readShapeSpatial shapefile produced GIS). Next bit code works study.area outline, massaging use soap film smoother. using soap film smoothers, code extraneous. code generates plot, shows survey area transect lines overlaid (using data segdata).  Also note since projected prediction grid, “squares” look quite like squares. plotting use polygons saved, polygons (stored pred.polys) read shapefile created GIS, object class SpatialPolygons sp package. needs project proper plotting using functions sf package. predsf <- st_as_sf(pred.polys)  Tips plotting polygons available ggplot2 pkg_down.","code":"library(sf) library(plyr)  # tell R that the survey.area object is currently in lat/long sp::proj4string(survey.area) <- sp::CRS(\"+proj=longlat +datum=WGS84\")  predsf <- st_as_sf(pred.polys)  area.sf <- st_as_sf(survey.area) st_crs(area.sf) <- \"WGS84\" area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))  # Convert preddata to a spatial object preddata_sf <- st_as_sf(preddata, coords=c(\"x\", \"y\")) st_crs(preddata_sf) <- st_crs(area.sf.proj) # Perform the intersection preddata_sf <- st_intersection(preddata_sf, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries coords_preddata <- data.frame(st_coordinates(preddata_sf))  preddata_sf$x <- coords_preddata$X preddata_sf$y <- coords_preddata$Y # proj 4 string # using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/ lcc_proj4 <- sp::CRS(\"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs \")  # project using LCC survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)  # simplify the object survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords) names(survey.area) <- c(\"x\", \"y\") segdata_sf <- st_as_sf(segdata, coords = c(\"x\",\"y\")) st_crs(segdata_sf) <- st_crs(area.sf.proj) # study area outline and segment centres plot_segments <- ggplot() +   geom_sf(data = area.sf.proj, fill=\"lightblue\", color = \"blue\", linewidth=.1) +   geom_sf(data=segdata_sf, fill=NA, color=\"black\", linewidth=.3) +   labs(title=\"1996 SE Fisheries Science Center Gulf of Mexico cruise\",        subtitle = \"Points are segment centres\") +   scale_fill_viridis_c(option = \"magma\", guide = \"none\") plot_segments predsf <- st_as_sf(pred.polys) # plot as projected plot(st_geometry(predsf), axes=TRUE)"},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"distance-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Distance data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top panels EDA plots show histograms observed distances cluster size, bottom panels show relationship observed distance observed cluster size, relationship observed distance Beaufort sea state. plots show relationship cluster size observed distance (fewer smaller clusters seem seen larger distances). following code generates EDA plots:  Top row, left right: histograms distance cluster size; bottom row: plot distance cluster size plot distances Beaufort sea state.","code":"par(mfrow=c(2,2))  # histograms hist(distdata$distance,main=\"\",xlab=\"Distance (m)\") hist(distdata$size,main=\"\",xlab=\"Cluster size\")  # plots of distance vs. cluster size plot(distdata$distance, distdata$size, main=\"\", xlab=\"Distance (m)\",      ylab=\"Group size\", pch=19, cex=0.5, col=gray(0.7))  # lm fit l.dat <- data.frame(distance=seq(0,8000,len=1000)) lo <- lm(size~distance, data=distdata) lines(l.dat$distance, as.vector(predict(lo,l.dat)))  plot(distdata$distance,distdata$beaufort, main=\"\", xlab=\"Distance (m)\",      ylab=\"Beaufort sea state\", pch=19, cex=0.5, col=gray(0.7))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Spatial data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Looking separately spatial data without thinking distances, can plot observed group sizes space (point size proportional group size observation). Circle size indicates size group observation. rather large areas observations, might cause variance estimates abundance rather large. also see depth data use depth later explanatory covariate spatial model.","code":"prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid) cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries depth <- ggplot() +          geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +          labs(title = \"Spotted dolphins, Gulf of Mexico\",               subtitle = \"Depth in meters, size of detected dolphin groups\") +          xlab(\"Longitude\") + ylab(\"Latitude\") +          geom_point(aes(x, y, size=size), data=distdata, colour=\"red\",alpha=I(0.5)) +          scale_fill_viridis_c(option = \"viridis\", direction = 1) depth"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"estimating-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"","what":"Estimating the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"use ds function package Distance fit detection function. (Distance package intended make standard distance sampling R relatively straightforward. flexible complex alternative, see function ddf mrds library.) First, loading Distance library: can fit detection function hazard-rate key adjustment terms: Calling summary gives us information parameter estimates, probability detection, AIC, etc: following code generates plot fitted detection function (left) quantile-quantile plot (right):  quantile-quantile plot show relatively good goodness fit hazard-rate detection function.","code":"library(Distance) detfc.hr.null <- ds(distdata, max(distdata$distance), key=\"hr\", adjustment=NULL) ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). summary(detfc.hr.null) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  841.1841  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##             estimate        se ## (Intercept) 7.986391 0.5239151 ##  ## Shape coefficient(s):   ##                estimate         se ## (Intercept) 0.000237466 0.02958901 ##  ##                       Estimate         SE        CV ## Average p            0.5918795  0.1667006 0.2816462 ## N in covered region 79.4080596 23.5573051 0.2966614 plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2) gof_ds(detfc.hr.null) ##  ## Goodness of fit results for ddf object ##  ## Distance sampling Cramer-von Mises test (unweighted) ## Test statistic = 0.0976846 p-value = 0.596345"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-covariates-to-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Estimating the detection function","what":"Adding covariates to the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"common include covariates detection function (-called Multiple Covariate Distance Sampling MCDS). dataset two covariates collected individual: Beaufort sea state size. brevity fit hazard-rate detection functions sea state included factor covariate follows: looking summary, detection function covariates give lower AIC model without covariates (843.71 vs. 841.18 hazard-rate model without covariates). Looking back bottom-right panel EDA plots, can see discernible pattern plot Beaufort vs distance. brevity, detection function model selection omitted . practise fit many different forms detection function (select model based goodness fit testing AIC).","code":"detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),                   key=\"hr\", adjustment=NULL) summary(detfc.hr.beau) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  843.7115  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##                         estimate        se ## (Intercept)           7.66291936  1.078972 ## as.factor(beaufort)2  2.24809809 14.754677 ## as.factor(beaufort)3  0.28581820  1.020809 ## as.factor(beaufort)4  0.07133776  1.225360 ## as.factor(beaufort)5 -0.36421115  1.540324 ##  ## Shape coefficient(s):   ##              estimate        se ## (Intercept) 0.2994644 0.5184204 ##  ##                       Estimate         SE        CV ## Average p            0.5420677  0.1753555 0.3234937 ## N in covered region 86.7050383 29.4646397 0.3398262"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"fitting-a-dsm","dir":"Articles > Lines_gomex","previous_headings":"","what":"Fitting a DSM","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"fitting dsm model, data must segmented; consists chopping transects attributing counts segments. mentioned , data already segmented.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"a-simple-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"A simple model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"begin simple model. assume number individuals segment quasi-Poisson distributed smooth function spatial coordinates (note formula exactly one specify gam mgcv). setting group=TRUE, abundance clusters/groups rather individuals can estimated (though ignore ). Note set method=\"REML\" ensure smooth terms estimated reliably. Running model: can obtain summary fitted model: exact interpretation model summary results can found (S. N. Wood, 2017); can see various information smooth components fitted general model statistics. can use deviance explained compare models2. can also get rough idea smooth space looks like using vis.gam (white/yellow indicates high values, red low indicates low values):  type=\"response\" argument ensures plot scale abundance values relative (offsets set median values). means plot useful get idea general shape smooth interpreted directly.","code":"dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)   -18.20       0.53  -34.34   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##         edf Ref.df     F  p-value     ## s(x,y) 24.8  27.49 2.354 0.000733 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.121   Deviance explained = 43.4% ## -REML = 936.04  Scale est. = 94.367    n = 387 vis.gam(dsm.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-another-environmental-covariate-to-the-spatial-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Adding another environmental covariate to the spatial model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data set also contains depth covariate (plotted ). can include model simply: see drop deviance explained, perhaps model useful first. discuss setting k parameter Model checking, . Setting select=TRUE (argument gam) impose extra shrinkage terms smooth model (allowing smooth terms removed model fitting; see ?gam information). particularly useful , include . However many environmental predictors model can good way (along looking \\(p\\)-values) perform term selection. Simply calling plot model object allows us look relationship depth linear predictor:  Omitting argument select call plot plot smooth terms, one time.","code":"dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy.depth) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, k = 10) + s(depth, k = 20) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.741      1.236  -15.16   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F p-value   ## s(x,y)   6.062  7.371 0.923  0.5038   ## s(depth) 9.443 11.466 1.585  0.0824 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0909   Deviance explained = 34.3% ## -REML = 939.52  Scale est. = 124.9     n = 387 plot(dsm.xy.depth, select=2)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-models-when-there-are-covariates-in-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Spatial models when there are covariates in the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"code fit DSM covariates detection function similar models, . However since detection function observation-level covariates, must estimate abundance per segment using Horvitz-Thompson-like estimator modelling, change response abundance.est: can see, summary results rather similar: resulting spatial smooth (though resulting surface somewhat “amplified”):","code":"dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method=\"REML\") summary(dsm.est.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## abundance.est ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -18.0233     0.4739  -38.03   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value     ## s(x,y) 24.64  27.44 2.349 0.00029 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.129   Deviance explained = 42.2% ## -REML = 1043.6  Scale est. = 174.1     n = 387 vis.gam(dsm.est.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", zlim=c(0, 300), contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-response-distributions","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other response distributions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often quasi-Poisson distribution doesn’t give adequate flexibility capture overdispersion response data (see Model checking Model selection ), illustrate two additional distributions can used count data. models section, ’ll move back count response, though estimated abundance also work.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"tweedie","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Tweedie","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Response distributions quasi-Poisson can used, example Tweedie distribution. Tweedie distribution available dsm setting family=tw().","code":"dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method=\"REML\") summary(dsm.xy.tweedie) ##  ## Family: Tweedie(p=1.347)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -17.2651     0.2363  -73.08   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value   ## s(x,y) 12.82  17.13 1.628  0.0562 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0684   Deviance explained = 26.8% ## -REML = 351.49  Scale est. = 60.26     n = 387"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"negative-binomial","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Negative binomial","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Though used , similarly, two options negative binomial distribution: negbin nb. former requires user specification single parameter theta range values parameter (specified vector), latter estimates value theta model fitting process (generally faster). latter recommended users.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-spatial-modelling-options","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other spatial modelling options","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"large literature spatial modelling using GAMs, much can harnessed DSM context. highlights.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"soap-film-smoothing","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other spatial modelling options","what":"Soap film smoothing","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"account complex region (e.g., region includes peninsulae) can use soap film smoother (Simon N. Wood, Bravington, & Hedley, 2008). use soap film smoother spatial part model must create set knots smoother use. easily done using make.soapgrid() function dsm: second argument specifies number points (direction) grid used create knots (knots grid outside survey.area removed). saw exploratory analysis, transect lines outside survey area. cause soap film smoother fail, remove : Note soap_checker script available can useful ensuring boundary, data knots correct format use soap film smoother. can run model depth covariate along spatial (soap film) smooth. Note k argument now refers complexity boundary smooth soap film, complexity film controlled knots given xt argument.","code":"soap.knots <- make.soapgrid(survey.area,c(15,10)) x <- segdata$x; y<-segdata$y onoff <- inSide(x=x,y=y, bnd=as.list(survey.area)) rm(x,y) segdata.soap <- segdata[onoff,] dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs=\"so\", k=15, xt=list(bnd=list(survey.area))) +             s(depth),           family=tw(), method=\"REML\",           detfc.hr.null, segdata.soap, obsdata, knots=soap.knots) summary(dsm.xy.tweedie.soap) ##  ## Family: Tweedie(p=1.356)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, bs = \"so\", k = 15, xt = list(bnd = list(survey.area))) +  ##     s(depth) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.018      0.405  -44.49   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F  p-value     ## s(x,y)   3.050 72.000 0.064 0.114944     ## s(depth) 5.135  6.196 3.895 0.000793 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0653   Deviance explained = 33.1% ## -REML = 339.26  Scale est. = 54.022    n = 365"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-checking","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model checking","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Fitting models well good, ’d like confirm models reasonable; dsm provides functions model checking. can use gam.check generate diagnostic plots:  show deviation Q-Q plot. “line” points plot residuals vs. linear predictor plot corresponds zeros data. Note well plots, gam.check also produces information model fitting. particular interest us last lines tell us basis size. k parameter provided s (te) terms dsm controls complexity smooths model. setting k parameter specify largest complexity smooth term model; long high enough, can sure enough flexibility. output gam.check , can see “p-value” calculated size basis, can good guide whether basis size needs increased. ?choose.k manual page mgcv gives guidance technical details matter. can look model form Tweedie distribution specified response:  Q-Q plot now seems much better (closer \\(y=x\\) line). plots histogram residuals rather hard interpret due large number zeros data. guidance interpreting gam.check output can found (S. N. Wood, 2017).","code":"gam.check(dsm.xy) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 6 iterations. ## Gradient range [-7.105947e-08,-9.826417e-09] ## (score 936.0363 & scale 94.3674). ## Hessian positive definite, eigenvalue range [5.800328,192.5304]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 24.8    1.01    0.99 gam.check(dsm.xy.tweedie) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 8 iterations. ## Gradient range [-1.513175e-06,3.981813e-06] ## (score 351.486 & scale 60.26034). ## Hessian positive definite, eigenvalue range [0.6230615,103.5094]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 12.8    0.73    0.34"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"randomised-quantile-residuals","dir":"Articles > Lines_gomex","previous_headings":"Model checking","what":"Randomised quantile residuals","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top right panel gam.check plots residuals vs. linear predictor plot includes odd line predictions. artifact link function, showing exact zeros data. can misleading distracting, making difficult see whether residuals show heteroskedasticity. Randomised quantile residuals (Dunn & Smyth, 1996) avoid issue transforming residuals exactly normally distributed. makes residuals vs. linear predictor plot much easier interpret therefore doesn’t include artifacts generated link function. plots can produced using rqgam.check dsm:  can see issue heteroskedasticity (increase spread residuals vs. linear predictor plot increasing values linear predictor). One can also plot residuals covariate values check pattern residuals. Note general, plots “Resids vs. linear pred.” interpreted caution output rqgam.check residuals generated normal construction (example Q-Q plot histogram residuals always look fine).","code":"rqgam_check(dsm.xy.tweedie)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-selection","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model selection","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Assuming models “passed” checks gam.check, rqgam.check sufficiently flexible, may left choice model “best”. several methods choosing best model – AIC, REML/GCV scores, deviance explained, full cross-validation test data . Though document intend full analysis pantropical dolphin data, can create results table compare various models fitted far terms abundance estimates associated uncertainties. can use resulting data.frame build table results using kable function:","code":"# make a data.frame to print out mod_results <- data.frame(\"Model name\" = c(\"`dsm.xy`\", \"`dsm.xy.depth`\", \"`dsm.xy.tweedie`\", \"`dsm.xy.tweedie.soap`\",                                            \"`dsm.est.xy`\"),                           \"Description\" = c(\"Bivariate smooth of location, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie\",                                             \"Soap film smooth of location, smooth of depth, Tweedie\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function\"),                           \"Deviance explained\" = c(unlist(lapply(list(dsm.xy,                                                                       dsm.xy.depth,                                                                       dsm.xy.tweedie,                                                                       dsm.xy.tweedie.soap),                  function(x){paste0(round(summary(x)$dev.expl*100,2),\"%\")})),NA)) kable(mod_results, col.names=c(\"Model name\", \"Description\", \"Deviance explained\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"abundance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Abundance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"model checked selected, can make predictions grid calculate abundance. offset stored area column3. Given predicted abundance estimates cell prediction grid, connected prediction grid object mapping:  can calculate abundance survey area simply summing predictions: can compare plot predictions dsm.xy.depth:  can see inclusion depth model noticeable effect distribution (note difference legend scale two plots). can also look total abundance: see much change abundance, terms abundance alone isn’t much two models. Next ’ll go look variance next can see bigger differences models.","code":"dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area) prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid)  preddata_sf$Prediction_xy <- dsm.xy.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.pred) ## [1] 28430.76 dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)  preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, smooth of depth, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.depth.pred) ## [1] 27054.6"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"variance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Variance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Obviously point estimates abundance important, also calculate uncertainty around abundance estimates. Fortunately dsm provides functions perform calculations display resulting uncertainty estimates. two approaches estimating uncertainty abundance estimate dsm. dsm_var_gam assumes spatial model detection function parts model independent . case squared coefficients variation model component added. dsm_var_prop takes account fact detection probability may correlated spatial part model. uses methods described (Bravington, Miller, & Hedley, 2021). dsm_var_prop can applied covariate detection function varies level segments, recorded segment (example Beaufort). don’t situation , opt dsm_var_gam. methods estimate variance abundance element list provided pred.data. case wish obtain abundance prediction cells, use split chop data set list elements give dsm_var_gam (dsm_var_prop). Calling summary give information uncertainty estimation: can also make plot CVs (transect lines observations overlaid) using following code.  Note increase CV away transect lines. can revisit model included depth location smooths observe coefficient variation model larger model location smooth.","code":"preddata.var <- split(preddata, 1:nrow(preddata)) dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,                           off.set=preddata$area) summary(dsm.xy.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 15252.81 28430.76 52994.04  ## (Using log-Normal approximation) ##  ## Point estimate                 : 28430.76  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.164  ## Total standard error           : 9265.663  ## Total coefficient of variation : 0.3259 preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries CV <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, uncertainty (CV)\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) CV dsm.xy.depth.var <- dsm_var_gam(dsm.xy.depth, pred.data=preddata.var,                                 off.set=preddata$area) summary(dsm.xy.depth.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 14377.57 27054.60 50909.23  ## (Using log-Normal approximation) ##  ## Point estimate                 : 27054.6  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.1741  ## Total standard error           : 8958.444  ## Total coefficient of variation : 0.3311"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"conclusions","dir":"Articles > Lines_gomex","previous_headings":"","what":"Conclusions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"document outlined analysis spatially-explicit distance sampling data using dsm package. Note many possible models can fitted using dsm aim show just options. Results models can rather different, care must taken performing model selection, discrimination criticism.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"software","dir":"Articles > Lines_gomex","previous_headings":"","what":"Software","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Distance available http://github.com/DistanceDevelopment/Distance well CRAN. dsm available http://github.com/DistanceDevelopment/dsm, well CRAN.","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"David L. Miller. Author. Eric Rexstad. Contributor. Louise Burt. Contributor. Mark V. Bravington. Contributor. Sharon Hedley. Contributor. Megan Ferguson. Contributor. Natalie Kelly. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller DL (2024). dsm: Density Surface Modelling Distance Sampling Data. R package version 2.3.3.9001, https://github.com/DistanceDevelopment/dsm.","code":"@Manual{,   title = {dsm: Density Surface Modelling of Distance Sampling Data},   author = {David L. Miller},   year = {2024},   note = {R package version 2.3.3.9001},   url = {https://github.com/DistanceDevelopment/dsm}, }"},{"path":[]},{"path":"/index.html","id":"what-is-dsm","dir":"","previous_headings":"","what":"Density Surface Modelling of Distance Sampling Data","title":"Density Surface Modelling of Distance Sampling Data","text":"dsm provides Generalized Additive Model (GAM)-based approach calculate spatially-explicit estimates animal abundance distance sampling (also presence/absence strip transect) data. Several utility functions provided model checking, plotting variance estimation. Open Access paper describing methods: Miller, DL, ML Burt, EA Rexstad L Thomas (2013). Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution.","code":""},{"path":"/index.html","id":"using-dsm","dir":"","previous_headings":"","what":"Using dsm","title":"Density Surface Modelling of Distance Sampling Data","text":"Examples use dsm distance sampling R packages available examples.distancesampling.org.","code":""},{"path":"/reference/block.info.per.su.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the block information — block.info.per.su","title":"Find the block information — block.info.per.su","text":"Takes transect data works many blocks given size (segment terms) fit .","code":""},{"path":"/reference/block.info.per.su.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the block information — block.info.per.su","text":"","code":"block.info.per.su(block.size, data, name.su)"},{"path":"/reference/block.info.per.su.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the block information — block.info.per.su","text":"block.size number segments per block data data used build model name.su names sampling units (.e., transects)","code":""},{"path":"/reference/block.info.per.su.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the block information — block.info.per.su","text":"data.frame following columns name        sample unit name (e.g. transect label) num.seg     number segments transect num.block   number blocks available start.block block number first block end.block   block number last block num.req     number blocks needed unit","code":""},{"path":"/reference/check.cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names exist — check.cols","title":"Check column names exist — check.cols","text":"Internal function check supplied data.frames correct columns checks sample labels unique.","code":""},{"path":"/reference/check.cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names exist — check.cols","text":"","code":"check.cols(ddf.obj, segment.data, observation.data, segment.area)"},{"path":"/reference/check.cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names exist — check.cols","text":"ddf.obj ddf object mrds segment.data segment data defined dsm observation.data observation data defined dsm segment.area area segments","code":""},{"path":"/reference/check.cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names exist — check.cols","text":"nothing, throws error something went wrong","code":""},{"path":"/reference/check.cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check column names exist — check.cols","text":"David Lawrence Miller","code":""},{"path":"/reference/dsm-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Data format for DSM — dsm-data","title":"Data format for DSM — dsm-data","text":"Two data.frames must provided dsm. referred observation.data segment.data.","code":""},{"path":"/reference/dsm-data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data format for DSM — dsm-data","text":"segment.data table sample identifiers define segments, corresponding effort (line length) expended environmental covariates used model abundance/density. observation.data provides link table observations used detection function samples (segments), can aggregate observations segments (.e., observation.data \"look-table\" observations segments). observation.data - observation data.frame must (least) following columns: object unique object identifier Sample.Label identifier segment observation occurred size size observed group (e.g., 1 animals occurred individually) distance distance observation One can often also use observation.data fit detection function (additional columns detection function covariates allowed table). segment.data: segment data.frame must (least) following columns: Effort effort (terms length segment) Sample.Label identifier segment (unique!) ??? environmental covariates, example location (projected latitude longitude), relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).","code":""},{"path":"/reference/dsm-data.html","id":"multiple-detection-functions","dir":"Reference","previous_headings":"","what":"Multiple detection functions","title":"Data format for DSM — dsm-data","text":"multiple detection functions used, column named ddfobj must included observation.data segment.data. lets model know detection function observation . numeric ordered ddf.obj argument dsm, e.g., ddf.obj=list(ship_ddf, aerial_ddf) means ship detections ddfobj=1 aerial detections ddfobj=2 observation data.","code":""},{"path":"/reference/dsm-data.html","id":"mark-recapture-distance-sampling-models","dir":"Reference","previous_headings":"","what":"Mark-recapture distance sampling models","title":"Data format for DSM — dsm-data","text":"using mrds models include mark-recapture components (currently independent observer trial modes supported) format observation data needs checked ensure observations duplicated. observer column also required observation.data. Independent observer mode unique observations (unique object IDs) required. Trial mode observations made observer 1 required.","code":""},{"path":"/reference/dsm-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Density surface modelling — dsm-package","title":"Density surface modelling — dsm-package","text":"dsm implements spatial models distance sampling data. Models detectability can fitted using packages mrds Distance. dsm fits generalized additive models spatially-referenced data. See Miller et al (2013) introduction.","code":""},{"path":"/reference/dsm-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density surface modelling — dsm-package","text":"information distance sampling methods example code available http://distancesampling.org/R/. help distance sampling package, Google Group https://groups.google.com/forum/#!forum/distance-sampling. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density surface modelling — dsm-package","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm.cor","title":"Check for autocorrelation in residuals — dsm.cor","text":"function deprecated, use dsm_cor.","code":""},{"path":"/reference/dsm.cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm.cor","text":"","code":"dsm.cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm.cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm.cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Fits density surface model (DSM) detection adjusted counts spatially-referenced distance sampling analysis. dsm takes observations animals, allocates segments line (strip transects) optionally adjusts counts based detectability using supplied detection function model. generalized additive model, generalized mixed model generalized linear model used model adjusted counts based formula involving environmental covariates.","code":""},{"path":"/reference/dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"dsm(   formula,   ddf.obj,   segment.data,   observation.data,   engine = \"gam\",   convert.units = 1,   family = quasipoisson(link = \"log\"),   group = FALSE,   control = list(keepData = TRUE),   availability = 1,   segment.area = NULL,   weights = NULL,   method = \"REML\",   ... )"},{"path":"/reference/dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"formula formula surface. valid formula. See \"Details\", , define response. ddf.obj result call ddf ds. multiple detection functions required list can provided. strip/circle transects assumed objects observed, see dummy_ddf. Mark-recapture distance sampling (mrds) models type io (independent observers) trial allowed. segment.data segment data, see dsm-data. observation.data observation data, see dsm-data. engine fitting engine used DSM (\"glm\"/\"gam\"/\"gamm\"/\"bam\"). convert.units conversion factor multiply area segments . See 'Units' . family response distribution (popular choices include quasipoisson, Tweedie/tw negbin/nb). Defaults quasipoisson. group TRUE abundance groups calculated rather abundance individuals. Setting option TRUE equivalent setting size group 1. control usual control argument gam; keepData must TRUE variance estimation work (though option set GLMs GAMMs). availability estimate availability bias. count models used multiply effective strip width (must vector length 1 length number rows segment.data); estimated abundance/estimated density models used scale response (must vector length 1 length number rows observation.data). Uncertainty availability handled present. segment.area NULL (default) segment areas calculated multiplying Effort column segment.data (right minus left) truncation distance ddf.obj strip.width. Alternatively vector segment areas can provided (must length number rows segment.data) character string giving name column segment.data contains areas. segment.area specified takes precedent. weights weights observation used model fitting. default, weights=NULL, weights observation area (see Details). Setting scalar value (e.g., weights=1) observations equally weighted. method smoothing parameter estimation method. Default \"REML\", using Restricted Maximum Likelihood. See gam options. Ignored engine=\"glm\". ... anything else passed straight glm, gam, gamm bam.","code":""},{"path":"/reference/dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"glm, gam, gamm bam object, additional element, $ddf holds detection function object.","code":""},{"path":"/reference/dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"response (LHS formula) can one following (restrictions outlined ): count count segment abundance.est estimated abundance per segment, estimation via Horvitz-Thompson estimator density.est density per segment offset used model dependent response: count area segment multiplied average probability detection segment abundance.est area segment density zero count response can used detection function covariates vary segments/points (within). example, weather conditions (like visibility sea state) foliage cover usually acceptable change within segment, animal sex behaviour work. abundance.est response can used covariates detection function. density case, observations can weighted segment areas via weights= argument. default (weights=NULL), density estimated weights set segment areas (using segment.area calculated detection function object metadata Effort data). Alternatively weights=1 set weights equal. third alternative pass vector length equal number segments, containing appropriate weights. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"often case distances collected metres segment lengths recorded kilometres. dsm allows provide conversion factor (convert.units) multiply areas . example: distances metres segment lengths kilometres setting convert.units=1000 lead analysis metres. Setting convert.units=1/1000 lead analysis kilometres. conversion factor applied segment.area specified.","code":""},{"path":"/reference/dsm.html","id":"large-models","dir":"Reference","previous_headings":"","what":"Large models","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"large models, engine=\"bam\" method=\"fREML\" may useful. Models specified bam gam. Read bam using option; option considered EXPERIMENTAL moment. particular note default basis choice (thin plate regression splines) slow general fitting less stable using gam. negative binomial response, theta must specified using bam.","code":""},{"path":"/reference/dsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"David L. Miller","code":""},{"path":"/reference/dsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) summary(mod1)  # predict over a grid mod1.pred <- predict(mod1, preddata, preddata$area)  # calculate the predicted abundance over the grid sum(mod1.pred)  # plot the smooth plot(mod1) }"},{"path":"/reference/dsm.var.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm.var.gam","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"function deprecated, use dsm_var_gam.","code":""},{"path":"/reference/dsm.var.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"","code":"dsm.var.gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm.var.movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"function deprecated, use dsm_var_movblk.","code":""},{"path":"/reference/dsm.var.movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"","code":"dsm.var.movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm.var.movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL -- file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm.var.prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm.var.prop","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"function deprecated, use dsm_var_prop.","code":""},{"path":"/reference/dsm.var.prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"","code":"dsm.var.prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm_cor","title":"Check for autocorrelation in residuals — dsm_cor","text":"DSM fitted data, function can used check autocorrelation residuals.","code":""},{"path":"/reference/dsm_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"dsm_cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm_cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for autocorrelation in residuals — dsm_cor","text":"plot vector fun applied lags.","code":""},{"path":"/reference/dsm_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for autocorrelation in residuals — dsm_cor","text":"Within Transect.Label, segments sorted according Segment.Labels. may require time get right particular data. one multiple surveys transects revisited, example, one may want make Transect.Label unique transect-survey identifier. Neither label need included model, must just present $data field model. usually means segment data passed dsm. current iteration function plot correlations nicely, things can get function return data (assigning result object). NA values residuals correlogram calculated. usually occurs due NA values covariates (smoother fitted values ). Code like (.na(dsm.obj$data)) might helpful.","code":""},{"path":"/reference/dsm_cor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for autocorrelation in residuals — dsm_cor","text":"David L. Miller","code":""},{"path":"/reference/dsm_cor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"# \\donttest{  library(Distance) #>  #> Attaching package: 'Distance' #> The following object is masked from 'package:dsm': #>  #>     dummy_ddf #> The following object is masked from 'package:mrds': #>  #>     create.bins  library(dsm)   # load the data, see ?mexdolphins  data(mexdolphins)   # fit a model  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.418 #> No survey area information supplied, only estimating detection function.  mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) #> Warning: Some observations are outside of detection function 1 truncation!   # look at lag 1 differences up to a maximum of lag 9, using deviance  # residuals  dsm_cor(mod1, resid.type=\"deviance\", max.lag=9,          Segment.Label=\"Sample.Label\")  # }"},{"path":"/reference/dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance propagation for density surface models — dsm_varprop","title":"Variance propagation for density surface models — dsm_varprop","text":"Calculate uncertainty predictions fitted DSM, including uncertainty detection function.","code":""},{"path":"/reference/dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance propagation for density surface models — dsm_varprop","text":"","code":"dsm_varprop(   model,   newdata = NULL,   trace = FALSE,   var.type = \"Vp\",   var_type = NULL )"},{"path":"/reference/dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance propagation for density surface models — dsm_varprop","text":"model fitted dsm. newdata prediction grid. Set NULL avoid making predictions just return model objects. trace debugging, see scale parameter estimation going. var.type variance-covariance matrix used (\"Vp\" variance-covariance conditional smoothing parameter(s), \"Vc\" unconditional). See gamObject details/explanation. doubt, stick default, \"Vp\". var_type deprecated, use var.type instead.","code":""},{"path":"/reference/dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance propagation for density surface models — dsm_varprop","text":"list elements: old_model fitted model supplied function model refit refitted model object, extra term pred point estimates predictions newdata var total variance calculated newdata ses standard error prediction cell newdata newdata=NULL last three entries NA.","code":""},{"path":"/reference/dsm_varprop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance propagation for density surface models — dsm_varprop","text":"make predictions spatial model, also want know uncertainty abundance estimate. Since density surface models 2 () stage models, need incorporate uncertainty earlier stages (.e. detection function) \"final\" uncertainty estimate. function refit spatial model include Hessian offset extra term. Variance estimates using new model can used calculate variance predicted abundance estimates incorporate detection function uncertainty. Importantly requires detection function covariates, vary within segment (, example covariates like sex used). information construct prediction grid data.frame, newdata, see predict.dsm. routine useful detection function covariates used DSM. Note can use var.type=\"Vc\" (see gamObject), variance-covariance matrix spatial model, corrected smoothing parameter uncertainty. See Wood, Pya & S\\\"afken (2016) information. Models fixed scale parameters (e.g., negative binomial) require extra round optimisation.","code":""},{"path":"/reference/dsm_varprop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Variance propagation for density surface models — dsm_varprop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_varprop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance propagation for density surface models — dsm_varprop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535. Wood, S.N., Pya, N. S\\\"afken, B. (2016) Smoothing parameter model selection general smooth models. Journal American Statistical Association, 1-45.","code":""},{"path":"/reference/dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance propagation for density surface models — dsm_varprop","text":"David L. Miller, based code Mark V. Bravington Sharon L. Hedley.","code":""},{"path":"/reference/dsm_var_gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm_var_gam","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"one willing assume detection function spatial model independent, function produce estimates variance predictions abundance, using result squared coefficients variation add.","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"dsm_var_gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"list elements model fitted model object pred.var variance regions given pred.data. bootstrap logical, always FALSE model fitted model extra term dsm.object original model (dsm.obj )","code":""},{"path":"/reference/dsm_var_gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"David L. Miller","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"if (FALSE) {  library(Distance)  library(dsm)   # load the Gulf of Mexico dolphin data (see ?mexdolphins)  data(mexdolphins)   # fit a detection function and look at the summary  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL)  summary(hr.model)   # fit a simple smooth of x and y  mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)   # Calculate the variance  # this will give a summary over the whole area in mexdolphins$preddata  mod1.var <- dsm_var_gam(mod1, preddata, off.set=preddata$area) }"},{"path":"/reference/dsm_var_movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Estimate variance abundance area using moving block bootstrap. Two procedures implemented, one incorporating detection function uncertainty, one .","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"dsm_var_movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm_var_movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL -- file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm_var_movblk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Setting ds.uncertainty=TRUE incorporate detection function uncertainty directly bootstrap. done generating observations fitted detection function re-fitting new detection function (form), calculating new effective strip width. Rejection sampling used generate observations (except half-normal case) procedure can rather slow. Note currently supported covariates detection function. Setting ds.uncertainty=FALSE incorporate detection function uncertainty using delta method. assumes detection function spatial model INDEPENDENT. probably reasonable.","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata) summary(mod1)  # calculate the variance by 500 moving block bootstraps mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,    block.size = 3, samp.unit.name = \"Transect.Label\",    off.set = preddata$area,    bar = TRUE, bs.file = \"mexico-bs.csv\", ds.uncertainty = TRUE) }"},{"path":"/reference/dsm_var_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm_var_prop","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"ensure uncertainty detection function correctly propagated final variance estimate abundance, function uses method first detailed Williams et al (2011), explanation given Bravington et al. (2021).","code":""},{"path":"/reference/dsm_var_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"","code":"dsm_var_prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"list elements model fitted model object pred.var variance region given pred.data bootstrap logical, always FALSE pred.data .set model fitted model extra term dsm.object original model, model.check simple check subtracting coefficients two models see large difference deriv numerically calculated Hessian offset","code":""},{"path":"/reference/dsm_var_prop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"idea refit spatial model including extra random effect. random effect zero mean hence effect point estimates. variance Hessian detection function. Variance estimates incorporate detection function uncertainty. mathematical details given paper references . Many prediction grids can supplied supplying list data.frames function. Note routine simply calls dsm_varprop. require multiple prediction grids, routine probably faster. routine useful detection function covariates used DSM.","code":""},{"path":"/reference/dsm_var_prop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_var_prop.html","id":"limitations","dir":"Reference","previous_headings":"","what":"Limitations","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Note routine useful detection function used DSM. used abundance.est density.est responses used. Importantly requires detection function covariates, vary within segment (, example covariates like sex used).","code":""},{"path":"/reference/dsm_var_prop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535.","code":""},{"path":"/reference/dsm_var_prop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Mark V. Bravington, Sharon L. Hedley. Bugs added David L. Miller.","code":""},{"path":"/reference/dummy_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection function objects when detection is certain — dummy_ddf","title":"Detection function objects when detection is certain — dummy_ddf","text":"Create detection function object strip/plot surveys use density surface models.","code":""},{"path":"/reference/dummy_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection function objects when detection is certain — dummy_ddf","text":"","code":"dummy_ddf(object, size = 1, width, left = 0, transect = \"line\")"},{"path":"/reference/dummy_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection function objects when detection is certain — dummy_ddf","text":"object numeric vector object identifiers, relating object field observation data DSM. size group size observation (default groups size 1) width right truncation left left truncation (default 0, left truncation) transect \"line\" \"point\" transect","code":""},{"path":"/reference/dummy_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection function objects when detection is certain — dummy_ddf","text":"David L Miller","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data from a fitted detection function — generate.ds.uncertainty","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"using dsm.var.movblk ds.uncertainty=TRUE, procedure generates data fitted detection function (assuming correct).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"","code":"generate.ds.uncertainty(ds.object)"},{"path":"/reference/generate.ds.uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"ds.object fitted detection function object (returned call ddf.ds).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"function changes random number generator seed. avoid potential side-effects, use something like: seed <- get(\".Random.seed\",envir=.GlobalEnv) running code assign(\".Random.seed\",seed,envir=.GlobalEnv) .","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"David L. Miller","code":""},{"path":"/reference/generate.mb.sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Moving block bootstrap sampler — generate.mb.sample","title":"Moving block bootstrap sampler — generate.mb.sample","text":"usually used , called within dsm.var.movblk.","code":""},{"path":"/reference/generate.mb.sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Moving block bootstrap sampler — generate.mb.sample","text":"","code":"generate.mb.sample(   num.blocks.required,   block.size,   which.blocks,   dsm.data,   unit.info,   n.units )"},{"path":"/reference/generate.mb.sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Moving block bootstrap sampler — generate.mb.sample","text":"num.blocks.required number blocks need. block.size number segments per block. .blocks blocks sampled. dsm.data $data element result call dsm. unit.info result calling block.info.per.su. n.units number sampling units.","code":""},{"path":"/reference/generate.mb.sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Moving block bootstrap sampler — generate.mb.sample","text":"vector log-residuals","code":""},{"path":"/reference/latlong2km.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert latitude and longitude to Northings and Eastings — latlong2km","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Convert longitude latitude co-ordinates kilometres west-east south-north axes (lon0,lat0) using \"spherical law cosines\".","code":""},{"path":"/reference/latlong2km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"","code":"latlong2km(lon, lat, lon0 = sum(range(lon))/2, lat0 = sum(range(lat))/2)"},{"path":"/reference/latlong2km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"lon longitude lat latitude lon0 longitude reference point (defaults mean longitude) lat0 latitude reference point (defaults mean latitude)","code":""},{"path":"/reference/latlong2km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"list elements km.e km.n.","code":""},{"path":"/reference/latlong2km.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"WARNING: approximate procedure converting latitude/ longitude Northing/Easting. Consider using projection conversions available packages sp, sf rgdal better results.","code":""},{"path":"/reference/latlong2km.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Simon N. Wood","code":""},{"path":"/reference/make.soapgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"routine simply creates grid knots (correct format) used \"internal\" part soap film smoother","code":""},{"path":"/reference/make.soapgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"","code":"make.soapgrid(bnd, n.grid)"},{"path":"/reference/make.soapgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"bnd list elements x y give locations boundary vertices. first last elements . n.grid either one number giving number points along x y axes used create grid, vector giving number x direction, y direction.","code":""},{"path":"/reference/make.soapgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"list elements x y, containing knot locations.","code":""},{"path":"/reference/make.soapgrid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"David L Miller","code":""},{"path":"/reference/mexdolphins.html","id":null,"dir":"Reference","previous_headings":"","what":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Data combination several NOAA shipboard surveys conducted pan-tropical spotted dolphins Gulf Mexico. data consist 47 observations groups dolphins. group size recorded, well Beaufort sea state time observation. Coordinates observation bathymetry data also available covariates analysis. complete example analysis (description data) provided http://distancesampling.org/R/vignettes/mexico-analysis.html.","code":""},{"path":"/reference/mexdolphins.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Halpin, P.N., .J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, . Dimatteo, J. Cleary, C. Good, L.B. Crowder, K.D. Hyrenbach. 2009. OBIS-SEAMAP: world data center marine mammal, sea bird, sea turtle distributions. Oceanography 22(2):104-115 NOAA Southeast Fisheries Science Center. 1996. Report Cetacean Survey Oceanic Selected Continental Shelf Waters Northern Gulf Mexico aboard NOAA Ship Oregon II (Cruise 220)","code":""},{"path":"/reference/obs_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Observed versus expected diagnostics for fitted DSMs — obs_exp","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"Given covariate, calculate observed expected counts unique value covariate. can useful goodness fit check DSMs.","code":""},{"path":"/reference/obs_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"obs_exp(model, covar, cut = NULL)"},{"path":"/reference/obs_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"model fitted dsm model object covar covariate aggregate (character) cut vector cut points aggregate . supplied, unique values covar used.","code":""},{"path":"/reference/obs_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"data.frame values observed expected counts.","code":""},{"path":"/reference/obs_exp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"One strategy model checking calculate observed expected counts different aggregations variable. match well model fit good.","code":""},{"path":"/reference/obs_exp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"David L Miller, suggestion Mark Bravington.","code":""},{"path":"/reference/obs_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # example with the Gulf of Mexico dolphin data data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) }"},{"path":"/reference/plot.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a density surface model. — plot.dsm","title":"Plot a density surface model. — plot.dsm","text":"See plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a density surface model. — plot.dsm","text":"","code":"# S3 method for dsm plot(x, ...)"},{"path":"/reference/plot.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a density surface model. — plot.dsm","text":"x model fitted dsm ... arguments passed plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a density surface model. — plot.dsm","text":"plot!","code":""},{"path":[]},{"path":"/reference/plot.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a density surface model. — plot.dsm","text":"David L. Miller","code":""},{"path":"/reference/plot.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Create plots of abundance uncertainty — plot.dsm.var","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"Note prediction data set must x y columns even used model.","code":""},{"path":"/reference/plot.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"","code":"# S3 method for dsm.var plot(   x,   poly = NULL,   limits = NULL,   breaks = NULL,   legend.breaks = NULL,   xlab = \"x\",   ylab = \"y\",   observations = TRUE,   plot = TRUE,   boxplot.coef = 1.5,   x.name = \"x\",   y.name = \"y\",   gg.grad = NULL,   ... )"},{"path":"/reference/plot.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"x dsm.var object poly list data.frame columns x y, gives coordinates polygon draw. may also optionally column group, many polygons. limits limits fill colours breaks breaks colour fill legend.breaks breaks displayed xlab label x axis ylab label y axis observations observations plotted? plot actually plot map, just return ggplot2 object? boxplot.coef control trimming (summary.dsm.var), effect bootstrap file saved. x.name name variable plot x axis. y.name name variable plot y axis. gg.grad optional ggplot gradient object. ... arguments","code":""},{"path":"/reference/plot.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"plot","code":""},{"path":"/reference/plot.dsm.var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"order get plotting work dsm_var_prop dsm_var_gam, one must first format data correctly since functions designed compute general summaries. One summary calculated element list pred supplied dsm_var_prop dsm_var_gam. plot uncertainty prediction grid, pred (data.frame), say, can create correct format simply using pred.new <- split(pred,1:nrow(pred)).","code":""},{"path":[]},{"path":"/reference/plot.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/plot_pred_by_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially plot predictions per model term — plot_pred_by_term","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"Plot effect smooth model spatially. term model, plot effect space. Plots made scale, relative influence smooth can seen.","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"plot_pred_by_term(dsm.obj, data, location.cov = c(\"x\", \"y\"))"},{"path":"/reference/plot_pred_by_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"dsm.obj fitted dsm object data data use plot (often prediction grid), data also include width height columns plotting location.cov deprecated, use location.cov","code":""},{"path":"/reference/plot_pred_by_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"ggplot2 plot","code":""},{"path":"/reference/plot_pred_by_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"David L Miller (idea taken inlabru)","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data and fit a model data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y) + s(depth), hr.model, segdata, obsdata)  preddata$width <- preddata$height <- sqrt(preddata$area)  # make the plot plot_pred_by_term(mod1, preddata, c(\"x\",\"y\"))  # better plot would be # library(viridis) # plot_pred_by_term(mod1, preddata, c(\"x\",\"y\")) + scale_fill_viridis() }"},{"path":"/reference/predict.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from a fitted density surface model — predict.dsm","title":"Predict from a fitted density surface model — predict.dsm","text":"Make predictions density abundance outside (inside) covered area.","code":""},{"path":"/reference/predict.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from a fitted density surface model — predict.dsm","text":"","code":"# S3 method for dsm predict(object, newdata = NULL, off.set = NULL, type = \"response\", ...)"},{"path":"/reference/predict.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from a fitted density surface model — predict.dsm","text":"object fitted dsm object newdata spatially referenced covariates e.g. altitude, depth, distance shore, etc. Covariates data.frame must names identical variable names used fitting model .set area cells prediction grid. units segments/distances given dsm. Replaces column newdata called .set supplied. Ignored newdata supplied type scale results . default \"response\", see predict.gam explanation options (usually necessary) ... arguments passed predict.gam","code":""},{"path":"/reference/predict.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from a fitted density surface model — predict.dsm","text":"predicted values response scale default (unless type specified, case see predict.gam).","code":""},{"path":"/reference/predict.dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict from a fitted density surface model — predict.dsm","text":"newdata supplied, predictions made data built model. Note order results necessarily segdata (segment data) data.frame supplied dsm. area .set used argument supplied, otherwise look areas column named .set newdata. Either way link function (usually log) applied offsets, need log passing function.","code":""},{"path":[]},{"path":"/reference/predict.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict from a fitted density surface model — predict.dsm","text":"David L. Miller","code":""},{"path":"/reference/predict.fake_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for fake detection functions — predict.fake_ddf","title":"Prediction for fake detection functions — predict.fake_ddf","text":"Prediction function dummy detection functions. function returns many 1s rows newdata. esw=TRUE strip width returned.","code":""},{"path":"/reference/predict.fake_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for fake detection functions — predict.fake_ddf","text":"","code":"# S3 method for fake_ddf predict(   object,   newdata = NULL,   compute = FALSE,   int.range = NULL,   esw = FALSE,   ... )"},{"path":"/reference/predict.fake_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for fake detection functions — predict.fake_ddf","text":"object model object newdata many 1s return? compute unused, compatibility mrds::predict int.range unused, compatibility mrds::predict esw strip width returned? ... S3 consistency","code":""},{"path":"/reference/predict.fake_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction for fake detection functions — predict.fake_ddf","text":"David L Miller","code":""},{"path":"/reference/print.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model object — print.dsm","title":"Print a description of a density surface model object — print.dsm","text":"method just gives short description fitted model. Use summary.dsm method information.","code":""},{"path":"/reference/print.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model object — print.dsm","text":"","code":"# S3 method for dsm print(x, ...)"},{"path":"/reference/print.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model object — print.dsm","text":"x model fitted dsm ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model object — print.dsm","text":"NULL","code":""},{"path":"/reference/print.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model object — print.dsm","text":"David L. Miller","code":""},{"path":"/reference/print.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm.var","title":"Print a description of a density surface model variance object — print.dsm.var","text":"method provides short summary, use summary.dsm.var method information.","code":""},{"path":"/reference/print.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm.var","text":"","code":"# S3 method for dsm.var print(x, ...)"},{"path":"/reference/print.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm.var","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model variance object — print.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm_varprop","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"method provides short summary, see summary.dsm_varprop.","code":""},{"path":"/reference/print.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"","code":"# S3 method for dsm_varprop print(x, ...)"},{"path":"/reference/print.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":[]},{"path":"/reference/print.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm.var","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"See summary.dsm.var information.","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"","code":"# S3 method for summary.dsm.var print(x, ...)"},{"path":"/reference/print.summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm_varprop","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"See summary.dsm_varprop information.","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"","code":"# S3 method for summary.dsm_varprop print(x, ...)"},{"path":"/reference/print.summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/rqgam.check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"function deprecated, use rqgam_check.","code":""},{"path":"/reference/rqgam.check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"","code":"rqgam.check(gam.obj, ...)"},{"path":"/reference/rqgam.check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Reproduces \"Resids vs. linear pred\" plot gam.check using randomised quantile residuals, la Dunn Smyth (1996). Checks heteroskedasticity usual, looking \"funnel\"-type structures points, much easier randomised quantile residuals deviance residuals, model uses count distribution response.","code":""},{"path":"/reference/rqgam_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"rqgam_check(gam.obj, ...)"},{"path":"/reference/rqgam_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"just plots!","code":""},{"path":"/reference/rqgam_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Note function works negative binomial Tweedie response distributions. Earlier versions function produced full gam.check output, confusing one plots really useful.  Checks k computed, need done using gam.check.","code":""},{"path":"/reference/rqgam_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Based code Natalie Kelly, bugs added Dave Miller","code":""},{"path":"/reference/rqgam_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"# \\donttest{ library(Distance) library(dsm) library(tweedie)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.418 #> No survey area information supplied, only estimating detection function.  # fit a simple smooth of x and y with a Tweedie response with estimated #  p parameter mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw()) #> Warning: Some observations are outside of detection function 1 truncation! rqgam_check(mod1)  # }"},{"path":"/reference/summary.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted density surface model — summary.dsm","title":"Summarize a fitted density surface model — summary.dsm","text":"Gives brief summary fitted dsm object.","code":""},{"path":"/reference/summary.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted density surface model — summary.dsm","text":"","code":"# S3 method for dsm summary(object, ...)"},{"path":"/reference/summary.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted density surface model — summary.dsm","text":"object model fitted dsm ... arguments passed summary.gam","code":""},{"path":"/reference/summary.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted density surface model — summary.dsm","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize a fitted density surface model — summary.dsm","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm.var","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"Gives brief summary fitted dsm variance object.","code":""},{"path":"/reference/summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"","code":"# S3 method for dsm.var summary(   object,   alpha = 0.05,   boxplot.coef = 1.5,   bootstrap.subregions = NULL,   ... )"},{"path":"/reference/summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95\\ confidence intervals) boxplot.coef value coef used calculate outliers see boxplot. bootstrap.subregions list vectors logicals indices subregions variances need calculated (bootstraps (see dsm.var.prop use subregions variance propagation). ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm_varprop","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"Gives brief summary fitted dsm_varprop variance object.","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"","code":"# S3 method for dsm_varprop summary(object, alpha = 0.05, ...)"},{"path":"/reference/summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95% confidence internal) ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/trim.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimmed variance — trim.var","title":"Trimmed variance — trim.var","text":"Trim variance estimates bootstrap. defined percentage defined amount necessary bring median trimmed mean within 8% defined 'outliers'.","code":""},{"path":"/reference/trim.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimmed variance — trim.var","text":"","code":"trim.var(untrimmed.bootstraps, boxplot.coef = 1.5)"},{"path":"/reference/trim.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimmed variance — trim.var","text":"untrimmed.bootstraps (usually $study.area.total element returned dsm.var.movblk bootstrap object. boxplot.coef value coef used calculate outliers see boxplot.","code":""},{"path":"/reference/trim.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimmed variance — trim.var","text":"trimmed variance","code":""},{"path":"/reference/trim.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trimmed variance — trim.var","text":"Louise Burt","code":""},{"path":"/reference/vis.concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis.concurvity","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"function deprecated, use vis_concurvity.","code":""},{"path":"/reference/vis.concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"","code":"vis.concurvity(model, type = \"estimate\")"},{"path":"/reference/vis.concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis_concurvity","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"Plot measures much one term model explained another. values high, one consider re-running variable selection one offending variables removed check stability term selection.","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"vis_concurvity(model, type = \"estimate\")"},{"path":"/reference/vis_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"methods considered somewhat experimental time. Consult concurvity information concurvity measures calculated.","code":""},{"path":"/reference/vis_concurvity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"David L Miller","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata)  # visualise concurvity using the \"estimate\" metric vis_concurvity(mod1) }"},{"path":"/news/index.html","id":"dsm-234","dir":"Changelog","previous_headings":"","what":"dsm 2.3.4","title":"dsm 2.3.4","text":"fix bug predictions NA (e.g., predictions outside soap boundary), variances NA due propagation (#4)","code":""},{"path":"/news/index.html","id":"dsm-233","dir":"Changelog","previous_headings":"","what":"dsm 2.3.3","title":"dsm 2.3.3","text":"CRAN release: 2022-08-20 dsm.var.prop -> dsm_var_prop dsm.var.gam -> dsm_var_gam dsm.var.movblk -> dsm_var_movblk dsm.cor -> dsm_cor rqgam.check -> rqgam_check vis.concurvity -> vis_concurvity var_type -> var.type","code":""},{"path":"/news/index.html","id":"dsm-232","dir":"Changelog","previous_headings":"","what":"dsm 2.3.2","title":"dsm 2.3.2","text":"CRAN release: 2022-03-17 fixed bug offset calculation dsm_varprop se estimation offsets logged twice. Thanks Megan Ferguson spotting . documentation now rmarkdown format dsm 2.3.1 transect type (line point) now determined detection function rather using transect= argument multiple detection functions now possible, use list() fitted detection functions supplied ddf.obj= argument dsm(). dsm.var.gam, dsm.var.prop dsm_varprop compatible. Vignette coming soon. Thanks Dave Fifield Ewan Wakefield extensive testing suggestions. See ?“dsm-data” information set data. strip transects now handled using dummy_ddf() function. presence~ response type removed Response options “D”, “density”, “Dhat”, “N”, “Nhat” “n” deprecated. Now “count”, “abundance.est” “density.est” allowed. Make predict.dsm work tibbles .set column newdata (error thrown previous code’s check ). Thanks Dave Fifield reporting issue. Fix bug inconsistent predictions density.est used response. Code predict.dsm simplified documentation updated. Thanks Dave Fifield reporting issue. mrds independent observer (io) trial models allowed detection function.Variance can estimated via dsm_varprop (usual limitation covariates may vary segment level) dsm.var.gam. feature currently experimental. Thanks Doug Sigourney Megan Ferguson help suggestions. See ?“dsm-data” information set data. example multiddf mrds analysis dsm https://github.com/densitymodelling/nefsc_fin_mrds_dsm availability= argument now works differently count models. availability must now length number segments (nrow(segment.data)) multiply offset (effective area) rather number animals observed coherent detectability handled case. dsm_varprop longer requies newdata supplied. newdata isn’t present just refitted original models returned, entries NA.","code":""},{"path":"/news/index.html","id":"dsm-230","dir":"Changelog","previous_headings":"","what":"dsm 2.3.0","title":"dsm 2.3.0","text":"CRAN release: 2020-04-22 fixed dsm_varprop (gam.fixed.priors) response distributions fixed scale parameters. Negative binomial results now may incorrect!","code":""},{"path":"/news/index.html","id":"dsm-2218","dir":"Changelog","previous_headings":"","what":"dsm 2.2.18","title":"dsm 2.2.18","text":"Clarification predict.dsm return documentation. Thanks Dave Fifield suggestion. .set=NULL lead predictions 0 density used. Now just returns density. Thanks Dave Fifield catch.","code":""},{"path":"/news/index.html","id":"dsm-2217","dir":"Changelog","previous_headings":"","what":"dsm 2.2.17","title":"dsm 2.2.17","text":"CRAN release: 2019-01-19 Now Suggests package “sp” (needed parts mexdolphin example data)","code":""},{"path":"/news/index.html","id":"dsm-2216","dir":"Changelog","previous_headings":"","what":"dsm 2.2.16","title":"dsm 2.2.16","text":"CRAN release: 2018-06-27 check Sample.Labels match , none throw error. Thanks Madhura Davate bringing issue attention. gamma option removed, since now ignored gam(). using method=“GCV.Cp” gamma=1.4 still used (generally advise using REML rather GCV smoothing parameter selection) dsm now requires mgcv version 1.8-23 higher. dsm.var.prop (dsm_varprop) now throw error covariates detection function can’t anything useful new function obs_exp diagnostic function compare observed expected counts different covariate aggregations","code":""},{"path":"/news/index.html","id":"dsm-2215","dir":"Changelog","previous_headings":"","what":"dsm 2.2.15","title":"dsm 2.2.15","text":"CRAN release: 2017-07-03 mexdolphins loaded using data(), data longer need attach()ed dsm.var.prop throws error count N used response. dsm.var.prop now calls dsm_varprop, keeps scale parameter constant refitting models variance propagation. dsm_varprop provides lower-level interface variance propagation probably faster flexible. add vis.concurvity visually assess concurvity (colinearity extended smooth terms) model new model diagnostic function: plot_pred_by_term – plots effect smooth model spatially. remove “abundance” option response, use “count” dsm.var.prop dsm_varprop better diagnostics checking variance model hasn’t done something weird dsm.var.movblk discards replicates model didn’t converge fix potential issue negative binomial response used variance propagation (due scale parameterisations). dsm_varprop now refits nb() models negbin() advise users look deeply. Thanks Eric Rexstad spotting . fix bug predict.dsm() predictions “density” response including offset. Thanks Len Thomas spotting .","code":""},{"path":"/news/index.html","id":"dsm-2214","dir":"Changelog","previous_headings":"","what":"dsm 2.2.14","title":"dsm 2.2.14","text":"CRAN release: 2017-01-26 nasty bug predict.dsm fixed: logging offset twice newdata provided, leading odd checking plots. also effected results predict() newdata= specified. Thanks Megan Ferguson alerting us . add support point transects(!) rqgam.check() now produced linear predictor vs. randomised quantile residuals plot avoid confusion interpreting plots REML now default method smoothing parameter selection","code":""},{"path":"/news/index.html","id":"dsm-2213","dir":"Changelog","previous_headings":"","what":"dsm 2.2.13","title":"dsm 2.2.13","text":"CRAN release: 2016-10-03 dsm.var.gam now example dsm.var.prop now checks detection function model covariates","code":""},{"path":"/news/index.html","id":"dsm-2212","dir":"Changelog","previous_headings":"","what":"dsm 2.2.12","title":"dsm 2.2.12","text":"CRAN release: 2016-08-10 alpha argument now works analytical variance reported using dsm.var.prop dsm.var.gam print.summary summary methods. Thanks Jason Roberts Eric Rexstad spotting issue. dsm now uses numDeriv::grad calculate derivatives variance estimation – much faster. Thanks Jason Roberts fix. dsm now requires mrds version 2.1.15 higher, due internal changes fixed issue Effort column required even segment.area specified. Thanks Greg Lollback spotting issue. ensured ordering segments matched ordering observations aggregation observations correct. Thanks Dave Fifield reporting bug suggesting fix. check.cols longer checks see column called “distance” strip transects used","code":""},{"path":"/news/index.html","id":"dsm-2211","dir":"Changelog","previous_headings":"","what":"dsm 2.2.11","title":"dsm 2.2.11","text":"dsm.var.prop uses update() rather messing around eval() print.summary.dsm.var didn’t work GAM variance detection function specified, now fixed (thanks Natalia Dellabianca spotting ) Removed non-working plot() code variance examples – see Mexico pantropical spotted dolphins proper treatment plotting using projections (http://distancesampling.org/R/vignettes/mexico-analysis.html) plot.var.gam longer crashes default distance data doesn’t columns “x” “y”","code":""},{"path":"/news/index.html","id":"dsm-2210","dir":"Changelog","previous_headings":"","what":"dsm 2.2.10","title":"dsm 2.2.10","text":"corrected offsets examples dsm, dsm.var.prop, dsm.var.movblk updated mexdolphins data lat/long reversal prediction data non-list storage stopped use rqgam.check used non-negbin/Tweedie responses (thanks Phil Bouchet highlighting issue).","code":""},{"path":"/news/index.html","id":"dsm-229","dir":"Changelog","previous_headings":"","what":"dsm 2.2.9","title":"dsm 2.2.9","text":"CRAN release: 2015-07-29 Moving block bootstrap now works detection function (suggested Laura Williamson) Mexico dolphin data areas prediction grids segments re-projected. Thanks Phil Bouchet pointing error. Context: https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ","code":""},{"path":"/news/index.html","id":"dsm-228","dir":"Changelog","previous_headings":"","what":"dsm 2.2.8","title":"dsm 2.2.8","text":"Speed reduced memory usage variance calculations, thanks Filipe Dias asking . Better error observations matched detection function. Thanks Ricardo Lima highlighting . Check missing columns incorrect logic, now corrected tests. Thanks Julia Migné spotting .","code":""},{"path":"/news/index.html","id":"dsm-227","dir":"Changelog","previous_headings":"","what":"dsm 2.2.7","title":"dsm 2.2.7","text":"Plotting check plots using random quantile residuals (rqgam.check) didn’t work Tweedie tw family models, now fixed. Thanks Laura Mannocci Jason Roberts pointing . Plotting CV used matrix() coerce data data.frame – thus converting elements simplest type. Errors ensued. Thanks Adrian Schiavini finding bug!","code":""},{"path":"/news/index.html","id":"dsm-226","dir":"Changelog","previous_headings":"","what":"dsm 2.2.6","title":"dsm 2.2.6","text":"dsm.var.prop dsm.var.gam now allow supply single number offset, copy many times need. Thanks Filipe Dias suggesting .","code":""},{"path":"/news/index.html","id":"dsm-225","dir":"Changelog","previous_headings":"","what":"dsm 2.2.5","title":"dsm 2.2.5","text":"CRAN release: 2014-09-16 dsm.var.prop now throw error detection function dsm ’s trying calculate variance (thanks Adrián Schiavini finding ) variance/CV plotting threw error detection function supplied observation=TRUE. Now error sidestepped (, thanks Adrián Schiavini finding ).","code":""},{"path":"/news/index.html","id":"dsm-224","dir":"Changelog","previous_headings":"","what":"dsm 2.2.4","title":"dsm 2.2.4","text":"CRAN release: 2014-06-17 changed stop() warning() data detection function included observation data frame, automatically excluded later, bug found Eric Rexstad Anna Cucknell","code":""},{"path":"/news/index.html","id":"dsm-223","dir":"Changelog","previous_headings":"","what":"dsm 2.2.3","title":"dsm 2.2.3","text":"CRAN release: 2014-04-23 moving block boostrap using observed abundances residuals therefore gave wrong answers (Eric Rexstad found bug)","code":""},{"path":"/news/index.html","id":"dsm-222","dir":"Changelog","previous_headings":"","what":"dsm 2.2.2","title":"dsm 2.2.2","text":"version number reporting now works (Len Thomas found bug) documentation default density presence models now weighted area segment","code":""},{"path":"/news/index.html","id":"dsm-221","dir":"Changelog","previous_headings":"","what":"dsm 2.2.1","title":"dsm 2.2.1","text":"switch new testthat spec added new response variables (Dhat, density.est, count, n), suggestion Len Thomas","code":""},{"path":"/news/index.html","id":"dsm-214","dir":"Changelog","previous_headings":"","what":"dsm 2.1.4","title":"dsm 2.1.4","text":"check observed distances within width now throw error","code":""},{"path":"/news/index.html","id":"dsm-213","dir":"Changelog","previous_headings":"","what":"dsm 2.1.3","title":"dsm 2.1.3","text":"CRAN release: 2013-08-20 CRAN-compatibility fixes","code":""},{"path":"/news/index.html","id":"dsm-212","dir":"Changelog","previous_headings":"","what":"dsm 2.1.2","title":"dsm 2.1.2","text":"updates dsm.cor unified plotting variance estimation tweaks non-dsm models","code":""},{"path":"/news/index.html","id":"dsm-206","dir":"Changelog","previous_headings":"","what":"dsm 2.0.6","title":"dsm 2.0.6","text":"removed warning () comparison length make.soapgrid need pass bnd list() dsm.cor now handles gamm objects dsm.cor allows one check residual autocorrelation models presence/absence data now longer needs dummy detection function object","code":""},{"path":"/news/index.html","id":"dsm-205","dir":"Changelog","previous_headings":"","what":"dsm 2.0.5","title":"dsm 2.0.5","text":"started NEWS file fixed bug dsm.var.gam() didn’t return predictions plotting result fail silly typo docs check ’re using mgcv version >= 1.7-24 GAMM fitting warn ","code":""}]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 0a60a2d..b1c8f6b 100644
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+++ b/docs/sitemap.xml
@@ -3,6 +3,9 @@
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     <loc>/404.html</loc>
   </url>
+  <url>
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+  </url>
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diff --git a/vignettes/data_format/data/loons.csv b/vignettes/data_format/data/loons.csv
new file mode 100644
index 0000000..75f7ab5
--- /dev/null
+++ b/vignettes/data_format/data/loons.csv
@@ -0,0 +1,942 @@
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diff --git a/vignettes/data_format/data/ri_coast.prj b/vignettes/data_format/data/ri_coast.prj
new file mode 100644
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diff --git a/vignettes/data_format/dataformat.bib b/vignettes/data_format/dataformat.bib
new file mode 100644
index 0000000..d139dc3
--- /dev/null
+++ b/vignettes/data_format/dataformat.bib
@@ -0,0 +1,83 @@
+
+@article{winiarski_spatial_2014,
+	title = {A spatial conservation prioritization approach for protecting marine birds given proposed offshore wind energy development},
+	volume = {169},
+	issn = {00063207},
+	url = {http://linkinghub.elsevier.com/retrieve/pii/S0006320713003832},
+	doi = {10.1016/j.biocon.2013.11.004},
+	language = {en},
+	urldate = {2018-01-25},
+	journal = {Biological Conservation},
+	author = {Winiarski, Kristopher J. and Miller, David L. and Paton, Peter W.C. and McWilliams, Scott R.},
+	month = jan,
+	year = {2014},
+	pages = {79--88},
+	file = {Biological Conservation 2014 Winiarski.pdf:/Users/dill/Zotero/storage/J4VKXEJD/Biological Conservation 2014 Winiarski.pdf:application/pdf}
+}
+
+@article{winiarski_integrating_2014,
+	title = {Integrating aerial and ship surveys of marine birds into a combined density surface model: {A} case study of wintering {Common} {Loons}},
+	volume = {116},
+	issn = {0010-5422, 1938-5129},
+	shorttitle = {Integrating aerial and ship surveys of marine birds into a combined density surface model},
+	url = {http://www.bioone.org/doi/10.1650/CONDOR-13-085.1},
+	doi = {10.1650/CONDOR-13-085.1},
+	language = {en},
+	number = {2},
+	urldate = {2018-01-29},
+	journal = {The Condor},
+	author = {Winiarski, Kristopher J. and Burt, M. Louise and Rexstad, Eric and Miller, David L. and Trocki, Carol L. and Paton, Peter W. C. and McWilliams, Scott R.},
+	month = may,
+	year = {2014},
+	pages = {149--161},
+	file = {The Condor 2014 Winiarski.pdf:/Users/dill/Zotero/storage/38MKGLIU/The Condor 2014 Winiarski.pdf:application/pdf}
+}
+
+@article{winiarski_spatially_2013,
+	title = {Spatially explicit model of wintering common loons: conservation implications},
+	volume = {492},
+	issn = {0171-8630, 1616-1599},
+	shorttitle = {Spatially explicit model of wintering common loons},
+	url = {http://www.int-res.com/abstracts/meps/v492/p273-283/},
+	doi = {10.3354/meps10492},
+	language = {en},
+	urldate = {2018-01-30},
+	journal = {Marine Ecology Progress Series},
+	author = {Winiarski, Kj and Miller, Dl and Paton, Pwc and McWilliams, Sr},
+	month = oct,
+	year = {2013},
+	pages = {273--283},
+	file = {Mar. Ecol. Prog. Ser. 2013 Winiarski.pdf:/Users/dill/Zotero/storage/N3KGNSE6/Mar. Ecol. Prog. Ser. 2013 Winiarski.pdf:application/pdf}
+}
+
+@article{miller_spatial_2013,
+	title = {Spatial models for distance sampling data: recent developments and future directions},
+	volume = {4},
+	issn = {2041210X},
+	shorttitle = {Spatial models for distance sampling data},
+	url = {http://doi.wiley.com/10.1111/2041-210X.12105},
+	doi = {10.1111/2041-210X.12105},
+	language = {en},
+	number = {11},
+	urldate = {2018-01-25},
+	journal = {Methods in Ecology and Evolution},
+	author = {Miller, David L. and Burt, M. Louise and Rexstad, Eric A. and Thomas, Len},
+	editor = {Gimenez, Olivier},
+	month = nov,
+	year = {2013},
+	pages = {1001--1010},
+	file = {Methods in Ecology and Evolution 2013 Miller.pdf:/Users/dill/Zotero/storage/HJPNM9RM/Methods in Ecology and Evolution 2013 Miller.pdf:application/pdf}
+}
+
+
+@book{bivand_applied_2008,
+	series = {Use R!},
+	title = {Applied Spatial Data Analysis with R},
+	isbn = {978-0-387-78171-6},
+	url = {https://books.google.co.uk/books?id=4gg8yx_lcj0C},
+	publisher = {Springer New York},
+	author = {Bivand, R.S. and Pebesma, E.J. and G{\'o}mez-Rubio, V.},
+	year = {2008},
+	lccn = {2008931196}
+}
+
diff --git a/vignettes/data_format/dsm-data-formatting.Rmd b/vignettes/data_format/dsm-data-formatting.Rmd
new file mode 100644
index 0000000..ecb5048
--- /dev/null
+++ b/vignettes/data_format/dsm-data-formatting.Rmd
@@ -0,0 +1,451 @@
+---
+title: '`dsm` data formatting example'
+description: |
+   'Making sure your data is in the correct format for `dsm`.'
+author:
+   - name: David L Miller
+     url: http://distancesampling.org
+     affiliation: CREEM, University of St Andrews
+     affiliation_url: https://creem.st-andrews.ac.uk
+date: "`r format(Sys.time(), '%B %Y')`"
+output: 
+  bookdown::html_document2:
+    number_sections: false
+    toc: true
+    toc_depth: 2
+    base_format: rmarkdown::html_vignette
+pkgdown:
+  as_is: true 
+bibliography: dataformat.bib
+csl: ../apa.csl
+vignette: >
+  %\VignetteIndexEntry{`dsm` data formatting example}
+  %\VignetteEngine{knitr::rmarkdown}
+  \usepackage[utf8]{inputenc}
+---
+
+```{r echo=FALSE}
+library(knitr)
+```
+
+```{r include=FALSE}
+knitr::opts_chunk$set(eval=TRUE, echo=TRUE, message=FALSE, warnings=FALSE, progress=FALSE)
+```
+
+# Introduction
+
+The `dsm` package expects data to be in a specific format. In this example I'll show how to get data into that format and give some explanation as to *why* each of these requirements exist.
+
+I recommend getting to grips with fitting models in `dsm` before reading this tutorial so you know what the end point will look like before trying to grapple with the data. An example for [Gulf of Mexico pantropical spotted dolphins is available here](http://distancedevelopment.github.io/dsm/articles/lines_gomex/mexico-analysis.html).
+
+# Prerequisites
+
+To run the code below, you will need to have the `ggplot2` and `sf` libraries installed. You can do this with the following code:
+
+```{r eval=FALSE}
+install.packages(c("ggplot2", "sf"))
+```
+
+The Rhode Island data required downloaded below:
+
+- [loon observations](data/loons.csv)
+- [coastline dbf](data/ri_coast.dbf)
+- [coastline projection](data/ri_coast.prj)
+- [coastline shapefile](data/ri_coast.shp)
+- [coastline shx](data/ri_coast.shx)
+- [transects dbf](data/ri_transects.dbf)
+- [transects projection](data/ri_transects.prj)
+- [transects shapefile](data/ri_transects.shp)
+- [transects shx](data/ri_transects.shx)
+
+These should be placed in a folder called `data` for the code below to work.
+
+# Overview
+
+When we collect distance sampling data, we need to record (1) the distance to an observation (along with other detectability covariates like weather), (2) the transect we are currently on, and (3) how much effort we expended on that transect (its length or number of visits).
+
+If we want to fit a DSM that information needs to be expanded to include where on the transect we are too, so we can build the spatial model. For point transects, this is simple as we are always at the point, but for line transects we need to know the position of the detection too (or equivalently, how far along the transect we are).
+
+More abstractly, we need information about both detections and effort. We also need to link these two. In `dsm` we use three objects to hold this information:
+
+- the fitted detection function (argument `ddf.obj`) holds all the detections but ignores spatial information
+- the "segments" (argument `segment.data`) holds the chunks of effort (parts of the transect or point visits) and their locations. It might also include environmental covariate information.
+- a table linking the above two (argument `observation.data`), making sure each detection has a corresponding segment.
+
+![Correspondence between `dsm` tables](images/dsm_tables.png){width="100%"}
+
+The above image gives an overview of how the different `data.frames` interact, along with how that corresponds to the real life situation of collecting data on a transect.
+
+The rest of this example goes through each table in turn, explaining their construction.
+
+# Table construction
+
+## Detection data
+
+The data requirements are very similar to those for packages `mrds` and `Distance`. In both cases, each detection has a corresponding row. For an analysis using `dsm` we have additional requirements in the data (which can be auto-generated by `Distance` but we recommend against this to ensure that you know that records link up correctly).
+
+For `Distance` we need:
+
+- `distance` : the distance to the detection
+- `object` : a unique detection identifier (which will link to the observation table)
+- `size` : the number of individuals in the detection (1 if objects occur alone)
+
+`mrds` requires the following extra columns (to allow for double observer surveys):
+
+- `detected` : 1 if detected by the observer and 0 if missed (always 1 for single observer)
+- `observer` : observer number (1 or 2) (always 1 for single observer)
+
+If other covariates that affect detectability are recorded (e.g., sex or weather) then these can be included in this data for analysis in `mrds` or `Distance`.
+
+Once the model is fitted, we deal only with that fitted model object in the `dsm` analysis.
+
+
+## Segment data
+
+I will use the term "segment" here to refer to both points for point transects and small chunks of transect for the line transect case. I'll assume that you've already segmented your lines while first describing the data format, then go on to an example of how to segment line transect data.
+
+The `data.frame` required for the segments needs the following columns:
+
+- `Effort` : the effort expended on this segment (either the length of the segment for lines, or the number of visits for points)
+- `Sample.Label` : a unique identifier for the segment (if you are engaged in a complicated survey, this might take a format like "`YEAR`-`AREA`-`LINE`-`SEGMENT`", so you have labels like "`2020-North-A-15`", which can be useful to keep track of where data came from later).
+
+In addition, environmental covariates like location and relevant covariates (sea surface temperature, foliage type, altitude, bathymetry, etc) can be included in this `data.frame` if they are to be used in the spatial model.
+
+
+### Segmenting
+
+If you have data as lines and you want to chop them up into segments, this section will give some sample R code to do this. How things work is extremely dependent on the input data, but hopefully this gives a template for what you want to do at least.
+
+*Before you jump in*: If you are fluent in ArcGIS or QGIS I recommend doing this task in the software you are familiar with at first. A simple guide to doing this task in [QGIS is here](https://www.northrivergeographic.com/qgis-split-a-line-at-an-interval) and for [ArcGIS I recommend the MGET toolbox](https://mgel.env.duke.edu/mget/).
+
+As an example, we will look at an aerial survey of seabirds off the coast of Rhode Island, USA. Data from these surveys were collected by the University of Rhode Island and analysed in @winiarski_spatially_2013, @winiarski_integrating_2014 and @winiarski_spatial_2014. I have the transects saved as a shapefile that I will then "chop-up" into segments. To start with let's plot the data with the coastline:
+
+```{r, fig.dim=c(7,5)}
+library(ggplot2)
+library(sf)
+
+# coast data, just for reference
+coastline <- read_sf("data/ri_coast.shp")
+
+# our transect lines
+transects <- read_sf("data/ri_transects.shp")
+
+# now make the plot
+p <- ggplot() +
+  # geom_sf knows what to do with spatial data
+  geom_sf(data=coastline) +
+  geom_sf(data=transects, aes(colour=Transect)) +
+  # chop to be on a better scale
+  coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +
+  # make the plot look simpler
+  theme_minimal()
+
+print(p)
+```
+
+In the above code we have loaded the [`sf` package](https://r-spatial.github.io/sf/). This is the package we will use to do most of our spatial work in segmenting the data. It has most of the tools expected from a GIS, though the package is still being developed so its functionality is always increasing.
+
+Investigating the `transects` data we loaded, we can see it consists of a `data.frame` with some extra bits (the spatial information, including locations (`geometry`) and projection (`CRS`)):
+
+```{r inspect-transects}
+transects
+```
+
+We have one row per transect (for a total of 24 transects), each of which consists of a line joining the start and end points that make up the transects. If we want we can plot individual rows via `plot(transects[1,])`; this can be useful to check that each row is a single line, or if further processing is needed (this can be tricky and is not covered in this tutorial but see "More information" below).
+
+This data looks in good shape, so we can use the `st_segmentize` function to take each transect and make segments from them. To make sure everything works correctly, we need to project the data first. Here I'm using an appropriate projected coordinate system (EPSG code 6348), which is the Rhode Island State Plane.
+
+```{r}
+# project transects
+transects <- st_transform(transects, 6348)
+
+# do the segmenting
+segs <- st_segmentize(transects, dfMaxLength=units::set_units(2000, "metres"))
+
+# transform back to lat/long
+segs <- st_transform(segs, 4326)
+transects <- st_transform(transects, 4326)
+```
+
+Here we know the truncation used for the detection function was 1000m (the distances were collected in bins and this was the further bin), and since we're trying to make our segments approximately square, we set the length to be twice that (since the truncation applies to either side of the transect), so 2000m.
+
+Looking at what that did:
+
+```{r inspect-segs}
+segs
+```
+
+See now that each row has many coordinates attached to it, just looking at the first row (transect 1) and comparing the coordinates between `transects` and `segs`
+
+```{r transects-rowone}
+st_coordinates(transects[1,])
+```
+
+`transects` has just two coordinates in it (the start and end points of the line). Whereas:
+
+```{r segs-rowone}
+st_coordinates(segs[1,])
+```
+
+`segs` has 5, corresponding to our segment cut points.
+
+We now need to break up the rows of `segs` into multiple rows, one per segment. This is a bit fiddly. We use a function provided by [`dieghernan`](https://dieghernan.github.io) on [their site](https://dieghernan.github.io/201905_Cast-to-subsegments/) to do this. We first load the function (don't worry about understanding the code!):
+
+```{r cast-to-subsegs-function}
+stdh_cast_substring <- function(x, to = "MULTILINESTRING") {
+  ggg <- st_geometry(x)
+
+  if (!unique(st_geometry_type(ggg)) %in% c("POLYGON", "LINESTRING")) {
+    stop("Input should be  LINESTRING or POLYGON")
+  }
+  for (k in 1:length(st_geometry(ggg))) {
+    sub <- ggg[k]
+    geom <- lapply(
+      1:(length(st_coordinates(sub)[, 1]) - 1),
+      function(i)
+        rbind(
+          as.numeric(st_coordinates(sub)[i, 1:2]),
+          as.numeric(st_coordinates(sub)[i + 1, 1:2])
+        )
+    ) %>%
+      st_multilinestring() %>%
+      st_sfc()
+
+    if (k == 1) {
+      endgeom <- geom
+    }
+    else {
+      endgeom <- rbind(endgeom, geom)
+    }
+  }
+  endgeom <- endgeom %>% st_sfc(crs = st_crs(x))
+  if (class(x)[1] == "sf") {
+    endgeom <- st_set_geometry(x, endgeom)
+  }
+  if (to == "LINESTRING") {
+    endgeom <- endgeom %>% st_cast("LINESTRING")
+  }
+  return(endgeom)
+}
+```
+
+We can then use it to separate the segments and look at the results: 
+
+```{r run-subseg}
+segs <- stdh_cast_substring(segs, to="LINESTRING")
+segs
+```
+
+We now have `r nrow(segs)` segments between 1km and 2km long. We can check the lengths using the `st_length` function and plot a histogram of the segment lengths:
+
+```{r length-segs, fig.dim=c(7,5)}
+hist(st_length(segs), xlab="Segment lengths (m)", main="")
+```
+
+Note that in setting the `dfMaxLength` argument to `st_segmentize` we are giving a rough guide to the segment length and the algorithm inside `st_segmentize` tries to get segments to be as equal in length as possible (note the $x$ axis in the histogram).
+
+Note also that the `Transect` column in `segs` now has duplicate entries as each transect has multiple segments in it. We can now create our required columns for `dsm`.
+
+First, the `Effort` column can be generated using `st_length`:
+
+```{r effort-segs}
+segs$Effort <- st_length(segs)
+```
+
+Then we can generate the `Sample.Labels`. Here I'll use a more complicated naming scheme to show how that's done, but one could simply write `segs$Sample.Label <- 1:nrow(segs)` and that would be sufficient. Instead, I would like to have my `Sample.Label` be the "`YEAR`-`AREA`-`LINE`-`SEGMENT`" scheme I suggested above.
+
+There are fancier ways to do this, but for clarity, let's use a `for` loop:
+
+```{r samplelabels-segs}
+# create a dummy column that we can fill in as we go
+segs$Sample.Label <- NA
+
+# set the year and area once for this data
+year <- 2010
+area <- "RI"
+
+# loop over the transect IDs
+for(this_transect in unique(segs$Transect)){
+  # how many segments in this transect?
+  n_segs <- nrow(subset(segs, Transect==this_transect))
+  # generate the n_segs labels that we need
+  segs$Sample.Label[segs$Transect==this_transect] <- paste(year, area, this_transect,
+                                                           1:n_segs, sep="-")
+}
+```
+
+Now we can check that looks right:
+
+```{r check-samplelabels}
+segs
+```
+
+With the required columns taken care of, we can also calculate the centroids of our segments to get the locations of each segment to use in the spatial model. Handily we can use `st_centroid` to do this in one step and keep all our data at the same time.
+
+We get a warning about how centroids might not be calculated correctly when latitude/longitude are used. So again we need to project the data first (to Rhode Island State Plane) for this step using `st_transform`.
+
+```{r centroid-locs}
+# save the line version of segs for plotting later
+segs_lines <- segs
+
+# project segs
+segs <- st_transform(segs, 6348)
+
+# find centroids
+segs <- st_centroid(segs)
+
+# project back to lat/long
+segs <- st_transform(segs, 4326)
+
+# how does that look?
+segs
+```
+
+
+Notice that our `geometry type` is now `POINT`. We can plot these centroids on our previous map:
+
+```{r, fig.dim=c(7,5)}
+p <- ggplot() +
+  # geom_sf knows what to do with spatial data
+  geom_sf(data=coastline) +
+  geom_sf(data=transects) +
+  geom_sf(data=segs) +
+  # chop to be on a better scale
+  coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +
+  # make the plot look simpler
+  theme_minimal()
+print(p)
+```
+
+At present, the `dsm` package doesn't know how to talk to `sf` objects, so as a final step we need to simplify `segs` to be a regular `data.frame`, which `dsm` can interpret. We can extract the coordinates of the centroids using `st_coordinates` and then append them onto a `data.frame` version of the object with the geometry dropped, like so:
+
+```{r dataframeify}
+segs_df <- cbind(as.data.frame(st_drop_geometry(segs)),
+              st_coordinates(segs))
+```
+
+We can double check that looks right:
+
+```{r checkfinal}
+head(segs_df)
+```
+
+and we are done. See the next section for how to link the segments and the detections.
+
+If we want to include spatial covariate information in the segment table, we could then use (for example) [`rerrdap`](https://CRAN.R-project.org/package=rerddap) to obtain remotely-sensed data. For *in situ* data (i.e., weather conditions recorded while on effort), this can be more complicated and will require the use of `summarize`, see [this thread for more information](https://github.com/r-spatial/sf/issues/321) on how to deal with this situation.
+
+## Linking observations and segments
+
+To link together the data in the detection function and the spatial data giving the effort, we use the observation `data.frame`. This is really just a cross-reference between those two tables, so each detection lives in one segment.
+
+The observation `data.frame` must have (at least) the following columns:
+
+- `object` : unique object identifier (corresponding to those identifiers in the detection function)
+- `Sample.Label` : the identifier for the segment that the observation occurred in
+- `size` : the size of each observed group (e.g., 1 if all animals occurred individually)
+- `distance` : distance to observation
+
+The observation data should have as many rows as there are in the detection function.
+
+### Relating observations and segments in practice
+
+There are a few methods for building the observation table. Here I'll illustrate one assigning detections to segments based on their distance (i.e., detections are associated with their closest segment centroid). This is appropriate when you don't see forwards too far, so it's unlikely that detections will be miss assigned. This is true, for instance, in an aerial survey where observers look out windows located on the sides of the plane but might be inappropriate for a shipboard survey where observers on the flying bridge can see way ahead of the ship.
+
+First loading the detection data for this survey (this would be what we use to fit the detection function, post-processing):
+
+```{r detect-data}
+obs <- read.csv("data/loons.csv")
+head(obs)
+```
+
+We can see there are columns for latitude and longitude (`Lat` and `Long`). We can make this `data.frame` be an `sf` object by telling `sf` that these columns contain spatial coordinates (in `sf` lingo, "geometry"):
+
+```{r geomit}
+obs <- st_as_sf(obs, coords=c("Long", "Lat"))
+# set the coordinate system
+st_crs(obs) <- 4326
+obs
+```
+
+We can overlay this on our previous map of transects:
+
+```{r, fig.dim=c(7,5)}
+p <- ggplot() +
+  # geom_sf knows what to do with spatial data
+  geom_sf(data=coastline) +
+  geom_sf(data=transects) +
+  geom_sf(data=obs, size=0.5) +
+  # chop to be on a better scale
+  coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +
+  # make the plot look simpler
+  theme_minimal()
+print(p)
+```
+
+Going back to our `segs` object above, we can use the `st_join` function, combined with the `st_nearest_feature` function to control how the tables are linked. Again, we need to project the data to avoid issues.
+
+```{r}
+# project segs
+segs <- st_transform(segs, 6348)
+obs <- st_transform(obs, 6348)
+
+# do the join
+obs <- st_join(obs, segs, join=st_nearest_feature)
+
+
+# project back to lat/long
+segs <- st_transform(segs, 4326)
+obs <- st_transform(obs, 4326)
+
+# how does that look?
+obs
+```
+
+Now `obs` has acquired additional columns from `segs`, including the `Sample.Label` (which we want) and `Effort` (which we don't). To check this worked okay, we can plot the `obs` and `segs_lines` (the line version of the segments which we saved earlier) with colour coding for the `Sample.Label`. I randomized the colour order so it would be easier to tell if observations were misallocated.
+
+```{r plot-check, fig.dim=c(7,5)}
+# make a random colour palette to avoid similar colours being near each other
+pal <- rainbow(nrow(segs), s=.6, v=.9)[sample(1:nrow(segs),nrow(segs))]
+p <- ggplot() +
+  # geom_sf knows what to do with spatial data
+  geom_sf(data=coastline) +
+  geom_sf(data=segs_lines, aes(colour=Sample.Label), pch=21) +
+  geom_sf(data=obs, size=0.5, aes(colour=Sample.Label)) +
+  # chop to be on a better scale
+  coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +
+  scale_colour_manual(values=pal) +
+  # make the plot look simpler
+  theme_minimal() +
+  theme(legend.position = "none")
+print(p)
+```
+
+As with `segs` we can remove unnecessary columns and geometry to get the required columns only:
+
+```{r obscols}
+# get rid of "spatialness"
+obs <- st_drop_geometry(obs)
+# select the columns we need
+obs <- obs[, c("object", "Sample.Label", "size", "Bin")]
+head(obs)
+```
+
+(Note here since we have binned data, we just keep the `Bin` column and need to process this further later.)
+
+A different way to approach this would be if there were timestamps on waypoints from the GPS, which could be related to the segments. One could then look at whether an observation was made between the start and end times of a segment.
+
+# More information
+
+We have provided some information about segmenting on [this wiki page](https://osf.io/wgc4f/wiki/Splitting%20transects%20into%20segments/), as part of the US Navy-funded project [DenMod](https://synergy.st-andrews.ac.uk/denmod/).
+
+This information is summarized at `?"dsm-data"` in the `dsm` package. It may also be useful to look at the data in the example dataset `mexdolphins` which can be loaded with `data(mexdolphins)`. The vignette for an analysis of those data [is available here](http://distancedevelopment.github.io/dsm/articles/lines_gomex/mexico-analysis.html).
+
+It is hard to give very general information on how to segment lines, as to some extent it depends on how the data were originally formatted (going all the way back to the make and model of the GPS unit used). More information on dealing with spatial data in R using the `sf` package is available at [the R spatial website](https://r-spatial.github.io/sf/) (see the "Articles" drop down in the header).
+
+Another source of help is the [distance sampling mailing list](https://groups.google.com/forum/#!topic/distance-sampling/). Be sure to search the archives prior to posting as there have been several threads on segmenting previously that might be helpful.
+
+# Acknowledgements
+
+Thanks to Phil Bouchet for providing helpful comments to an early version of this document and to Iúri Correia for finding an important bug.
+
+# References
\ No newline at end of file
diff --git a/vignettes/data_format/images/dsm_tables.png b/vignettes/data_format/images/dsm_tables.png
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From b9a771a0f8b300bf03f727ecd1c68dc920386dbf Mon Sep 17 00:00:00 2001
From: erex <ericincrail@gmail.com>
Date: Wed, 13 Nov 2024 16:32:15 +0000
Subject: [PATCH 5/6] change donation footer

---
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 docs/reference/dsm.cor.html                   |   81 +-
 docs/reference/dsm.html                       |  107 +-
 docs/reference/dsm.var.gam.html               |   73 +-
 docs/reference/dsm.var.movblk.html            |   85 +-
 docs/reference/dsm.var.prop.html              |   73 +-
 docs/reference/dsm_cor.html                   |   91 +-
 docs/reference/dsm_var_gam.html               |   85 +-
 docs/reference/dsm_var_movblk.html            |   95 +-
 docs/reference/dsm_var_prop.html              |   85 +-
 docs/reference/dsm_varprop.html               |   83 +-
 docs/reference/dummy_ddf.html                 |   77 +-
 docs/reference/generate.ds.uncertainty.html   |   69 +-
 docs/reference/generate.mb.sample.html        |   83 +-
 docs/reference/index.html                     |  268 +-
 docs/reference/latlong2km.html                |   79 +-
 docs/reference/make.soapgrid.html             |   75 +-
 docs/reference/mexdolphins.html               |   63 +-
 docs/reference/obs_exp.html                   |   81 +-
 docs/reference/plot.dsm.html                  |   73 +-
 docs/reference/plot.dsm.var.html              |  103 +-
 docs/reference/plot_pred_by_term.html         |   81 +-
 docs/reference/predict.dsm.html               |   83 +-
 docs/reference/predict.fake_ddf.html          |   81 +-
 docs/reference/print.dsm.html                 |   79 +-
 docs/reference/print.dsm.var.html             |   79 +-
 docs/reference/print.dsm_varprop.html         |   73 +-
 docs/reference/print.summary.dsm.var.html     |   75 +-
 docs/reference/print.summary.dsm_varprop.html |   75 +-
 docs/reference/rqgam.check.html               |   67 +-
 docs/reference/rqgam_check.html               |   77 +-
 docs/reference/summary.dsm.html               |   73 +-
 docs/reference/summary.dsm.var.html           |   79 +-
 docs/reference/summary.dsm_varprop.html       |   79 +-
 docs/reference/trim.var.html                  |   75 +-
 docs/reference/vis.concurvity.html            |   67 +-
 docs/reference/vis_concurvity.html            |   75 +-
 docs/search.json                              |    2 +-
 docs/sitemap.xml                              |  196 +-
 80 files changed, 12018 insertions(+), 2719 deletions(-)
 create mode 100644 docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
 create mode 100644 docs/deps/clipboard.js-2.0.11/clipboard.min.js
 create mode 100644 docs/deps/font-awesome-6.4.2/css/all.css
 create mode 100644 docs/deps/font-awesome-6.4.2/css/all.min.css
 create mode 100644 docs/deps/font-awesome-6.4.2/css/v4-shims.css
 create mode 100644 docs/deps/font-awesome-6.4.2/css/v4-shims.min.css
 create mode 100644 docs/deps/font-awesome-6.4.2/webfonts/fa-brands-400.ttf
 create mode 100644 docs/deps/font-awesome-6.4.2/webfonts/fa-brands-400.woff2
 create mode 100644 docs/deps/font-awesome-6.4.2/webfonts/fa-regular-400.ttf
 create mode 100644 docs/deps/font-awesome-6.4.2/webfonts/fa-regular-400.woff2
 create mode 100644 docs/deps/font-awesome-6.4.2/webfonts/fa-solid-900.ttf
 create mode 100644 docs/deps/font-awesome-6.4.2/webfonts/fa-solid-900.woff2
 create mode 100644 docs/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.ttf
 create mode 100644 docs/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.woff2
 create mode 100644 docs/deps/headroom-0.11.0/headroom.min.js
 create mode 100644 docs/deps/headroom-0.11.0/jQuery.headroom.min.js
 create mode 100644 docs/deps/search-1.0.0/autocomplete.jquery.min.js
 create mode 100644 docs/deps/search-1.0.0/fuse.min.js
 create mode 100644 docs/deps/search-1.0.0/mark.min.js
 create mode 100644 docs/katex-auto.js
 create mode 100644 docs/lightswitch.js

diff --git a/.Rhistory b/.Rhistory
index b8207cc..0c1aa13 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,58 +1,3 @@
-# Chunk 2
-knitr::opts_chunk$set(eval=TRUE, echo=TRUE, message=FALSE, warnings=FALSE, progress=FALSE)
-# Chunk 3: loadlibraries
-library(dsm)
-library(ggplot2)
-# plotting options
-gg.opts <- theme(panel.grid.major=element_blank(),
-panel.grid.minor=element_blank(),
-panel.background=element_blank())
-# Chunk 4: download-extra-data
-load("mexdolphins-extra.rda")
-# Chunk 5: loaddata
-data(mexdolphins)
-# Chunk 6: head-segdata
-head(segdata)
-# Chunk 7: head-distdata
-head(distdata)
-# Chunk 8: head-obsdata
-head(obsdata)
-# Chunk 9: head-preddata
-head(preddata)
-# Chunk 10: projectsurvey
-library(sf)
-library(plyr)
-# tell R that the survey.area object is currently in lat/long
-sp::proj4string(survey.area) <- sp::CRS("+proj=longlat +datum=WGS84")
-predsf <- st_as_sf(pred.polys)
-area.sf <- st_as_sf(survey.area)
-st_crs(area.sf) <- "WGS84"
-area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))
-# Convert preddata to a spatial object
-preddata_sf <- st_as_sf(preddata, coords=c("x", "y"))
-st_crs(preddata_sf) <- st_crs(area.sf.proj)
-# Perform the intersection
-preddata_sf <- st_intersection(preddata_sf, area.sf.proj)
-coords_preddata <- data.frame(st_coordinates(preddata_sf))
-preddata_sf$x <- coords_preddata$X
-preddata_sf$y <- coords_preddata$Y
-# Chunk 11: studyareasoap
-# proj 4 string
-# using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/
-lcc_proj4 <- sp::CRS("+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
-# project using LCC
-survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)
-# simplify the object
-survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords)
-names(survey.area) <- c("x", "y")
-# Chunk 12: areawithtransects
-segdata_sf <- st_as_sf(segdata, coords = c("x","y"))
-st_crs(segdata_sf) <- st_crs(area.sf.proj)
-# study area outline and segment centres
-plot_segments <- ggplot() +
-geom_sf(data = area.sf.proj, fill="lightblue", color = "blue", linewidth=.1) +
-geom_sf(data=segdata_sf, fill=NA, color="black", linewidth=.3) +
-labs(title="1996 SE Fisheries Science Center Gulf of Mexico cruise",
 subtitle = "Points are segment centres") +
 scale_fill_viridis_c(option = "magma", guide = "none")
 plot_segments
@@ -510,3 +455,58 @@ pkgdown::build_site()
 pkgdown::build_site()
 pkgdown::build_site()
 pkgdown::build_site()
+pkgdown::build_site()
+library(dsm)
+library(ggplot2)
+# plotting options
+gg.opts <- theme(panel.grid.major=element_blank(),
+panel.grid.minor=element_blank(),
+panel.background=element_blank())
+load("mexdolphins-extra.rda")
+data(mexdolphins)
+data(mexdolphins)
+setwd("~/GitHub/dsm/vignettes/lines_gomex")
+load("mexdolphins-extra.rda")
+library(sf)
+library(plyr)
+# tell R that the survey.area object is currently in lat/long
+sp::proj4string(survey.area) <- sp::CRS("+proj=longlat +datum=WGS84")
+predsf <- st_as_sf(pred.polys)
+area.sf <- st_as_sf(survey.area)
+st_crs(area.sf) <- "WGS84"
+area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))
+# Convert preddata to a spatial object
+preddata_sf <- st_as_sf(preddata, coords=c("x", "y"))
+st_crs(preddata_sf) <- st_crs(area.sf.proj)
+# Perform the intersection
+preddata_sf <- st_intersection(preddata_sf, area.sf.proj)
+coords_preddata <- data.frame(st_coordinates(preddata_sf))
+preddata_sf$x <- coords_preddata$X
+preddata_sf$y <- coords_preddata$Y
+# proj 4 string
+# using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/
+lcc_proj4 <- sp::CRS("+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
+# project using LCC
+survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)
+# simplify the object
+survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords)
+names(survey.area) <- c("x", "y")
+prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
+prediction_grid_sf <- st_sf(geometry = prediction_grid)
+cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
+cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
+depth <- ggplot() +
+geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
+labs(title = "Spotted dolphins, Gulf of Mexico",
+subtitle = "Depth in meters, size of detected dolphin groups") +
+xlab("Longitude") + ylab("Latitude") +
+geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
+scale_fill_viridis_c(option = "viridis", direction = 1)
+depth
+library(Distance)
+detfc.hr.null <- ds(distdata, max(distdata$distance), key="hr", adjustment=NULL)
+plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2)
+gof_ds(detfc.hr.null)
+dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method="REML")
+summary(dsm.xy)
+unique(detfc.hr.null$ddf$fitted)
diff --git a/_pkgdown.yml b/_pkgdown.yml
index c7e5850..606efaa 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -23,7 +23,7 @@ footer:
     left: clarity
 
   components:
-    donate: "If you wish to donate to development and maintenance"    
+    donate: "If you wish to donate to development and maintenance, please <a href='mailto:distance@st-andrews.ac.uk'>email us</a>."    
     clarity:  "We improve our site and software support by using Microsoft Clarity to see <br>
       how you use our website. By using our site, you agree that we and Microsoft <br>
       can collect and use this data. Clarity is GDPR compliant."
diff --git a/docs/404.html b/docs/404.html
index b304dbf..79b224a 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -8,72 +8,51 @@
 <title>Page not found (404) • dsm</title>
 <script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
+<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
       <ul class="navbar-nav me-auto">
-<li class="nav-item">
-  <a class="nav-link" href="reference/index.html">Reference</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="news/index.html">Changelog</a>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul>
 </li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-title-body">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Page not found (404)</h1>
-      
+
+      <h1>Page not found (404)</h1>
+
     </div>
 
 Content not found. Please use links in the navbar.
@@ -87,15 +66,15 @@
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 5034a3b..6b7eae5 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -1,60 +1,39 @@
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-      <img src="" class="logo" alt=""><h1>`dsm` data formatting example</h1>
+
+      <h1>`dsm` data formatting example</h1>
                         <h4 data-toc-skip class="author">David L Miller</h4>
             <address class="author_afil">
-      CREEM, University of St Andrews<br><h4 data-toc-skip class="date">May 2024</h4>
+      CREEM, University of St Andrews<br><h4 data-toc-skip class="date">November 2024</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/vignettes/data_format/dsm-data-formatting.Rmd" class="external-link"><code>vignettes/data_format/dsm-data-formatting.Rmd</code></a></small>
       <div class="d-none name"><code>dsm-data-formatting.Rmd</code></div>
@@ -128,7 +107,7 @@ <h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"
 <li>a table linking the above two (argument <code>observation.data</code>), making sure each detection has a corresponding segment.</li>
 </ul>
 <div class="float">
-<img src="images/dsm_tables.png" style="width:100.0%" alt="Correspondence between dsm tables"><div class="figcaption">Correspondence between <code>dsm</code> tables</div>
+<img src="images%2Fdsm_tables.png" style="width:100.0%" alt="Correspondence between dsm tables"><div class="figcaption">Correspondence between <code>dsm</code> tables</div>
 </div>
 <p>The above image gives an overview of how the different <code>data.frames</code> interact, along with how that corresponds to the real life situation of collecting data on a transect.</p>
 <p>The rest of this example goes through each table in turn, explaining their construction.</p>
@@ -179,9 +158,10 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <p>As an example, we will look at an aerial survey of seabirds off the coast of Rhode Island, USA. Data from these surveys were collected by the University of Rhode Island and analysed in <span class="citation">K. Winiarski, Miller, Paton, &amp; McWilliams (2013)</span>, <span class="citation">K. J. Winiarski, Burt, et al. (2014)</span> and <span class="citation">K. J. Winiarski, Miller, Paton, &amp; McWilliams (2014)</span>. I have the transects saved as a shapefile that I will then “chop-up” into segments. To start with let’s plot the data with the coastline:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># coast data, just for reference</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning: package 'sf' was built under R version 4.4.2</span></span></code></pre>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># coast data, just for reference</span></span>
 <span><span class="va">coastline</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_read.html" class="external-link">read_sf</a></span><span class="op">(</span><span class="st">"data/ri_coast.shp"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># our transect lines</span></span>
@@ -201,7 +181,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-4-1.png"><!-- --></p>
 <p>In the above code we have loaded the <a href="https://r-spatial.github.io/sf/" class="external-link"><code>sf</code> package</a>. This is the package we will use to do most of our spatial work in segmenting the data. It has most of the tools expected from a GIS, though the package is still being developed so its functionality is always increasing.</p>
 <p>Investigating the <code>transects</code> data we loaded, we can see it consists of a <code>data.frame</code> with some extra bits (the spatial information, including locations (<code>geometry</code>) and projection (<code>CRS</code>)):</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">transects</span></span></code></pre></div>
 <pre><code><span><span class="co">## Simple feature collection with 26 features and 1 field</span></span>
 <span><span class="co">## Geometry type: LINESTRING</span></span>
@@ -224,7 +204,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #949494;"># ℹ 16 more rows</span></span></span></code></pre>
 <p>We have one row per transect (for a total of 24 transects), each of which consists of a line joining the start and end points that make up the transects. If we want we can plot individual rows via <code>plot(transects[1,])</code>; this can be useful to check that each row is a single line, or if further processing is needed (this can be tricky and is not covered in this tutorial but see “More information” below).</p>
 <p>This data looks in good shape, so we can use the <code>st_segmentize</code> function to take each transect and make segments from them. To make sure everything works correctly, we need to project the data first. Here I’m using an appropriate projected coordinate system (EPSG code 6348), which is the Rhode Island State Plane.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># project transects</span></span>
 <span><span class="va">transects</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">transects</span>, <span class="fl">6348</span><span class="op">)</span></span>
 <span></span>
@@ -236,7 +216,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="va">transects</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">transects</span>, <span class="fl">4326</span><span class="op">)</span></span></code></pre></div>
 <p>Here we know the truncation used for the detection function was 1000m (the distances were collected in bins and this was the further bin), and since we’re trying to make our segments approximately square, we set the length to be twice that (since the truncation applies to either side of the transect), so 2000m.</p>
 <p>Looking at what that did:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
 <pre><code><span><span class="co">## Simple feature collection with 26 features and 1 field</span></span>
 <span><span class="co">## Geometry type: LINESTRING</span></span>
@@ -258,13 +238,13 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #BCBCBC;">10</span>       10 (-71.421 41.38723, -71.41952 41.36946, -71.41804 41.3517, -71.41656…</span></span>
 <span><span class="co">## <span style="color: #949494;"># ℹ 16 more rows</span></span></span></code></pre>
 <p>See now that each row has many coordinates attached to it, just looking at the first row (transect 1) and comparing the coordinates between <code>transects</code> and <code>segs</code></p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">transects</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##              X        Y L1</span></span>
 <span><span class="co">## [1,] -71.86948 41.29969  1</span></span>
 <span><span class="co">## [2,] -71.86581 41.25891  1</span></span></code></pre>
 <p><code>transects</code> has just two coordinates in it (the start and end points of the line). Whereas:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">segs</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##              X        Y L1</span></span>
 <span><span class="co">## [1,] -71.86948 41.29969  1</span></span>
@@ -273,7 +253,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## [4,] -71.86581 41.25891  1</span></span></code></pre>
 <p><code>segs</code> has 5, corresponding to our segment cut points.</p>
 <p>We now need to break up the rows of <code>segs</code> into multiple rows, one per segment. This is a bit fiddly. We use a function provided by <a href="https://dieghernan.github.io" class="external-link"><code>dieghernan</code></a> on <a href="https://dieghernan.github.io/201905_Cast-to-subsegments/" class="external-link">their site</a> to do this. We first load the function (don’t worry about understanding the code!):</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">stdh_cast_substring</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">to</span> <span class="op">=</span> <span class="st">"MULTILINESTRING"</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  <span class="va">ggg</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_geometry</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
 <span></span>
@@ -310,11 +290,11 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">endgeom</span><span class="op">)</span></span>
 <span><span class="op">}</span></span></code></pre></div>
 <p>We can then use it to separate the segments and look at the results:</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu">stdh_cast_substring</span><span class="op">(</span><span class="va">segs</span>, to<span class="op">=</span><span class="st">"LINESTRING"</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in st_cast.sf(., "LINESTRING"): repeating attributes for all</span></span>
 <span><span class="co">## sub-geometries for which they may not be constant</span></span></code></pre>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
 <pre><code><span><span class="co">## Simple feature collection with 590 features and 1 field</span></span>
 <span><span class="co">## Geometry type: LINESTRING</span></span>
@@ -336,17 +316,17 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #BCBCBC;">10</span>        3 (-71.78654 41.29813, -71.78505 41.28065)</span></span>
 <span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
 <p>We now have 590 segments between 1km and 2km long. We can check the lengths using the <code>st_length</code> function and plot a histogram of the segment lengths:</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_measures.html" class="external-link">st_length</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>, xlab<span class="op">=</span><span class="st">"Segment lengths (m)"</span>, main<span class="op">=</span><span class="st">""</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dsm-data-formatting_files/figure-html/length-segs-1.png"><!-- --></p>
 <p>Note that in setting the <code>dfMaxLength</code> argument to <code>st_segmentize</code> we are giving a rough guide to the segment length and the algorithm inside <code>st_segmentize</code> tries to get segments to be as equal in length as possible (note the <span class="math inline">\(x\)</span> axis in the histogram).</p>
 <p>Note also that the <code>Transect</code> column in <code>segs</code> now has duplicate entries as each transect has multiple segments in it. We can now create our required columns for <code>dsm</code>.</p>
 <p>First, the <code>Effort</code> column can be generated using <code>st_length</code>:</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span><span class="op">$</span><span class="va">Effort</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_measures.html" class="external-link">st_length</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span></span></code></pre></div>
 <p>Then we can generate the <code>Sample.Labels</code>. Here I’ll use a more complicated naming scheme to show how that’s done, but one could simply write <code>segs$Sample.Label &lt;- 1:nrow(segs)</code> and that would be sufficient. Instead, I would like to have my <code>Sample.Label</code> be the “<code>YEAR</code>-<code>AREA</code>-<code>LINE</code>-<code>SEGMENT</code>” scheme I suggested above.</p>
 <p>There are fancier ways to do this, but for clarity, let’s use a <code>for</code> loop:</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># create a dummy column that we can fill in as we go</span></span>
 <span><span class="va">segs</span><span class="op">$</span><span class="va">Sample.Label</span> <span class="op">&lt;-</span> <span class="cn">NA</span></span>
 <span></span>
@@ -363,7 +343,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span>                                                           <span class="fl">1</span><span class="op">:</span><span class="va">n_segs</span>, sep<span class="op">=</span><span class="st">"-"</span><span class="op">)</span></span>
 <span><span class="op">}</span></span></code></pre></div>
 <p>Now we can check that looks right:</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
 <pre><code><span><span class="co">## Simple feature collection with 590 features and 3 fields</span></span>
 <span><span class="co">## Geometry type: LINESTRING</span></span>
@@ -386,7 +366,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
 <p>With the required columns taken care of, we can also calculate the centroids of our segments to get the locations of each segment to use in the spatial model. Handily we can use <code>st_centroid</code> to do this in one step and keep all our data at the same time.</p>
 <p>We get a warning about how centroids might not be calculated correctly when latitude/longitude are used. So again we need to project the data first (to Rhode Island State Plane) for this step using <code>st_transform</code>.</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># save the line version of segs for plotting later</span></span>
 <span><span class="va">segs_lines</span> <span class="op">&lt;-</span> <span class="va">segs</span></span>
 <span></span>
@@ -396,7 +376,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co"># find centroids</span></span>
 <span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_unary.html" class="external-link">st_centroid</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: st_centroid assumes attributes are constant over geometries</span></span></code></pre>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># project back to lat/long</span></span>
 <span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">4326</span><span class="op">)</span></span>
 <span></span>
@@ -422,7 +402,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #BCBCBC;">10</span>        3  (-71.7858 41.28939)  <span style="text-decoration: underline;">1</span>948. 2010-RI-3-2 </span></span>
 <span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
 <p>Notice that our <code>geometry type</code> is now <code>POINT</code>. We can plot these centroids on our previous map:</p>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="co"># geom_sf knows what to do with spatial data</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">coastline</span><span class="op">)</span> <span class="op">+</span></span>
@@ -435,11 +415,11 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-6-1.png"><!-- --></p>
 <p>At present, the <code>dsm</code> package doesn’t know how to talk to <code>sf</code> objects, so as a final step we need to simplify <code>segs</code> to be a regular <code>data.frame</code>, which <code>dsm</code> can interpret. We can extract the coordinates of the centroids using <code>st_coordinates</code> and then append them onto a <code>data.frame</code> version of the object with the geometry dropped, like so:</p>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_drop_geometry</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span>,</span>
 <span>              <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>We can double check that looks right:</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">segs_df</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##   Transect       Effort Sample.Label         X        Y</span></span>
 <span><span class="co">## 1        1 1515.147 [m]  2010-RI-1-1 -71.86887 41.29289</span></span>
@@ -473,7 +453,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 </h4>
 <p>There are a few methods for building the observation table. Here I’ll illustrate one assigning detections to segments based on their distance (i.e., detections are associated with their closest segment centroid). This is appropriate when you don’t see forwards too far, so it’s unlikely that detections will be miss assigned. This is true, for instance, in an aerial survey where observers look out windows located on the sides of the plane but might be inappropriate for a shipboard survey where observers on the flying bridge can see way ahead of the ship.</p>
 <p>First loading the detection data for this survey (this would be what we use to fit the detection function, post-processing):</p>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html" class="external-link">read.csv</a></span><span class="op">(</span><span class="st">"data/loons.csv"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##         Date     Time Bin      Lat      Long Observer object size</span></span>
@@ -484,7 +464,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="co">## 5 2011-02-23 18991230   A 40.94153 -71.38517       KW    446    1</span></span>
 <span><span class="co">## 6 2011-02-07 18991230   A 40.94225 -71.46438       KW    457    1</span></span></code></pre>
 <p>We can see there are columns for latitude and longitude (<code>Lat</code> and <code>Long</code>). We can make this <code>data.frame</code> be an <code>sf</code> object by telling <code>sf</code> that these columns contain spatial coordinates (in <code>sf</code> lingo, “geometry”):</p>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">obs</span>, coords<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Long"</span>, <span class="st">"Lat"</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co"># set the coordinate system</span></span>
 <span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fl">4326</span></span>
@@ -507,7 +487,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="co">## 9  2011-02-23 18991230   B       JV    814    1 POINT (-71.51172 40.97912)</span></span>
 <span><span class="co">## 10 2011-04-19 18991230   A       KW    911    1 POINT (-71.59762 40.98571)</span></span></code></pre>
 <p>We can overlay this on our previous map of transects:</p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="co"># geom_sf knows what to do with spatial data</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">coastline</span><span class="op">)</span> <span class="op">+</span></span>
@@ -520,7 +500,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-7-1.png"><!-- --></p>
 <p>Going back to our <code>segs</code> object above, we can use the <code>st_join</code> function, combined with the <code>st_nearest_feature</code> function to control how the tables are linked. Again, we need to project the data to avoid issues.</p>
-<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># project segs</span></span>
 <span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">6348</span><span class="op">)</span></span>
 <span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">obs</span>, <span class="fl">6348</span><span class="op">)</span></span>
@@ -564,7 +544,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="co">## 9   2010-RI-7-22 POINT (-71.51172 40.97912)</span></span>
 <span><span class="co">## 10  2010-RI-24-9 POINT (-71.59762 40.98571)</span></span></code></pre>
 <p>Now <code>obs</code> has acquired additional columns from <code>segs</code>, including the <code>Sample.Label</code> (which we want) and <code>Effort</code> (which we don’t). To check this worked okay, we can plot the <code>obs</code> and <code>segs_lines</code> (the line version of the segments which we saved earlier) with colour coding for the <code>Sample.Label</code>. I randomized the colour order so it would be easier to tell if observations were misallocated.</p>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># make a random colour palette to avoid similar colours being near each other</span></span>
 <span><span class="va">pal</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/grDevices/palettes.html" class="external-link">rainbow</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>, s<span class="op">=</span><span class="fl">.6</span>, v<span class="op">=</span><span class="fl">.9</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>,<span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span><span class="op">]</span></span>
 <span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
@@ -581,7 +561,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dsm-data-formatting_files/figure-html/plot-check-1.png"><!-- --></p>
 <p>As with <code>segs</code> we can remove unnecessary columns and geometry to get the required columns only:</p>
-<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># get rid of "spatialness"</span></span>
 <span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_drop_geometry</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span></span>
 <span><span class="co"># select the columns we need</span></span>
@@ -627,7 +607,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
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-      <img src="" class="logo" alt=""><h1>Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</h1>
+
+      <h1>Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</h1>
                         <h4 data-toc-skip class="author">David L Miller</h4>
             <address class="author_afil">
-      CREEM, Univ of St Andrews<br><h4 data-toc-skip class="date">May 2024</h4>
+      CREEM, Univ of St Andrews<br><h4 data-toc-skip class="date">November 2024</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/vignettes/lines_gomex/mexico-analysis.Rmd" class="external-link"><code>vignettes/lines_gomex/mexico-analysis.Rmd</code></a></small>
       <div class="d-none name"><code>mexico-analysis.Rmd</code></div>
@@ -183,8 +162,10 @@ <h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class
 <p>Using latitude and longitude when performing spatial smoothing can be problematic when certain smoother bases are used. In particular when bivariate isotropic bases are used the non-isotropic nature of latitude and longitude is inconsistent (moving one degree in one direction is not the same as moving one degree in the other).</p>
 <p>We give an example of projecting the polygon that defines the survey area (which as simply been read into R using <code>readShapeSpatial</code> from a shapefile produced by GIS).</p>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://had.co.nz/plyr" class="external-link">plyr</a></span><span class="op">)</span></span>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning: package 'sf' was built under R version 4.4.2</span></span></code></pre>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://had.co.nz/plyr" class="external-link">plyr</a></span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># tell R that the survey.area object is currently in lat/long</span></span>
 <span><span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/is.projected.html" class="external-link">proj4string</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/CRS-class.html" class="external-link">CRS</a></span><span class="op">(</span><span class="st">"+proj=longlat +datum=WGS84"</span><span class="op">)</span></span>
@@ -202,13 +183,13 @@ <h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class
 <span><span class="va">preddata_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">preddata_sf</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">coords_preddata</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">preddata_sf</span><span class="op">)</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">coords_preddata</span><span class="op">$</span><span class="va">X</span></span>
 <span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">y</span> <span class="op">&lt;-</span> <span class="va">coords_preddata</span><span class="op">$</span><span class="va">Y</span> </span></code></pre></div>
 <p>Next bit of code works with the <code>study.area</code> outline, massaging it for use with soap film smoother. If you will not be using soap film smoothers, the code is extraneous.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># proj 4 string</span></span>
 <span><span class="co"># using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/</span></span>
 <span><span class="va">lcc_proj4</span> <span class="op">&lt;-</span> <span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/CRS-class.html" class="external-link">CRS</a></span><span class="op">(</span><span class="st">"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs "</span><span class="op">)</span></span>
@@ -220,7 +201,7 @@ <h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class
 <span><span class="va">survey.area</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">@</span><span class="va">polygons</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">@</span><span class="va">Polygons</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">@</span><span class="va">coords</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>, <span class="st">"y"</span><span class="op">)</span></span></code></pre></div>
 <p>The below code generates this plot, which shows the survey area with the transect lines overlaid (using data from <code>segdata</code>).</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segdata_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">segdata</span>, coords <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">segdata_sf</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">area.sf.proj</span><span class="op">)</span></span>
 <span><span class="co"># study area outline and segment centres</span></span>
@@ -233,7 +214,7 @@ <h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class
 <span><span class="va">plot_segments</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/areawithtransects-1.png"><!-- --></p>
 <p>Also note that since we have projected our prediction grid, the “squares” do not look quite like squares. So for plotting we will use the polygons that we have saved, these polygons (stored in <code>pred.polys</code>) are read from a shapefile created in GIS, the object itself is of class <code>SpatialPolygons</code> from the <code>sp</code> package. It needs project for proper plotting using functions in the <code>sf</code> package. predsf &lt;- st_as_sf(pred.polys)</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">predsf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">pred.polys</span><span class="op">)</span></span>
 <span><span class="co"># plot as projected</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_geometry</a></span><span class="op">(</span><span class="va">predsf</span><span class="op">)</span>, axes<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
@@ -249,7 +230,7 @@ <h3 id="distance-data">Distance data<a class="anchor" aria-label="anchor" href="
 </h3>
 <p>The top panels of the EDA plots below show histograms of observed distances and cluster size, while the bottom panels show the relationship between observed distance and observed cluster size, and the relationship between observed distance and Beaufort sea state. The plots show that there is some relationship between cluster size and observed distance (fewer smaller clusters seem to be seen at larger distances).</p>
 <p>The following code generates the EDA plots:</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># histograms</span></span>
@@ -274,14 +255,14 @@ <h3 id="distance-data">Distance data<a class="anchor" aria-label="anchor" href="
 <h3 id="spatial-data">Spatial data<a class="anchor" aria-label="anchor" href="#spatial-data"></a>
 </h3>
 <p>Looking separately at the spatial data without thinking about the distances, we can plot the observed group sizes in space (point size is proportional to the group size for each observation). Circle size indicates the size of the group in the observation. There are rather large areas with no observations, which might cause our variance estimates for abundance to be rather large. We also see the depth data which we will use depth later as an explanatory covariate in our spatial model.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">prediction_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_make_grid.html" class="external-link">st_make_grid</a></span><span class="op">(</span><span class="va">area.sf.proj</span>, cellsize <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">9000</span>,<span class="fl">9000</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">prediction_grid_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/sf.html" class="external-link">st_sf</a></span><span class="op">(</span>geometry <span class="op">=</span> <span class="va">prediction_grid</span><span class="op">)</span></span>
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">prediction_grid_sf</span>, <span class="va">preddata_sf</span>, join <span class="op">=</span> <span class="va">st_nearest_feature</span><span class="op">)</span></span>
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">depth</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill<span class="op">=</span><span class="va">depth</span><span class="op">)</span>, color<span class="op">=</span><span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
 <span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Spotted dolphins, Gulf of Mexico"</span>,</span>
@@ -298,16 +279,22 @@ <h2 id="estimating-the-detection-function">Estimating the detection function<a c
 </h2>
 <p>We use the <code>ds</code> function in the package <code>Distance</code> to fit the detection function. (The <code>Distance</code> package is intended to make standard distance sampling in <code>R</code> relatively straightforward. For a more flexible but more complex alternative, see the function <code>ddf</code> in the <code>mrds</code> library.)</p>
 <p>First, loading the <code>Distance</code> library:</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></code></pre></div>
 <p>We can then fit a detection function with hazard-rate key with no adjustment terms:</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">detfc.hr.null</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span><span class="op">)</span>, key<span class="op">=</span><span class="st">"hr"</span>, adjustment<span class="op">=</span><span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, :</span></span>
 <span><span class="co">## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible</span></span>
+<span><span class="co">## problem in data (e.g., spike near zero distance).</span></span>
+<span><span class="co">## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, :</span></span>
+<span><span class="co">## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible</span></span>
+<span><span class="co">## problem in data (e.g., spike near zero distance).</span></span></code></pre>
+<pre><code><span><span class="co">## Warning in ds(distdata, max(distdata$distance), key = "hr", adjustment = NULL):</span></span>
+<span><span class="co">## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible</span></span>
 <span><span class="co">## problem in data (e.g., spike near zero distance).</span></span></code></pre>
 <p>Calling <code>summary</code> gives us information about parameter estimates, probability of detection, AIC, etc:</p>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">detfc.hr.null</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Summary for distance analysis </span></span>
@@ -315,41 +302,41 @@ <h2 id="estimating-the-detection-function">Estimating the detection function<a c
 <span><span class="co">## Distance range         :  0  -  7847.467 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model       : Hazard-rate key function </span></span>
-<span><span class="co">## AIC         :  841.1841 </span></span>
+<span><span class="co">## AIC         :  841.2528 </span></span>
 <span><span class="co">## Optimisation:  mrds (nlminb) </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Detection function parameters</span></span>
 <span><span class="co">## Scale coefficient(s):  </span></span>
 <span><span class="co">##             estimate        se</span></span>
-<span><span class="co">## (Intercept) 7.986391 0.5239151</span></span>
+<span><span class="co">## (Intercept)  7.98244 0.9533779</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Shape coefficient(s):  </span></span>
-<span><span class="co">##                estimate         se</span></span>
-<span><span class="co">## (Intercept) 0.000237466 0.02958901</span></span>
+<span><span class="co">##             estimate        se</span></span>
+<span><span class="co">## (Intercept)        0 0.7833406</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">##                       Estimate         SE        CV</span></span>
-<span><span class="co">## Average p            0.5918795  0.1667006 0.2816462</span></span>
-<span><span class="co">## N in covered region 79.4080596 23.5573051 0.2966614</span></span></code></pre>
+<span><span class="co">## Average p            0.5912252  0.2224714 0.3762887</span></span>
+<span><span class="co">## N in covered region 79.4959326 30.8184416 0.3876732</span></span></code></pre>
 <p>The following code generates a plot of the fitted detection function (left) and quantile-quantile plot (right):</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">detfc.hr.null</span>, showpoints<span class="op">=</span><span class="cn">FALSE</span>, pl.den<span class="op">=</span><span class="fl">0</span>, lwd<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/Distance/man/gof_ds.html" class="external-link">gof_ds</a></span><span class="op">(</span><span class="va">detfc.hr.null</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Goodness of fit results for ddf object</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Distance sampling Cramer-von Mises test (unweighted)</span></span>
-<span><span class="co">## Test statistic = 0.0976846 p-value = 0.596345</span></span></code></pre>
+<span><span class="co">## Test statistic = 0.0972004 p-value = 0.598774</span></span></code></pre>
 <p><img src="mexico-analysis_files/figure-html/hr-detfct-1.png"><img src="mexico-analysis_files/figure-html/hr-detfct-2.png"></p>
 <p>The quantile-quantile plot show relatively good goodness of fit for the hazard-rate detection function.</p>
 <div class="section level3">
 <h3 id="adding-covariates-to-the-detection-function">Adding covariates to the detection function<a class="anchor" aria-label="anchor" href="#adding-covariates-to-the-detection-function"></a>
 </h3>
 <p>It is common to include covariates in the detection function (so-called Multiple Covariate Distance Sampling or MCDS). In this dataset there are two covariates that were collected on each individual: Beaufort sea state and size. For brevity we fit only a hazard-rate detection functions with the sea state included as a factor covariate as follows:</p>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">detfc.hr.beau</span><span class="op">&lt;-</span><span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span><span class="op">)</span>, formula<span class="op">=</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">beaufort</span><span class="op">)</span>,</span>
 <span>                  key<span class="op">=</span><span class="st">"hr"</span>, adjustment<span class="op">=</span><span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
 <p>Again looking at the <code>summary</code>,</p>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">detfc.hr.beau</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Summary for distance analysis </span></span>
@@ -376,7 +363,7 @@ <h3 id="adding-covariates-to-the-detection-function">Adding covariates to the de
 <span><span class="co">##                       Estimate         SE        CV</span></span>
 <span><span class="co">## Average p            0.5420677  0.1753555 0.3234937</span></span>
 <span><span class="co">## N in covered region 86.7050383 29.4646397 0.3398262</span></span></code></pre>
-<p>Here the detection function with covariates does not give a lower AIC than the model without covariates (843.71 vs. 841.18 for the hazard-rate model without covariates). Looking back to the bottom-right panel of the EDA plots, we can see there is not a discernible pattern in the plot of Beaufort vs distance.</p>
+<p>Here the detection function with covariates does not give a lower AIC than the model without covariates (843.71 vs. 841.25 for the hazard-rate model without covariates). Looking back to the bottom-right panel of the EDA plots, we can see there is not a discernible pattern in the plot of Beaufort vs distance.</p>
 <p>For brevity, detection function model selection has been omitted here. In practise we would fit many different forms for the detection function (and select a model based on goodness of fit testing and AIC).</p>
 </div>
 </div>
@@ -389,10 +376,10 @@ <h3 id="a-simple-model">A simple model<a class="anchor" aria-label="anchor" href
 </h3>
 <p>We begin with a very simple model. We assume that the number of individuals in each segment are quasi-Poisson distributed and that they are a smooth function of their spatial coordinates (note that the formula is exactly as one would specify to <code>gam</code> in <code>mgcv</code>). By setting <code>group=TRUE</code>, the abundance of clusters/groups rather than individuals can be estimated (though we ignore this here). Note we set <code>method="REML"</code> to ensure that smooth terms are estimated reliably.</p>
 <p>Running the model:</p>
-<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span></code></pre></div>
 <p>We can then obtain a summary of the fitted model:</p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Family: quasipoisson </span></span>
@@ -418,7 +405,7 @@ <h3 id="a-simple-model">A simple model<a class="anchor" aria-label="anchor" href
 <p>The exact interpretation of the model summary results can be found in <span class="citation">(S. N. Wood, 2017)</span>; here we can see various information about the smooth components fitted and general model statistics.</p>
 <p>We can use the deviance explained to compare between models<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content='&lt;p&gt;Note though that the adjusted &lt;span class="math inline"&gt;\(R^2\)&lt;/span&gt; for the model is defined as the proportion of variance explained but the “original” variance used for comparison doesn’t include the offset (the area (or effective of the segments). It is therefore not recommended for one to directly interpret the &lt;span class="math inline"&gt;\(R^2\)&lt;/span&gt; value (see the &lt;code&gt;summary.gam&lt;/code&gt; manual page for further details).&lt;/p&gt;'><sup>2</sup></a>.</p>
 <p>We can also get a rough idea of what the smooth of space looks like using <code>vis.gam</code> (white/yellow indicates high values, red low indicates low values):</p>
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/vis.gam.html" class="external-link">vis.gam</a></span><span class="op">(</span><span class="va">dsm.xy</span>, plot.type<span class="op">=</span><span class="st">"contour"</span>, view<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span>, asp<span class="op">=</span><span class="fl">1</span>, type<span class="op">=</span><span class="st">"response"</span>, contour.col<span class="op">=</span><span class="st">"black"</span>, n.grid<span class="op">=</span><span class="fl">100</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/visgam1-1.png"><!-- --></p>
 <p>The <code>type="response"</code> argument ensures that the plot is on the scale of abundance but the values are relative (as the offsets are set to be their median values). This means that the plot is useful to get an idea of the general shape of the smooth but cannot be interpreted directly.</p>
@@ -427,7 +414,7 @@ <h3 id="a-simple-model">A simple model<a class="anchor" aria-label="anchor" href
 <h3 id="adding-another-environmental-covariate-to-the-spatial-model">Adding another environmental covariate to the spatial model<a class="anchor" aria-label="anchor" href="#adding-another-environmental-covariate-to-the-spatial-model"></a>
 </h3>
 <p>The data set also contains a <code>depth</code> covariate (which we plotted above). We can include in the model very simply:</p>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.depth</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span>,k<span class="op">=</span><span class="fl">10</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">depth</span>,k<span class="op">=</span><span class="fl">20</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
@@ -439,7 +426,7 @@ <h3 id="adding-another-environmental-covariate-to-the-spatial-model">Adding anot
 <span><span class="co">## </span></span>
 <span><span class="co">## Parametric coefficients:</span></span>
 <span><span class="co">##             Estimate Std. Error t value Pr(&gt;|t|)    </span></span>
-<span><span class="co">## (Intercept)  -18.741      1.236  -15.16   &lt;2e-16 ***</span></span>
+<span><span class="co">## (Intercept)  -18.740      1.236  -15.16   &lt;2e-16 ***</span></span>
 <span><span class="co">## ---</span></span>
 <span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
 <span><span class="co">## </span></span>
@@ -455,7 +442,7 @@ <h3 id="adding-another-environmental-covariate-to-the-spatial-model">Adding anot
 <p>Here we see a drop in deviance explained, so perhaps this model is not as useful as the first. We discuss setting the <code>k</code> parameter in <a href="#model-checking">Model checking</a>, below.</p>
 <p>Setting <code>select=TRUE</code> here (as an argument to <code>gam</code>) would impose extra shrinkage terms on each smooth in the model (allowing smooth terms to be removed from the model during fitting; see <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">?gam</a></code> for more information). This is not particularly useful here, so we do not include it. However when there are many environmental predictors is in the model this can be a good way (along with looking at <span class="math inline">\(p\)</span>-values) to perform term selection.</p>
 <p>Simply calling <code>plot</code> on the model object allows us to look at the relationship between depth and the linear predictor:</p>
-<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, select<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm-xy-depth-depth-1.png"><!-- --></p>
 <p>Omitting the argument <code>select</code> in the call to <code>plot</code> will plot each of the smooth terms, one at a time.</p>
@@ -464,10 +451,10 @@ <h3 id="adding-another-environmental-covariate-to-the-spatial-model">Adding anot
 <h3 id="spatial-models-when-there-are-covariates-in-the-detection-function">Spatial models when there are covariates in the detection function<a class="anchor" aria-label="anchor" href="#spatial-models-when-there-are-covariates-in-the-detection-function"></a>
 </h3>
 <p>The code to fit the DSM when there are covariates in the detection function is similar to the other models, above. However since the detection function has observation-level covariates, we must estimate the abundance per segment using a Horvitz-Thompson-like estimator before modelling, so we change the response to be <code>abundance.est</code>:</p>
-<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.est.xy</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">abundance.est</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.beau</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span></code></pre></div>
 <p>As we can see, the <code>summary</code> results are rather similar:</p>
-<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.est.xy</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Family: quasipoisson </span></span>
@@ -491,7 +478,7 @@ <h3 id="spatial-models-when-there-are-covariates-in-the-detection-function">Spat
 <span><span class="co">## R-sq.(adj) =  0.129   Deviance explained = 42.2%</span></span>
 <span><span class="co">## -REML = 1043.6  Scale est. = 174.1     n = 387</span></span></code></pre>
 <p>As is the resulting spatial smooth (though the resulting surface is somewhat “amplified”):</p>
-<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/vis.gam.html" class="external-link">vis.gam</a></span><span class="op">(</span><span class="va">dsm.est.xy</span>, plot.type<span class="op">=</span><span class="st">"contour"</span>, view<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span>, asp<span class="op">=</span><span class="fl">1</span>, type<span class="op">=</span><span class="st">"response"</span>, zlim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">300</span><span class="op">)</span>, contour.col<span class="op">=</span><span class="st">"black"</span>, n.grid<span class="op">=</span><span class="fl">100</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/visgam5-1.png"><!-- --></p>
 </div>
@@ -504,7 +491,7 @@ <h3 id="other-response-distributions">Other response distributions<a class="anch
 <h4 id="tweedie">Tweedie<a class="anchor" aria-label="anchor" href="#tweedie"></a>
 </h4>
 <p>Response distributions other than the quasi-Poisson can be used, for example the Tweedie distribution. The Tweedie distribution is available in <code>dsm</code> by setting <code>family=tw()</code>.</p>
-<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.tweedie</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, family<span class="op">=</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></span><span class="op">(</span><span class="op">)</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
@@ -516,7 +503,7 @@ <h4 id="tweedie">Tweedie<a class="anchor" aria-label="anchor" href="#tweedie"></
 <span><span class="co">## </span></span>
 <span><span class="co">## Parametric coefficients:</span></span>
 <span><span class="co">##             Estimate Std. Error t value Pr(&gt;|t|)    </span></span>
-<span><span class="co">## (Intercept) -17.2651     0.2363  -73.08   &lt;2e-16 ***</span></span>
+<span><span class="co">## (Intercept) -17.2640     0.2363  -73.07   &lt;2e-16 ***</span></span>
 <span><span class="co">## ---</span></span>
 <span><span class="co">## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
 <span><span class="co">## </span></span>
@@ -544,18 +531,18 @@ <h4 id="soap-film-smoothing">Soap film smoothing<a class="anchor" aria-label="an
 </h4>
 <p>To account for a complex region (e.g., a region that includes peninsulae) we can use the soap film smoother <span class="citation">(Simon N. Wood, Bravington, &amp; Hedley, 2008)</span>.</p>
 <p>To use a soap film smoother for the spatial part of the model we must create a set of knots for the smoother to use. This is easily done using the <code><a href="../../reference/make.soapgrid.html">make.soapgrid()</a></code> function in <code>dsm</code>:</p>
-<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb49"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">soap.knots</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/make.soapgrid.html">make.soapgrid</a></span><span class="op">(</span><span class="va">survey.area</span>,<span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">15</span>,<span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>where the second argument specifies the number of points (in each direction) in the grid that will be used to create the knots (knots in the grid outside of <code>survey.area</code> are removed).</p>
 <p>As we saw in the exploratory analysis, some of the transect lines are outside of the survey area. These will cause the soap film smoother to fail, so we remove them:</p>
-<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">segdata</span><span class="op">$</span><span class="va">x</span>; <span class="va">y</span><span class="op">&lt;-</span><span class="va">segdata</span><span class="op">$</span><span class="va">y</span></span>
 <span><span class="va">onoff</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/inSide.html" class="external-link">inSide</a></span><span class="op">(</span>x<span class="op">=</span><span class="va">x</span>,y<span class="op">=</span><span class="va">y</span>, bnd<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">as.list</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/rm.html" class="external-link">rm</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span></span>
 <span><span class="va">segdata.soap</span> <span class="op">&lt;-</span> <span class="va">segdata</span><span class="op">[</span><span class="va">onoff</span>,<span class="op">]</span></span></code></pre></div>
 <p>Note that the <a href="https://github.com/dill/soap_checker" class="external-link"><code>soap_checker</code> script available here</a> can be useful in ensuring that the boundary, data and knots are in the correct format to use with the soap film smoother.</p>
 <p>We can run a model with both the <code>depth</code> covariate along with a spatial (soap film) smooth. Note that the <code>k</code> argument now refers to the complexity of the boundary smooth in the soap film, and the complexity of the film is controlled by the knots given in the <code>xt</code> argument.</p>
-<div class="sourceCode" id="cb48"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb51"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.tweedie.soap</span><span class="op">&lt;-</span><span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, bs<span class="op">=</span><span class="st">"so"</span>, k<span class="op">=</span><span class="fl">15</span>, xt<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>bnd<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>            <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">depth</span><span class="op">)</span>,</span>
 <span>          family<span class="op">=</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></span><span class="op">(</span><span class="op">)</span>, method<span class="op">=</span><span class="st">"REML"</span>,</span>
@@ -592,13 +579,13 @@ <h2 id="model-checking">Model checking<a class="anchor" aria-label="anchor" href
 </h2>
 <p>Fitting models is all well and good, but we’d like to confirm that the models we have are reasonable; <code>dsm</code> provides some functions for model checking.</p>
 <p>We can use <code>gam.check</code> to generate diagnostic plots:</p>
-<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb53"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></span><span class="op">(</span><span class="va">dsm.xy</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy-check-1.png"><!-- --></p>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Method: REML   Optimizer: outer newton</span></span>
 <span><span class="co">## full convergence after 6 iterations.</span></span>
-<span><span class="co">## Gradient range [-7.105947e-08,-9.826417e-09]</span></span>
+<span><span class="co">## Gradient range [-7.105956e-08,-9.826305e-09]</span></span>
 <span><span class="co">## (score 936.0363 &amp; scale 94.3674).</span></span>
 <span><span class="co">## Hessian positive definite, eigenvalue range [5.800328,192.5304].</span></span>
 <span><span class="co">## Model rank =  30 / 30 </span></span>
@@ -614,7 +601,7 @@ <h2 id="model-checking">Model checking<a class="anchor" aria-label="anchor" href
 <p>By setting the <code>k</code> parameter we specify the largest complexity for that smooth term in the model; as long as this is high enough, we can be sure that there is enough flexibility. In the output from <code>gam.check</code> above, we can see that there is a “p-value” calculated for the size of the basis, this can be a good guide as to whether the basis size needs to be increased.</p>
 <p>The <code><a href="https://rdrr.io/pkg/mgcv/man/choose.k.html" class="external-link">?choose.k</a></code> manual page from <code>mgcv</code> gives further guidance and technical details on this matter.</p>
 <p>We can look at the same model form but with a Tweedie distribution specified as the response:</p>
-<div class="sourceCode" id="cb52"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb55"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy.tweedie-check-1.png"><!-- --></p>
 <pre><code><span><span class="co">## </span></span>
@@ -637,7 +624,7 @@ <h4 id="randomised-quantile-residuals">Randomised quantile residuals<a class="an
 </h4>
 <p>In the top right panel of the above <code>gam.check</code> plots the residuals vs. linear predictor plot includes a odd line of predictions. These are an artifact of the link function, showing the exact zeros in the data. These can be misleading and distracting, making it difficult to see whether residuals show heteroskedasticity.</p>
 <p>Randomised quantile residuals <span class="citation">(Dunn &amp; Smyth, 1996)</span> avoid this issue by transforming the residuals to be exactly normally distributed. This makes the residuals vs. linear predictor plot much easier to interpret as it therefore doesn’t include the artifacts generated by the link function. These plots can be produced using <code>rqgam.check</code> in <code>dsm</code>:</p>
-<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../../reference/rqgam_check.html">rqgam_check</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy.tweedie-rqcheck-1.png"><!-- --></p>
 <p>Here we can see that there is no issue with heteroskedasticity (no increase in spread in the residuals vs. linear predictor plot with increasing values of the linear predictor). One can also plot these residuals against covariate values to check for pattern in the residuals.</p>
@@ -649,7 +636,7 @@ <h2 id="model-selection">Model selection<a class="anchor" aria-label="anchor" hr
 </h2>
 <p>Assuming that models have “passed” the checks in <code>gam.check</code>, <code>rqgam.check</code> and are sufficiently flexible, we may be left with a choice of which model is “best”. There are several methods for choosing the best model – AIC, REML/GCV scores, deviance explained, full cross-validation with test data and so on.</p>
 <p>Though this document does not intend to be a full analysis of the pantropical dolphin data, we can create a results table to compare the various models that have been fitted so far in terms of their abundance estimates and associated uncertainties.</p>
-<div class="sourceCode" id="cb55"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb58"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># make a data.frame to print out</span></span>
 <span><span class="va">mod_results</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="st">"Model name"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"`dsm.xy`"</span>, <span class="st">"`dsm.xy.depth`"</span>, <span class="st">"`dsm.xy.tweedie`"</span>, <span class="st">"`dsm.xy.tweedie.soap`"</span>,</span>
 <span>                                           <span class="st">"`dsm.est.xy`"</span><span class="op">)</span>,</span>
@@ -664,7 +651,7 @@ <h2 id="model-selection">Model selection<a class="anchor" aria-label="anchor" hr
 <span>                                                                      <span class="va">dsm.xy.tweedie.soap</span><span class="op">)</span>,</span>
 <span>                 <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">{</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">$</span><span class="va">dev.expl</span><span class="op">*</span><span class="fl">100</span>,<span class="fl">2</span><span class="op">)</span>,<span class="st">"%"</span><span class="op">)</span><span class="op">}</span><span class="op">)</span><span class="op">)</span>,<span class="cn">NA</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>We can then use the resulting <code>data.frame</code> to build a table of results using the <code>kable</code> function:</p>
-<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">kable</span><span class="op">(</span><span class="va">mod_results</span>, col.names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Model name"</span>, <span class="st">"Description"</span>, <span class="st">"Deviance explained"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
@@ -710,10 +697,10 @@ <h2 id="model-selection">Model selection<a class="anchor" aria-label="anchor" hr
 <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="anchor" href="#abundance-estimation"></a>
 </h2>
 <p>Once a model has been checked and selected, we can make predictions over the grid and calculate abundance. The offset is stored in the <code>area</code> column<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content='&lt;p&gt;An earlier version of this vignette incorrectly stated that the areas of the prediction cells were 444km&lt;span class="math inline"&gt;\(^2\)&lt;/span&gt;. This has been corrected. Thanks to Phil Bouchet for pointing this out.&lt;/p&gt;'><sup>3</sup></a>.</p>
-<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">dsm.xy</span>, <span class="va">preddata</span>, <span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></code></pre></div>
 <p>Given predicted abundance estimates for each cell in the prediction grid, these are connected to the prediction grid object for mapping:</p>
-<div class="sourceCode" id="cb58"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">prediction_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_make_grid.html" class="external-link">st_make_grid</a></span><span class="op">(</span><span class="va">area.sf.proj</span>, cellsize <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">9000</span>,<span class="fl">9000</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">prediction_grid_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/sf.html" class="external-link">st_sf</a></span><span class="op">(</span>geometry <span class="op">=</span> <span class="va">prediction_grid</span><span class="op">)</span></span>
 <span></span>
@@ -723,7 +710,7 @@ <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">Prediction_xy</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
@@ -733,11 +720,11 @@ <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="
 <span><span class="va">pred</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy-preds-1.png"><!-- --></p>
 <p>We can calculate abundance over the survey area by simply summing these predictions:</p>
-<div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">dsm.xy.pred</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] 28430.76</span></span></code></pre>
+<pre><code><span><span class="co">## [1] 28462.22</span></span></code></pre>
 <p>We can compare this with a plot of the predictions from this <code>dsm.xy.depth</code>:</p>
-<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.depth.pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, <span class="va">preddata</span>, <span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">Prediction_xy_depth</span> <span class="op">&lt;-</span> <span class="va">dsm.xy.depth.pred</span></span>
@@ -746,7 +733,7 @@ <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb65"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb68"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">Prediction_xy_depth</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
@@ -756,9 +743,9 @@ <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="
 <span><span class="va">pred</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy.depth-preds-1.png"><!-- --></p>
 <p>We can see the inclusion of depth into the model has had a noticeable effect on the distribution (note the difference in legend scale between the two plots). We can again also look at the total abundance:</p>
-<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb69"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">dsm.xy.depth.pred</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] 27054.6</span></span></code></pre>
+<pre><code><span><span class="co">## [1] 27084.54</span></span></code></pre>
 <p>Here we see that there is not much of a change in the abundance, so in terms of abundance alone there isn’t much between the two models. Next we’ll go on to look at variance next where we can see bigger differences between the models.</p>
 </div>
 <div class="section level2">
@@ -774,35 +761,35 @@ <h2 id="variance-estimation">Variance estimation<a class="anchor" aria-label="an
 </ul>
 <p><code>dsm_var_prop</code> can only be applied when there is a covariate in the detection function that varies at the level of the segments, and is recorded at each segment (for example Beaufort). We don’t have that situation here, so we opt for <code>dsm_var_gam</code>.</p>
 <p>Both methods estimate the variance of the abundance for each element in the list provided in <code>pred.data</code>. In our case we wish to obtain an abundance for each of the prediction cells, so we use <code>split</code> to chop our data set into list elements to give to <code>dsm_var_gam</code> (or <code>dsm_var_prop</code>).</p>
-<div class="sourceCode" id="cb68"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">preddata.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/split.html" class="external-link">split</a></span><span class="op">(</span><span class="va">preddata</span>, <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">preddata</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">dsm.xy.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm_var_gam.html">dsm_var_gam</a></span><span class="op">(</span><span class="va">dsm.xy</span>, pred.data<span class="op">=</span><span class="va">preddata.var</span>,</span>
 <span>                          off.set<span class="op">=</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></code></pre></div>
 <p>Calling <code>summary</code> will give some information about uncertainty estimation:</p>
-<div class="sourceCode" id="cb69"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb72"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.var</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Summary of uncertainty in a density surface model calculated</span></span>
 <span><span class="co">##  analytically for GAM, with delta method</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Approximate asymptotic confidence interval:</span></span>
 <span><span class="co">##     2.5%     Mean    97.5% </span></span>
-<span><span class="co">## 15252.81 28430.76 52994.04 </span></span>
+<span><span class="co">## 16091.72 28430.76 50231.32 </span></span>
 <span><span class="co">## (Using log-Normal approximation)</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Point estimate                 : 28430.76 </span></span>
-<span><span class="co">## CV of detection function       : 0.2816462 </span></span>
+<span><span class="co">## CV of detection function       : 0.2471842 </span></span>
 <span><span class="co">## CV from GAM                    : 0.164 </span></span>
-<span><span class="co">## Total standard error           : 9265.663 </span></span>
-<span><span class="co">## Total coefficient of variation : 0.3259</span></span></code></pre>
+<span><span class="co">## Total standard error           : 8433.356 </span></span>
+<span><span class="co">## Total coefficient of variation : 0.2966</span></span></code></pre>
 <p>We can also make a plot of the CVs (with transect lines and observations overlaid) using the following code.</p>
-<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">CV</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">dsm.xy.var</span><span class="op">$</span><span class="va">pred.var</span><span class="op">)</span><span class="op">/</span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">Prediction_xy</span></span>
 <span></span>
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">prediction_grid_sf</span>, <span class="va">preddata_sf</span>, join <span class="op">=</span> <span class="va">st_nearest_feature</span><span class="op">)</span></span>
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb76"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">CV</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">CV</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
@@ -813,7 +800,7 @@ <h2 id="variance-estimation">Variance estimation<a class="anchor" aria-label="an
 <p><img src="mexico-analysis_files/figure-html/dsm.xyvarplot-1.png"><!-- --></p>
 <p>Note the increase in CV away from the transect lines.</p>
 <p>We can revisit the model that included both depth and location smooths and observe that the coefficient of variation for that model is larger than that of the model with only the location smooth.</p>
-<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb77"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.depth.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm_var_gam.html">dsm_var_gam</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, pred.data<span class="op">=</span><span class="va">preddata.var</span>,</span>
 <span>                                off.set<span class="op">=</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.depth.var</span><span class="op">)</span></span></code></pre></div>
@@ -874,7 +861,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 </div>
 </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -885,15 +872,15 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
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 </div>
 
-  
 
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 </html>
diff --git a/docs/articles/lines_gomex/mexico-analysis_files/figure-html/dsm.xyvarplot-1.png b/docs/articles/lines_gomex/mexico-analysis_files/figure-html/dsm.xyvarplot-1.png
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diff --git a/docs/authors.html b/docs/authors.html
index 1612e35..02e04a0 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,101 +1,80 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • dsm</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • dsm</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"></head><body>
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+    <li><a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
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-      <img src="" class="logo" alt=""><h1>Authors and Citation</h1>
+
+      <h1>Authors and Citation</h1>
     </div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2>Authors</h2>
-      
+
       <ul class="list-unstyled"><li>
-          <p><strong>David L. Miller</strong>. Author. 
+          <p><strong>David L. Miller</strong>. Author.
           </p>
         </li>
         <li>
-          <p><strong>Eric Rexstad</strong>. Contributor. 
+          <p><strong>Eric Rexstad</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Louise Burt</strong>. Contributor. 
+          <p><strong>Louise Burt</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Mark V. Bravington</strong>. Contributor. 
+          <p><strong>Mark V. Bravington</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Sharon Hedley</strong>. Contributor. 
+          <p><strong>Sharon Hedley</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Megan Ferguson</strong>. Contributor. 
+          <p><strong>Megan Ferguson</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Natalie Kelly</strong>. Contributor. 
+          <p><strong>Natalie Kelly</strong>. Contributor.
           </p>
         </li>
       </ul></div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2 id="citation">Citation</h2>
       <p><small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
       <p>Miller DL (2024).
 <em>dsm: Density Surface Modelling of Distance Sampling Data</em>.
-R package version 2.3.3.9001, <a href="https://github.com/DistanceDevelopment/dsm" class="external-link">https://github.com/DistanceDevelopment/dsm</a>. 
+R package version 2.3.3.9001, <a href="https://github.com/DistanceDevelopment/dsm" class="external-link">https://github.com/DistanceDevelopment/dsm</a>.
 </p>
       <pre>@Manual{,
   title = {dsm: Density Surface Modelling of Distance Sampling Data},
@@ -105,7 +84,8 @@ <h2 id="citation">Citation</h2>
   url = {https://github.com/DistanceDevelopment/dsm},
 }</pre>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -114,14 +94,14 @@ <h2 id="citation">Citation</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.min.css b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
index 8db4478..68a32c8 100644
--- a/docs/deps/bootstrap-5.3.1/bootstrap.min.css
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
@@ -2,4 +2,4 @@
    * Bootstrap  v5.3.1 (https://getbootstrap.com/)
    * Copyright 2011-2023 The Bootstrap Authors
    * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
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-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) 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var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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.show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23191918'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23021a41'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #82a1d1;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(5,66,163,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23698ec8'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(5,66,163,0.25);--bs-pagination-active-color: #FCFAF2;--bs-pagination-active-bg: #0542A3;--bs-pagination-active-border-color: #0542A3;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #FCFAF2;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #FCFAF2;--bs-progress-bar-bg: #0542A3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(252,250,242,0.15) 25%, transparent 25%, transparent 50%, rgba(252,250,242,0.15) 50%, rgba(252,250,242,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 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-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) 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var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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.show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23191918'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23021a41'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #82a1d1;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(5,66,163,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23698ec8'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(5,66,163,0.25);--bs-pagination-active-color: #FCFAF2;--bs-pagination-active-bg: #0542A3;--bs-pagination-active-border-color: #0542A3;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #FCFAF2;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #FCFAF2;--bs-progress-bar-bg: #0542A3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(252,250,242,0.15) 25%, transparent 25%, transparent 50%, rgba(252,250,242,0.15) 50%, rgba(252,250,242,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 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diff --git a/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js b/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
new file mode 100644
index 0000000..c628326
--- /dev/null
+++ b/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap Table of Contents v1.0.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+!function(a){"use strict";window.Toc={helpers:{findOrFilter:function(e,t){var n=e.find(t);return e.filter(t).add(n).filter(":not([data-toc-skip])")},generateUniqueIdBase:function(e){return a(e).text().trim().replace(/\'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,64).replace(/^-+|-+$/gm,"").toLowerCase()||e.tagName.toLowerCase()},generateUniqueId:function(e){for(var t=this.generateUniqueIdBase(e),n=0;;n++){var r=t;if(0<n&&(r+="-"+n),!document.getElementById(r))return r}},generateAnchor:function(e){if(e.id)return e.id;var t=this.generateUniqueId(e);return e.id=t},createNavList:function(){return a('<ul class="nav navbar-nav"></ul>')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('<a class="nav-link"></a>');n.attr("href","#"+e),n.text(t);var r=a("<li></li>");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1<this.findOrFilter(e,"h"+t).length)return t}return 1},getHeadings:function(e,t){var n="h"+t,r="h"+(t+1);return this.findOrFilter(e,n+","+r)},getNavLevel:function(e){return parseInt(e.tagName.charAt(1),10)},populateNav:function(r,a,e){var i,s=r,c=this;e.each(function(e,t){var n=c.generateNavItem(t);c.getNavLevel(t)===a?s=r:i&&s===r&&(s=c.createChildNavList(i)),s.append(n),i=n})},parseOps:function(e){var t;return(t=e.jquery?{$nav:e}:e).$scope=t.$scope||a(document.body),t}},init:function(e){(e=this.helpers.parseOps(e)).$nav.attr("data-toggle","toc");var t=this.helpers.createChildNavList(e.$nav),n=this.helpers.getTopLevel(e.$scope),r=this.helpers.getHeadings(e.$scope,n);this.helpers.populateNav(t,n,r)}},a(function(){a('nav[data-toggle="toc"]').each(function(e,t){var n=a(t);Toc.init(n)})})}(jQuery);
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diff --git a/docs/deps/clipboard.js-2.0.11/clipboard.min.js b/docs/deps/clipboard.js-2.0.11/clipboard.min.js
new file mode 100644
index 0000000..1103f81
--- /dev/null
+++ b/docs/deps/clipboard.js-2.0.11/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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diff --git a/docs/deps/data-deps.txt b/docs/deps/data-deps.txt
index 693b82d..8aa49e2 100644
--- a/docs/deps/data-deps.txt
+++ b/docs/deps/data-deps.txt
@@ -2,3 +2,12 @@
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
+<link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet" />
+<script src="deps/headroom-0.11.0/headroom.min.js"></script>
+<script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script>
+<script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script>
+<script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script>
+<script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script>
+<script src="deps/search-1.0.0/fuse.min.js"></script>
+<script src="deps/search-1.0.0/mark.min.js"></script>
diff --git a/docs/deps/font-awesome-6.4.2/css/all.css b/docs/deps/font-awesome-6.4.2/css/all.css
new file mode 100644
index 0000000..bdb6e3a
--- /dev/null
+++ b/docs/deps/font-awesome-6.4.2/css/all.css
@@ -0,0 +1,7968 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa {
+  font-family: var(--fa-style-family, "Font Awesome 6 Free");
+  font-weight: var(--fa-style, 900); }
+
+.fa,
+.fa-classic,
+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, none));
+          transform: rotate(var(--fa-rotate-angle, none)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
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+  content: "\f086"; }
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+  content: "\f0ea"; }
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+  content: "\f0ea"; }
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+  content: "\e13c"; }
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+  content: "\f46d"; }
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+  content: "\f4de"; }
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+  content: "\f4de"; }
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+  content: "\f4fc"; }
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+  content: "\e597"; }
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+  content: "\e571"; }
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+  content: "\f781"; }
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+  content: "\f504"; }
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+  content: "\e539"; }
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+  content: "\f6a0"; }
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+  content: "\f6a0"; }
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+  content: "\f458"; }
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+  content: "\f458"; }
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+  content: "\f458"; }
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+  content: "\f204"; }
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+  content: "\f187"; }
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+  content: "\f187"; }
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+  content: "\e545"; }
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+  content: "\f886"; }
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+  content: "\f886"; }
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+  content: "\f886"; }
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+  content: "\f58a"; }
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+  content: "\f58a"; }
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+  content: "\f5bd"; }
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+  content: "\f63b"; }
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+  content: "\57"; }
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+  content: "\f57c"; }
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+  content: "\f57c"; }
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+  content: "\f75b"; }
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+  content: "\f1ce"; }
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+  content: "\f3fa"; }
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+.fa-tablet-alt::before {
+  content: "\f3fa"; }
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+  content: "\f1b0"; }
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+  content: "\f0c2"; }
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+  content: "\e58a"; }
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+  content: "\f579"; }
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+  content: "\f579"; }
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+  content: "\f80d"; }
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+  content: "\e57f"; }
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+  content: "\f0e3"; }
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+  content: "\f0e3"; }
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+  content: "\f1e5"; }
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+  content: "\f131"; }
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+  content: "\e05b"; }
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+  content: "\f21c"; }
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+  content: "\f562"; }
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+  content: "\f562"; }
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+  content: "\f5ae"; }
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+  content: "\f5ae"; }
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+  content: "\e068"; }
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+  content: "\e068"; }
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+  content: "\e523"; }
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+  content: "\f152"; }
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+  content: "\f152"; }
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+  content: "\f0c4"; }
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+  content: "\f0c4"; }
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+  content: "\e57a"; }
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+  content: "\e584"; }
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+  content: "\f453"; }
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+  content: "\f0ce"; }
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+  content: "\e521"; }
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+  content: "\f566"; }
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+  content: "\f566"; }
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+  content: "\e073"; }
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+  content: "\e139"; }
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+  content: "\f3e5"; }
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+  content: "\f3e5"; }
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+  content: "\f699"; }
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+  content: "\e50c"; }
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+  content: "\f146"; }
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+  content: "\f146"; }
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+  content: "\f533"; }
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+  content: "\f14e"; }
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+  content: "\f150"; }
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+  content: "\f150"; }
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+  content: "\e4ef"; }
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+  content: "\f5fc"; }
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+  content: "\f5c3"; }
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+  content: "\f485"; }
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+  content: "\f0c9"; }
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+  content: "\f0c9"; }
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+  content: "\e533"; }
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+  content: "\f253"; }
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+.fa-hourglass-3::before {
+  content: "\f253"; }
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+.fa-heart-crack::before {
+  content: "\f7a9"; }
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+  content: "\f7a9"; }
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+  content: "\f360"; }
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+  content: "\f360"; }
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+  content: "\f597"; }
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+  content: "\f597"; }
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+  content: "\f008"; }
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+  content: "\f547"; }
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+.fa-people-robbery::before {
+  content: "\e536"; }
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+.fa-lightbulb::before {
+  content: "\f0eb"; }
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+.fa-caret-left::before {
+  content: "\f0d9"; }
+
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+  content: "\f06a"; }
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+  content: "\f06a"; }
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+  content: "\e56d"; }
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+  content: "\f08b"; }
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+  content: "\f08b"; }
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+  content: "\f13a"; }
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+  content: "\f13a"; }
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+  content: "\f13e"; }
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+  content: "\f13e"; }
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+  content: "\f740"; }
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+  content: "\f58f"; }
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+.fa-headphones-alt::before {
+  content: "\f58f"; }
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+  content: "\f0e8"; }
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+  content: "\f4b9"; }
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+  content: "\f4b9"; }
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+  content: "\f538"; }
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+  content: "\e568"; }
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+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
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+.fa-hanukiah::before {
+  content: "\f6e6"; }
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+  content: "\f52d"; }
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+  content: "\f027"; }
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+  content: "\f027"; }
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+  content: "\f4b3"; }
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+  content: "\f743"; }
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+  content: "\f066"; }
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+  content: "\e2cd"; }
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+  content: "\e2cd"; }
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+  content: "\f644"; }
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+  content: "\e4fa"; }
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+  content: "\2a"; }
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+  content: "\f14a"; }
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+  content: "\f14a"; }
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+  content: "\e221"; }
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+  content: "\f1dc"; }
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+  content: "\f1dc"; }
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+  content: "\f6e2"; }
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+  content: "\f03a"; }
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+  content: "\f03a"; }
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+  content: "\f87b"; }
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+  content: "\f87b"; }
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+  content: "\f217"; }
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+.fa-gamepad::before {
+  content: "\f11b"; }
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+  content: "\f192"; }
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+  content: "\f192"; }
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+  content: "\f567"; }
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+  content: "\f567"; }
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+  content: "\f7fb"; }
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+  content: "\e513"; }
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+.fa-campground::before {
+  content: "\f6bb"; }
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+  content: "\f65e"; }
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+.fa-futbol::before {
+  content: "\f1e3"; }
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+  content: "\f1e3"; }
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+  content: "\f1e3"; }
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+  content: "\f1fc"; }
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+  content: "\f1fc"; }
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+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
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+.fa-hot-tub::before {
+  content: "\f593"; }
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+.fa-map-location::before {
+  content: "\f59f"; }
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+.fa-map-marked::before {
+  content: "\f59f"; }
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+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
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+.fa-bridge-lock::before {
+  content: "\e4cc"; }
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+  content: "\f81d"; }
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+  content: "\f044"; }
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+  content: "\f044"; }
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+.fa-car-side::before {
+  content: "\f5e4"; }
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+.fa-share-nodes::before {
+  content: "\f1e0"; }
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+  content: "\f1e0"; }
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+  content: "\e4ff"; }
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+.fa-hourglass-half::before {
+  content: "\f252"; }
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+  content: "\f252"; }
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+.fa-microscope::before {
+  content: "\f610"; }
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+  content: "\e06d"; }
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+  content: "\f290"; }
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+  content: "\f290"; }
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+  content: "\f881"; }
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+  content: "\f881"; }
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+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
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+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
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+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
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+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
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+  content: "\e4cb"; }
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+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
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+  content: "\f58c"; }
+
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+  content: "\f58c"; }
+
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+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
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+  content: "\f2a4"; }
+
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+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
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+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
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+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
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+  content: "\f57a"; }
+
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+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
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+  content: "\f0a7"; }
+
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+  content: "\e09a"; }
+
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+  content: "\f07b"; }
+
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+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
+.fa-ad::before {
+  content: "\f641"; }
+
+.fa-circle-arrow-right::before {
+  content: "\f0a9"; }
+
+.fa-arrow-circle-right::before {
+  content: "\f0a9"; }
+
+.fa-filter-circle-xmark::before {
+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
+
+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
+
+.fa-list-numeric::before {
+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
+.fa-level-up::before {
+  content: "\f148"; }
+
+.fa-u::before {
+  content: "\55"; }
+
+.fa-square-root-variable::before {
+  content: "\f698"; }
+
+.fa-square-root-alt::before {
+  content: "\f698"; }
+
+.fa-clock::before {
+  content: "\f017"; }
+
+.fa-clock-four::before {
+  content: "\f017"; }
+
+.fa-backward-step::before {
+  content: "\f048"; }
+
+.fa-step-backward::before {
+  content: "\f048"; }
+
+.fa-pallet::before {
+  content: "\f482"; }
+
+.fa-faucet::before {
+  content: "\e005"; }
+
+.fa-baseball-bat-ball::before {
+  content: "\f432"; }
+
+.fa-s::before {
+  content: "\53"; }
+
+.fa-timeline::before {
+  content: "\e29c"; }
+
+.fa-keyboard::before {
+  content: "\f11c"; }
+
+.fa-caret-down::before {
+  content: "\f0d7"; }
+
+.fa-house-chimney-medical::before {
+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
+.fa-mobile-android-alt::before {
+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
+
+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
+
+.fa-khanda::before {
+  content: "\f66d"; }
+
+.fa-sliders::before {
+  content: "\f1de"; }
+
+.fa-sliders-h::before {
+  content: "\f1de"; }
+
+.fa-folder-tree::before {
+  content: "\f802"; }
+
+.fa-network-wired::before {
+  content: "\f6ff"; }
+
+.fa-map-pin::before {
+  content: "\f276"; }
+
+.fa-hamsa::before {
+  content: "\f665"; }
+
+.fa-cent-sign::before {
+  content: "\e3f5"; }
+
+.fa-flask::before {
+  content: "\f0c3"; }
+
+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
+.fa-wand-sparkles::before {
+  content: "\f72b"; }
+
+.fa-ellipsis-vertical::before {
+  content: "\f142"; }
+
+.fa-ellipsis-v::before {
+  content: "\f142"; }
+
+.fa-ticket::before {
+  content: "\f145"; }
+
+.fa-power-off::before {
+  content: "\f011"; }
+
+.fa-right-long::before {
+  content: "\f30b"; }
+
+.fa-long-arrow-alt-right::before {
+  content: "\f30b"; }
+
+.fa-flag-usa::before {
+  content: "\f74d"; }
+
+.fa-laptop-file::before {
+  content: "\e51d"; }
+
+.fa-tty::before {
+  content: "\f1e4"; }
+
+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
+
+.fa-person-rifle::before {
+  content: "\e54e"; }
+
+.fa-house-medical-circle-exclamation::before {
+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
+
+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
+
+.fa-calculator::before {
+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
+
+.fa-n::before {
+  content: "\4e"; }
+
+.fa-cable-car::before {
+  content: "\f7da"; }
+
+.fa-tram::before {
+  content: "\f7da"; }
+
+.fa-cloud-rain::before {
+  content: "\f73d"; }
+
+.fa-building-circle-xmark::before {
+  content: "\e4d4"; }
+
+.fa-ship::before {
+  content: "\f21a"; }
+
+.fa-arrows-down-to-line::before {
+  content: "\e4b8"; }
+
+.fa-download::before {
+  content: "\f019"; }
+
+.fa-face-grin::before {
+  content: "\f580"; }
+
+.fa-grin::before {
+  content: "\f580"; }
+
+.fa-delete-left::before {
+  content: "\f55a"; }
+
+.fa-backspace::before {
+  content: "\f55a"; }
+
+.fa-eye-dropper::before {
+  content: "\f1fb"; }
+
+.fa-eye-dropper-empty::before {
+  content: "\f1fb"; }
+
+.fa-eyedropper::before {
+  content: "\f1fb"; }
+
+.fa-file-circle-check::before {
+  content: "\e5a0"; }
+
+.fa-forward::before {
+  content: "\f04e"; }
+
+.fa-mobile::before {
+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
+
+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
+.fa-align-center::before {
+  content: "\f037"; }
+
+.fa-book-skull::before {
+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
+.fa-id-card::before {
+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
+.fa-heart-circle-exclamation::before {
+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
+.fa-home::before {
+  content: "\f015"; }
+
+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
+
+.fa-file-medical::before {
+  content: "\f477"; }
+
+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
+
+.fa-arrow-right-arrow-left::before {
+  content: "\f0ec"; }
+
+.fa-exchange::before {
+  content: "\f0ec"; }
+
+.fa-rotate-right::before {
+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
+
+.fa-rotate-forward::before {
+  content: "\f2f9"; }
+
+.fa-utensils::before {
+  content: "\f2e7"; }
+
+.fa-cutlery::before {
+  content: "\f2e7"; }
+
+.fa-arrow-up-wide-short::before {
+  content: "\f161"; }
+
+.fa-sort-amount-up::before {
+  content: "\f161"; }
+
+.fa-mill-sign::before {
+  content: "\e1ed"; }
+
+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
+.fa-long-arrow-alt-up::before {
+  content: "\f30c"; }
+
+.fa-stop::before {
+  content: "\f04d"; }
+
+.fa-code-merge::before {
+  content: "\f387"; }
+
+.fa-upload::before {
+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
+
+.fa-mound::before {
+  content: "\e52d"; }
+
+.fa-toilet-portable::before {
+  content: "\e583"; }
+
+.fa-compact-disc::before {
+  content: "\f51f"; }
+
+.fa-file-arrow-down::before {
+  content: "\f56d"; }
+
+.fa-file-download::before {
+  content: "\f56d"; }
+
+.fa-caravan::before {
+  content: "\f8ff"; }
+
+.fa-shield-cat::before {
+  content: "\e572"; }
+
+.fa-bolt::before {
+  content: "\f0e7"; }
+
+.fa-zap::before {
+  content: "\f0e7"; }
+
+.fa-glass-water::before {
+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
+.fa-plane-circle-xmark::before {
+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
+
+.fa-yen::before {
+  content: "\f157"; }
+
+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
+.fa-sun::before {
+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
+.fa-face-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrow-alt-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrows-turn-to-dots::before {
+  content: "\e4c1"; }
+
+.fa-florin-sign::before {
+  content: "\e184"; }
+
+.fa-arrow-down-short-wide::before {
+  content: "\f884"; }
+
+.fa-sort-amount-desc::before {
+  content: "\f884"; }
+
+.fa-sort-amount-down-alt::before {
+  content: "\f884"; }
+
+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
+
+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
+
+.fa-user-lock::before {
+  content: "\f502"; }
+
+.fa-arrow-right-long::before {
+  content: "\f178"; }
+
+.fa-long-arrow-right::before {
+  content: "\f178"; }
+
+.fa-anchor-circle-xmark::before {
+  content: "\e4ac"; }
+
+.fa-ellipsis::before {
+  content: "\f141"; }
+
+.fa-ellipsis-h::before {
+  content: "\f141"; }
+
+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
+.fa-first-aid::before {
+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
+.fa-hands-holding-circle::before {
+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
+.fa-book-open-reader::before {
+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
+
+.fa-square-h::before {
+  content: "\f0fd"; }
+
+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
+.fa-map-marker-alt::before {
+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
+
+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
+.fa-droplet::before {
+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
+
+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
+
+.fa-stream::before {
+  content: "\f550"; }
+
+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
+
+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
+
+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
+.fa-expand::before {
+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
+.fa-arrow-trend-up::before {
+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-arrow-turn-right::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
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+
+.fa-pied-piper-square:before {
+  content: "\e01e"; }
+
+.fa-creative-commons-nd:before {
+  content: "\f4eb"; }
+
+.fa-palfed:before {
+  content: "\f3d8"; }
+
+.fa-superpowers:before {
+  content: "\f2dd"; }
+
+.fa-resolving:before {
+  content: "\f3e7"; }
+
+.fa-xbox:before {
+  content: "\f412"; }
+
+.fa-searchengin:before {
+  content: "\f3eb"; }
+
+.fa-tiktok:before {
+  content: "\e07b"; }
+
+.fa-square-facebook:before {
+  content: "\f082"; }
+
+.fa-facebook-square:before {
+  content: "\f082"; }
+
+.fa-renren:before {
+  content: "\f18b"; }
+
+.fa-linux:before {
+  content: "\f17c"; }
+
+.fa-glide:before {
+  content: "\f2a5"; }
+
+.fa-linkedin:before {
+  content: "\f08c"; }
+
+.fa-hubspot:before {
+  content: "\f3b2"; }
+
+.fa-deploydog:before {
+  content: "\f38e"; }
+
+.fa-twitch:before {
+  content: "\f1e8"; }
+
+.fa-ravelry:before {
+  content: "\f2d9"; }
+
+.fa-mixer:before {
+  content: "\e056"; }
+
+.fa-square-lastfm:before {
+  content: "\f203"; }
+
+.fa-lastfm-square:before {
+  content: "\f203"; }
+
+.fa-vimeo:before {
+  content: "\f40a"; }
+
+.fa-mendeley:before {
+  content: "\f7b3"; }
+
+.fa-uniregistry:before {
+  content: "\f404"; }
+
+.fa-figma:before {
+  content: "\f799"; }
+
+.fa-creative-commons-remix:before {
+  content: "\f4ee"; }
+
+.fa-cc-amazon-pay:before {
+  content: "\f42d"; }
+
+.fa-dropbox:before {
+  content: "\f16b"; }
+
+.fa-instagram:before {
+  content: "\f16d"; }
+
+.fa-cmplid:before {
+  content: "\e360"; }
+
+.fa-facebook:before {
+  content: "\f09a"; }
+
+.fa-gripfire:before {
+  content: "\f3ac"; }
+
+.fa-jedi-order:before {
+  content: "\f50e"; }
+
+.fa-uikit:before {
+  content: "\f403"; }
+
+.fa-fort-awesome-alt:before {
+  content: "\f3a3"; }
+
+.fa-phabricator:before {
+  content: "\f3db"; }
+
+.fa-ussunnah:before {
+  content: "\f407"; }
+
+.fa-earlybirds:before {
+  content: "\f39a"; }
+
+.fa-trade-federation:before {
+  content: "\f513"; }
+
+.fa-autoprefixer:before {
+  content: "\f41c"; }
+
+.fa-whatsapp:before {
+  content: "\f232"; }
+
+.fa-slideshare:before {
+  content: "\f1e7"; }
+
+.fa-google-play:before {
+  content: "\f3ab"; }
+
+.fa-viadeo:before {
+  content: "\f2a9"; }
+
+.fa-line:before {
+  content: "\f3c0"; }
+
+.fa-google-drive:before {
+  content: "\f3aa"; }
+
+.fa-servicestack:before {
+  content: "\f3ec"; }
+
+.fa-simplybuilt:before {
+  content: "\f215"; }
+
+.fa-bitbucket:before {
+  content: "\f171"; }
+
+.fa-imdb:before {
+  content: "\f2d8"; }
+
+.fa-deezer:before {
+  content: "\e077"; }
+
+.fa-raspberry-pi:before {
+  content: "\f7bb"; }
+
+.fa-jira:before {
+  content: "\f7b1"; }
+
+.fa-docker:before {
+  content: "\f395"; }
+
+.fa-screenpal:before {
+  content: "\e570"; }
+
+.fa-bluetooth:before {
+  content: "\f293"; }
+
+.fa-gitter:before {
+  content: "\f426"; }
+
+.fa-d-and-d:before {
+  content: "\f38d"; }
+
+.fa-microblog:before {
+  content: "\e01a"; }
+
+.fa-cc-diners-club:before {
+  content: "\f24c"; }
+
+.fa-gg-circle:before {
+  content: "\f261"; }
+
+.fa-pied-piper-hat:before {
+  content: "\f4e5"; }
+
+.fa-kickstarter-k:before {
+  content: "\f3bc"; }
+
+.fa-yandex:before {
+  content: "\f413"; }
+
+.fa-readme:before {
+  content: "\f4d5"; }
+
+.fa-html5:before {
+  content: "\f13b"; }
+
+.fa-sellsy:before {
+  content: "\f213"; }
+
+.fa-sass:before {
+  content: "\f41e"; }
+
+.fa-wirsindhandwerk:before {
+  content: "\e2d0"; }
+
+.fa-wsh:before {
+  content: "\e2d0"; }
+
+.fa-buromobelexperte:before {
+  content: "\f37f"; }
+
+.fa-salesforce:before {
+  content: "\f83b"; }
+
+.fa-octopus-deploy:before {
+  content: "\e082"; }
+
+.fa-medapps:before {
+  content: "\f3c6"; }
+
+.fa-ns8:before {
+  content: "\f3d5"; }
+
+.fa-pinterest-p:before {
+  content: "\f231"; }
+
+.fa-apper:before {
+  content: "\f371"; }
+
+.fa-fort-awesome:before {
+  content: "\f286"; }
+
+.fa-waze:before {
+  content: "\f83f"; }
+
+.fa-cc-jcb:before {
+  content: "\f24b"; }
+
+.fa-snapchat:before {
+  content: "\f2ab"; }
+
+.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa-fantasy-flight-games:before {
+  content: "\f6dc"; }
+
+.fa-rust:before {
+  content: "\e07a"; }
+
+.fa-wix:before {
+  content: "\f5cf"; }
+
+.fa-square-behance:before {
+  content: "\f1b5"; }
+
+.fa-behance-square:before {
+  content: "\f1b5"; }
+
+.fa-supple:before {
+  content: "\f3f9"; }
+
+.fa-rebel:before {
+  content: "\f1d0"; }
+
+.fa-css3:before {
+  content: "\f13c"; }
+
+.fa-staylinked:before {
+  content: "\f3f5"; }
+
+.fa-kaggle:before {
+  content: "\f5fa"; }
+
+.fa-space-awesome:before {
+  content: "\e5ac"; }
+
+.fa-deviantart:before {
+  content: "\f1bd"; }
+
+.fa-cpanel:before {
+  content: "\f388"; }
+
+.fa-goodreads-g:before {
+  content: "\f3a9"; }
+
+.fa-square-git:before {
+  content: "\f1d2"; }
+
+.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa-square-tumblr:before {
+  content: "\f174"; }
+
+.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa-trello:before {
+  content: "\f181"; }
+
+.fa-creative-commons-nc-jp:before {
+  content: "\f4ea"; }
+
+.fa-get-pocket:before {
+  content: "\f265"; }
+
+.fa-perbyte:before {
+  content: "\e083"; }
+
+.fa-grunt:before {
+  content: "\f3ad"; }
+
+.fa-weebly:before {
+  content: "\f5cc"; }
+
+.fa-connectdevelop:before {
+  content: "\f20e"; }
+
+.fa-leanpub:before {
+  content: "\f212"; }
+
+.fa-black-tie:before {
+  content: "\f27e"; }
+
+.fa-themeco:before {
+  content: "\f5c6"; }
+
+.fa-python:before {
+  content: "\f3e2"; }
+
+.fa-android:before {
+  content: "\f17b"; }
+
+.fa-bots:before {
+  content: "\e340"; }
+
+.fa-free-code-camp:before {
+  content: "\f2c5"; }
+
+.fa-hornbill:before {
+  content: "\f592"; }
+
+.fa-js:before {
+  content: "\f3b8"; }
+
+.fa-ideal:before {
+  content: "\e013"; }
+
+.fa-git:before {
+  content: "\f1d3"; }
+
+.fa-dev:before {
+  content: "\f6cc"; }
+
+.fa-sketch:before {
+  content: "\f7c6"; }
+
+.fa-yandex-international:before {
+  content: "\f414"; }
+
+.fa-cc-amex:before {
+  content: "\f1f3"; }
+
+.fa-uber:before {
+  content: "\f402"; }
+
+.fa-github:before {
+  content: "\f09b"; }
+
+.fa-php:before {
+  content: "\f457"; }
+
+.fa-alipay:before {
+  content: "\f642"; }
+
+.fa-youtube:before {
+  content: "\f167"; }
+
+.fa-skyatlas:before {
+  content: "\f216"; }
+
+.fa-firefox-browser:before {
+  content: "\e007"; }
+
+.fa-replyd:before {
+  content: "\f3e6"; }
+
+.fa-suse:before {
+  content: "\f7d6"; }
+
+.fa-jenkins:before {
+  content: "\f3b6"; }
+
+.fa-twitter:before {
+  content: "\f099"; }
+
+.fa-rockrms:before {
+  content: "\f3e9"; }
+
+.fa-pinterest:before {
+  content: "\f0d2"; }
+
+.fa-buffer:before {
+  content: "\f837"; }
+
+.fa-npm:before {
+  content: "\f3d4"; }
+
+.fa-yammer:before {
+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/docs/deps/font-awesome-6.4.2/css/all.min.css b/docs/deps/font-awesome-6.4.2/css/all.min.css
new file mode 100644
index 0000000..6604a06
--- /dev/null
+++ b/docs/deps/font-awesome-6.4.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 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diff --git a/docs/deps/font-awesome-6.4.2/css/v4-shims.css b/docs/deps/font-awesome-6.4.2/css/v4-shims.css
new file mode 100644
index 0000000..a85953d
--- /dev/null
+++ b/docs/deps/font-awesome-6.4.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
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+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f15b"; }
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+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
+
+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
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+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
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+.fa.fa-video-camera:before {
+  content: "\f03d"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f03e"; }
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+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f03e"; }
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+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-image:before {
+  content: "\f03e"; }
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+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f044"; }
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+.fa.fa-edit {
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+  font-weight: 400; }
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+.fa.fa-edit:before {
+  content: "\f044"; }
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+.fa.fa-share-square-o:before {
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
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+.fa.fa-arrows:before {
+  content: "\f0b2"; }
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+.fa.fa-times-circle-o {
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+  font-weight: 400; }
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+.fa.fa-times-circle-o:before {
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+  font-weight: 400; }
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+.fa.fa-check-circle-o:before {
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+.fa.fa-compress:before {
+  content: "\f422"; }
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+.fa.fa-eye {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-eye-slash {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-warning:before {
+  content: "\f071"; }
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+.fa.fa-calendar:before {
+  content: "\f073"; }
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+  content: "\f338"; }
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+  content: "\f337"; }
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+.fa.fa-bar-chart:before {
+  content: "\e0e3"; }
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+.fa.fa-bar-chart-o:before {
+  content: "\e0e3"; }
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+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
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+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/docs/deps/font-awesome-6.4.2/css/v4-shims.min.css b/docs/deps/font-awesome-6.4.2/css/v4-shims.min.css
new file mode 100644
index 0000000..64e4e8d
--- /dev/null
+++ b/docs/deps/font-awesome-6.4.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 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diff --git a/docs/deps/headroom-0.11.0/headroom.min.js b/docs/deps/headroom-0.11.0/headroom.min.js
new file mode 100644
index 0000000..433069f
--- /dev/null
+++ b/docs/deps/headroom-0.11.0/headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.11.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset,a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o});
\ No newline at end of file
diff --git a/docs/deps/headroom-0.11.0/jQuery.headroom.min.js b/docs/deps/headroom-0.11.0/jQuery.headroom.min.js
new file mode 100644
index 0000000..17f70c9
--- /dev/null
+++ b/docs/deps/headroom-0.11.0/jQuery.headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery);
\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/autocomplete.jquery.min.js b/docs/deps/search-1.0.0/autocomplete.jquery.min.js
new file mode 100644
index 0000000..fb63a6a
--- /dev/null
+++ b/docs/deps/search-1.0.0/autocomplete.jquery.min.js
@@ -0,0 +1,7 @@
+/*!
+ * autocomplete.js 0.38.0
+ * https://github.com/algolia/autocomplete.js
+ * Copyright 2020 Algolia, Inc. and other contributors; Licensed MIT
+ */
+!function(a){function b(d){if(c[d])return c[d].exports;var e=c[d]={exports:{},id:d,loaded:!1};return a[d].call(e.exports,e,e.exports,b),e.loaded=!0,e.exports}var c={};b.m=a,b.c=c,b.p="",b(0)}([function(a,b,c){"use strict";a.exports=c(1)},function(a,b,c){"use strict";var d=c(2),e=c(3);d.element=e;var f=c(4);f.isArray=e.isArray,f.isFunction=e.isFunction,f.isObject=e.isPlainObject,f.bind=e.proxy,f.each=function(a,b){function c(a,c){return b(c,a)}e.each(a,c)},f.map=e.map,f.mixin=e.extend,f.Event=e.Event;var g,h,i,j=c(5),k=c(6);g=e.fn.autocomplete,h="aaAutocomplete",i={initialize:function(a,b){function c(){var c,d=e(this),f=new k({el:d});c=new j({input:d,eventBus:f,dropdownMenuContainer:a.dropdownMenuContainer,hint:void 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a=this.input.getQuery();this.dropdown.isEmpty&&a.length>=this.minLength?this.dropdown.update(a):this.dropdown.moveCursorDown(),this.dropdown.open()},_onLeftKeyed:function(){"rtl"===this.dir&&this._autocomplete()},_onRightKeyed:function(){"ltr"===this.dir&&this._autocomplete()},_onQueryChanged:function(a,b){this.input.clearHintIfInvalid(),b.length>=this.minLength?this.dropdown.update(b):this.dropdown.empty(),this.dropdown.open(),this._setLanguageDirection()},_onWhitespaceChanged:function(){this._updateHint(),this.dropdown.open()},_setLanguageDirection:function(){var a=this.input.getLanguageDirection();this.dir!==a&&(this.dir=a,this.$node.css("direction",a),this.dropdown.setLanguageDirection(a))},_updateHint:function(){var a,b,c,d,e,f;a=this.dropdown.getDatumForTopSuggestion(),a&&this.dropdown.isVisible()&&!this.input.hasOverflow()?(b=this.input.getInputValue(),c=l.normalizeQuery(b),d=i.escapeRegExChars(c),e=new 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+var c=[].slice.call(arguments,1);this.cacheSuggestions(a,b,c),this._render.apply(this,[a,b].concat(c))}}if(this.query=a,this.canceled=!1,this.shouldFetchFromCache(a))b.apply(this,[this.cachedSuggestions].concat(this.cachedRenderExtraArgs));else{var c=this,d=function(){c.canceled||c.source(a,b.bind(c))};if(this.debounce){var e=function(){c.debounceTimeout=null,d()};clearTimeout(this.debounceTimeout),this.debounceTimeout=setTimeout(e,this.debounce)}else d()}},cacheSuggestions:function(a,b,c){this.cachedQuery=a,this.cachedSuggestions=b,this.cachedRenderExtraArgs=c},shouldFetchFromCache:function(a){return this.cache&&this.cachedQuery===a&&this.cachedSuggestions&&this.cachedSuggestions.length},clearCachedSuggestions:function(){delete this.cachedQuery,delete this.cachedSuggestions,delete this.cachedRenderExtraArgs},cancel:function(){this.canceled=!0},clear:function(){this.$el&&(this.cancel(),this.$el.empty(),this.trigger("rendered",""))},isEmpty:function(){return this._isEmpty},destroy:function(){this.clearCachedSuggestions(),this.$el=null}}),a.exports=d},function(a,b){"use strict";a.exports={wrapper:'<span class="%ROOT%"></span>',dropdown:'<span class="%PREFIX%%DROPDOWN_MENU%"></span>',dataset:'<div class="%PREFIX%%DATASET%-%CLASS%"></div>',suggestions:'<span class="%PREFIX%%SUGGESTIONS%"></span>',suggestion:'<div class="%PREFIX%%SUGGESTION%"></div>'}},function(a,b,c){"use strict";var d=c(4),e={wrapper:{position:"relative",display:"inline-block"},hint:{position:"absolute",top:"0",left:"0",borderColor:"transparent",boxShadow:"none",opacity:"1"},input:{position:"relative",verticalAlign:"top",backgroundColor:"transparent"},inputWithNoHint:{position:"relative",verticalAlign:"top"},dropdown:{position:"absolute",top:"100%",left:"0",zIndex:"100",display:"none"},suggestions:{display:"block"},suggestion:{whiteSpace:"nowrap",cursor:"pointer"},suggestionChild:{whiteSpace:"normal"},ltr:{left:"0",right:"auto"},rtl:{left:"auto",right:"0"},defaultClasses:{root:"algolia-autocomplete",prefix:"aa",noPrefix:!1,dropdownMenu:"dropdown-menu",input:"input",hint:"hint",suggestions:"suggestions",suggestion:"suggestion",cursor:"cursor",dataset:"dataset",empty:"empty"},appendTo:{wrapper:{position:"absolute",zIndex:"100",display:"none"},input:{},inputWithNoHint:{},dropdown:{display:"block"}}};d.isMsie()&&d.mixin(e.input,{backgroundImage:"url(data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)"}),d.isMsie()&&d.isMsie()<=7&&d.mixin(e.input,{marginTop:"-1px"}),a.exports=e},function(a,b,c){"use strict";a.exports={hits:c(21),popularIn:c(24)}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23),g=function(){function a(a,d){return window.Promise.resolve().then(function(){return b.length&&(e=a.search(b),b=[]),e}).then(function(a){if(a)return c=a.results,c[d]})}var b=[],c=[],e=window.Promise.resolve();return function(c,e){return function(f,g){a(c.as,b.push({indexName:c.indexName,query:f,params:e})-1).then(function(a){a&&g(a.hits,a)}).catch(function(a){d.error(a.message)})}}}();a.exports=function(a,b){var c=f(a.as._ua);if(c&&c[0]>=3&&c[1]>20){var d="autocomplete.js "+e;a.as._ua.indexOf(d)===-1&&(a.as._ua+="; "+d)}return g(a,b)}},function(a,b){a.exports="0.38.0"},function(a,b){"use strict";a.exports=function(a){var b=a.match(/Algolia for JavaScript \((\d+\.)(\d+\.)(\d+)\)/)||a.match(/Algolia for vanilla JavaScript (\d+\.)(\d+\.)(\d+)/);if(b)return[b[1],b[2],b[3]]}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23);a.exports=function(a,b,c,g){function h(h,i){a.search(h,b,function(a,h){if(a)return void d.error(a.message);if(h.hits.length>0){var l=h.hits[0],m=d.mixin({hitsPerPage:0},c);delete m.source,delete m.index;var n=f(k.as._ua);return n&&n[0]>=3&&n[1]>20&&(b.additionalUA="autocomplete.js "+e),void k.search(j(l),m,function(a,b){if(a)return void d.error(a.message);var c=[];if(g.includeAll){var e=g.allTitle||"All departments";c.push(d.mixin({facet:{value:e,count:b.nbHits}},d.cloneDeep(l)))}d.each(b.facets,function(a,b){d.each(a,function(a,e){c.push(d.mixin({facet:{facet:b,value:e,count:a}},d.cloneDeep(l)))})});for(var f=1;f<h.hits.length;++f)c.push(h.hits[f]);i(c,h)})}i([])})}var i=f(a.as._ua);if(i&&i[0]>=3&&i[1]>20&&(b=b||{},b.additionalUA="autocomplete.js "+e),!c.source)return d.error("Missing 'source' key");var j=d.isFunction(c.source)?c.source:function(a){return a[c.source]};if(!c.index)return d.error("Missing 'index' key");var k=c.index;return g=g||{},h}}]);
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diff --git a/docs/deps/search-1.0.0/fuse.min.js b/docs/deps/search-1.0.0/fuse.min.js
new file mode 100644
index 0000000..7def598
--- /dev/null
+++ b/docs/deps/search-1.0.0/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.4.6 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function n(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}function r(e,t,r){return t&&n(e.prototype,t),r&&n(e,r),e}function i(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var 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diff --git a/docs/deps/search-1.0.0/mark.min.js b/docs/deps/search-1.0.0/mark.min.js
new file mode 100644
index 0000000..1eea053
--- /dev/null
+++ b/docs/deps/search-1.0.0/mark.min.js
@@ -0,0 +1,7 @@
+/*!***************************************************
+* mark.js v8.11.1
+* https://markjs.io/
+* Copyright (c) 2014–2018, Julian Kühnel
+* Released under the MIT license https://git.io/vwTVl
+*****************************************************/
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diff --git a/docs/index.html b/docs/index.html
index 5caf680..8763192 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -5,71 +5,49 @@
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-<meta name="description" content="Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.">
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-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Density Surface Modelling of Distance Sampling Data">
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
+<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Density Surface Modelling of Distance Sampling Data">
+<meta name="description" content="Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.">
 <meta property="og:description" content="Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
       <ul class="navbar-nav me-auto">
-<li class="nav-item">
-  <a class="nav-link" href="reference/index.html">Reference</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="news/index.html">Changelog</a>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul>
 </li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-home">
 <div class="row">
@@ -135,15 +113,15 @@ <h2 data-toc-skip>Developers</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/docs/katex-auto.js b/docs/katex-auto.js
new file mode 100644
index 0000000..20651d9
--- /dev/null
+++ b/docs/katex-auto.js
@@ -0,0 +1,14 @@
+// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345
+document.addEventListener("DOMContentLoaded", function () {
+  var mathElements = document.getElementsByClassName("math");
+  var macros = [];
+  for (var i = 0; i < mathElements.length; i++) {
+    var texText = mathElements[i].firstChild;
+    if (mathElements[i].tagName == "SPAN") {
+      katex.render(texText.data, mathElements[i], {
+        displayMode: mathElements[i].classList.contains("display"),
+        throwOnError: false,
+        macros: macros,
+        fleqn: false
+      });
+    }}});
diff --git a/docs/lightswitch.js b/docs/lightswitch.js
new file mode 100644
index 0000000..9467125
--- /dev/null
+++ b/docs/lightswitch.js
@@ -0,0 +1,85 @@
+
+/*!
+ * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under the Creative Commons Attribution 3.0 Unported License.
+ * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0.
+ */
+
+const getStoredTheme = () => localStorage.getItem('theme')
+const setStoredTheme = theme => localStorage.setItem('theme', theme)
+
+const getPreferredTheme = () => {
+  const storedTheme = getStoredTheme()
+  if (storedTheme) {
+    return storedTheme
+  }
+
+  return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'
+}
+
+const setTheme = theme => {
+  if (theme === 'auto') {
+    document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'))
+  } else {
+    document.documentElement.setAttribute('data-bs-theme', theme)
+  }
+}
+
+function bsSetupThemeToggle () {
+  'use strict'
+
+  const showActiveTheme = (theme, focus = false) => {
+    var activeLabel, activeIcon;
+
+    document.querySelectorAll('[data-bs-theme-value]').forEach(element => {
+      const buttonTheme = element.getAttribute('data-bs-theme-value')
+      const isActive = buttonTheme == theme
+      
+      element.classList.toggle('active', isActive)
+      element.setAttribute('aria-pressed', isActive)
+
+      if (isActive) {
+        activeLabel = element.textContent;
+        activeIcon = element.querySelector('span').classList.value;
+      }
+    })
+
+    const themeSwitcher = document.querySelector('#dropdown-lightswitch')
+    if (!themeSwitcher) {
+      return
+    }
+
+    themeSwitcher.setAttribute('aria-label', activeLabel)
+    themeSwitcher.querySelector('span').classList.value = activeIcon;
+
+    if (focus) {
+      themeSwitcher.focus()
+    }
+  }
+
+  window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => {
+    const storedTheme = getStoredTheme()
+    if (storedTheme !== 'light' && storedTheme !== 'dark') {
+      setTheme(getPreferredTheme())
+    }
+  })
+
+  window.addEventListener('DOMContentLoaded', () => {
+    showActiveTheme(getPreferredTheme())
+
+    document
+      .querySelectorAll('[data-bs-theme-value]')
+      .forEach(toggle => {
+        toggle.addEventListener('click', () => {
+          const theme = toggle.getAttribute('data-bs-theme-value')
+          setTheme(theme)
+          setStoredTheme(theme)
+          showActiveTheme(theme, true)
+        })
+      })
+  })
+}
+
+setTheme(getPreferredTheme());
+bsSetupThemeToggle();
diff --git a/docs/news/index.html b/docs/news/index.html
index ed1e840..28deef2 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="active nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-news">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Changelog</h1>
+
+      <h1>Changelog</h1>
       <small>Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
@@ -233,7 +212,7 @@ <h2 class="pkg-version" data-toc-text="2.0.5" id="dsm-205">dsm 2.0.5<a class="an
 <li>silly typo in docs</li>
 <li>check if we’re using mgcv version &gt;= 1.7-24 for GAMM fitting and warn if not</li>
 </ul></div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -242,14 +221,14 @@ <h2 class="pkg-version" data-toc-text="2.0.5" id="dsm-205">dsm 2.0.5<a class="an
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index 9bd6621..1a99c65 100644
--- a/docs/pkgdown.js
+++ b/docs/pkgdown.js
@@ -153,4 +153,10 @@ async function searchFuse(query, callback) {
   });
 })(window.jQuery || window.$)
 
-
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
+    event.preventDefault();  // Prevent any default action associated with the '/' key
+    document.getElementById('search-input').focus();  // Set focus to the search input
+  }
+});
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index c5e42bd..3b8b19b 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,8 +1,7 @@
-pandoc: 3.1.11
-pkgdown: 2.0.9
+pandoc: '3.2'
+pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
-  dsm-data-formatting: data_format/dsm-data-formatting.html
-  mexico-analysis: lines_gomex/mexico-analysis.html
-last_built: 2024-05-13T07:13Z
-
+  data_format/dsm-data-formatting: data_format/dsm-data-formatting.html
+  lines_gomex/mexico-analysis: lines_gomex/mexico-analysis.html
+last_built: 2024-11-13T16:30Z
diff --git a/docs/reference/block.info.per.su.html b/docs/reference/block.info.per.su.html
index 5686639..9aec416 100644
--- a/docs/reference/block.info.per.su.html
+++ b/docs/reference/block.info.per.su.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the transect data and works out how many blocks of a given
-size (in segment terms) fit into each."><title>Find the block information — block.info.per.su • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Find the block information — block.info.per.su"><meta property="og:description" content="Takes the transect data and works out how many blocks of a given
-size (in segment terms) fit into each."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Find the block information — block.info.per.su • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Find the block information — block.info.per.su"><meta name="description" content="Takes the transect data and works out how many blocks of a given
+size (in segment terms) fit into each."><meta property="og:description" content="Takes the transect data and works out how many blocks of a given
+size (in segment terms) fit into each."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Find the block information</h1>
+
+      <h1>Find the block information</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/block.info.per.su.R" class="external-link"><code>R/block.info.per.su.R</code></a></small>
       <div class="d-none name"><code>block.info.per.su.Rd</code></div>
     </div>
@@ -72,23 +51,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>block.size</dt>
+
+
+<dl><dt id="arg-block-size">block.size<a class="anchor" aria-label="anchor" href="#arg-block-size"></a></dt>
 <dd><p>number of segments per block</p></dd>
 
 
-<dt>data</dt>
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>data used to build the model</p></dd>
 
 
-<dt>name.su</dt>
+<dt id="arg-name-su">name.su<a class="anchor" aria-label="anchor" href="#arg-name-su"></a></dt>
 <dd><p>names of the sampling units (i.e., transects)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a <code>data.frame</code> with the following columns</p><ul><li><p><code>name</code>        the sample unit name (e.g. transect label)</p></li>
+    <p>a <code>data.frame</code> with the following columns</p><ul><li><p><code>name</code>        the sample unit name (e.g. transect label)</p></li>
 <li><p><code>num.seg</code>     number of segments in that transect</p></li>
 <li><p><code>num.block</code>   number of blocks available</p></li>
 <li><p><code>start.block</code> block number for first block</p></li>
@@ -96,7 +75,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <li><p><code>num.req</code>     number of blocks needed for the unit</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -105,14 +84,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/check.cols.html b/docs/reference/check.cols.html
index 43558ec..906dd36 100644
--- a/docs/reference/check.cols.html
+++ b/docs/reference/check.cols.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Internal function to check that supplied data.frames have the correct
-columns and checks that sample labels are all unique."><title>Check column names exist — check.cols • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check column names exist — check.cols"><meta property="og:description" content="Internal function to check that supplied data.frames have the correct
-columns and checks that sample labels are all unique."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check column names exist — check.cols • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check column names exist — check.cols"><meta name="description" content="Internal function to check that supplied data.frames have the correct
+columns and checks that sample labels are all unique."><meta property="og:description" content="Internal function to check that supplied data.frames have the correct
+columns and checks that sample labels are all unique."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
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-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Check column names exist</h1>
+
+      <h1>Check column names exist</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/check.cols.R" class="external-link"><code>R/check.cols.R</code></a></small>
       <div class="d-none name"><code>check.cols.Rd</code></div>
     </div>
@@ -72,34 +51,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ddf.obj</dt>
+
+
+<dl><dt id="arg-ddf-obj">ddf.obj<a class="anchor" aria-label="anchor" href="#arg-ddf-obj"></a></dt>
 <dd><p>a <code>ddf</code> object from <code>mrds</code></p></dd>
 
 
-<dt>segment.data</dt>
+<dt id="arg-segment-data">segment.data<a class="anchor" aria-label="anchor" href="#arg-segment-data"></a></dt>
 <dd><p>segment data as defined in <code><a href="dsm.html">dsm</a></code></p></dd>
 
 
-<dt>observation.data</dt>
+<dt id="arg-observation-data">observation.data<a class="anchor" aria-label="anchor" href="#arg-observation-data"></a></dt>
 <dd><p>observation data as defined in <code><a href="dsm.html">dsm</a></code></p></dd>
 
 
-<dt>segment.area</dt>
+<dt id="arg-segment-area">segment.area<a class="anchor" aria-label="anchor" href="#arg-segment-area"></a></dt>
 <dd><p>area of segments</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>nothing, but throws an error if something went wrong</p>
+    <p>nothing, but throws an error if something went wrong</p>
     </div>
     <div class="section level2">
     <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>David Lawrence Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -108,14 +87,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm-data.html b/docs/reference/dsm-data.html
index c1ec2e2..f4207dc 100644
--- a/docs/reference/dsm-data.html
+++ b/docs/reference/dsm-data.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Two data.frames must be provided to dsm. They are referred to as
-observation.data and segment.data."><title>Data format for DSM — dsm-data • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data format for DSM — dsm-data"><meta property="og:description" content="Two data.frames must be provided to dsm. They are referred to as
-observation.data and segment.data."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Data format for DSM — dsm-data • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data format for DSM — dsm-data"><meta name="description" content="Two data.frames must be provided to dsm. They are referred to as
+observation.data and segment.data."><meta property="og:description" content="Two data.frames must be provided to dsm. They are referred to as
+observation.data and segment.data."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
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-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Data format for DSM</h1>
+
+      <h1>Data format for DSM</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm-package.R" class="external-link"><code>R/dsm-package.R</code></a></small>
       <div class="d-none name"><code>dsm-data.Rd</code></div>
     </div>
@@ -93,7 +72,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="multiple-detection-functions">Multiple detection functions<a class="anchor" aria-label="anchor" href="#multiple-detection-functions"></a></h2>
-    
+
 
 
 <p>If multiple detection functions are to be used, then a column named <code>ddfobj</code>
@@ -105,7 +84,7 @@ <h2 id="multiple-detection-functions">Multiple detection functions<a class="anch
     </div>
     <div class="section level2">
     <h2 id="mark-recapture-distance-sampling-models">Mark-recapture distance sampling models<a class="anchor" aria-label="anchor" href="#mark-recapture-distance-sampling-models"></a></h2>
-    
+
 
 
 <p>When using <code>mrds</code> models that include mark-recapture components (currently
@@ -117,7 +96,7 @@ <h2 id="mark-recapture-distance-sampling-models">Mark-recapture distance samplin
 <li><p><em>Trial mode</em> only observations made by observer 1 are required.</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -126,14 +105,14 @@ <h2 id="mark-recapture-distance-sampling-models">Mark-recapture distance samplin
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm-package.html b/docs/reference/dsm-package.html
index e0a3ee3..057a9a2 100644
--- a/docs/reference/dsm-package.html
+++ b/docs/reference/dsm-package.html
@@ -1,65 +1,44 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="dsm implements spatial models for distance sampling data. Models for
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Density surface modelling — dsm-package • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Density surface modelling — dsm-package"><meta name="description" content="dsm implements spatial models for distance sampling data. Models for
 detectability can be fitted using packages mrds or Distance. dsm fits
 generalized additive models to spatially-referenced data. See Miller et al
-(2013) for an introduction."><title>Density surface modelling — dsm-package • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Density surface modelling — dsm-package"><meta property="og:description" content="dsm implements spatial models for distance sampling data. Models for
+(2013) for an introduction."><meta property="og:description" content="dsm implements spatial models for distance sampling data. Models for
 detectability can be fitted using packages mrds or Distance. dsm fits
 generalized additive models to spatially-referenced data. See Miller et al
-(2013) for an introduction."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
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-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Density surface modelling</h1>
+
+      <h1>Density surface modelling</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm-package.R" class="external-link"><code>R/dsm-package.R</code></a></small>
       <div class="d-none name"><code>dsm-package.Rd</code></div>
     </div>
@@ -92,7 +71,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 CRC/Chapman &amp; Hall.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -101,14 +80,14 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm.cor.html b/docs/reference/dsm.cor.html
index dcfa216..f57f32d 100644
--- a/docs/reference/dsm.cor.html
+++ b/docs/reference/dsm.cor.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use dsm_cor."><title>Check for autocorrelation in residuals — dsm.cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm.cor"><meta property="og:description" content="This function is deprecated, use dsm_cor."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check for autocorrelation in residuals — dsm.cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm.cor"><meta name="description" content="This function is deprecated, use dsm_cor."><meta property="og:description" content="This function is deprecated, use dsm_cor."></head><body>
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-    
 
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Check for autocorrelation in residuals</h1>
+
+      <h1>Check for autocorrelation in residuals</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
       <div class="d-none name"><code>dsm.cor.Rd</code></div>
     </div>
@@ -79,51 +58,53 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.obj</dt>
+
+
+<dl><dt id="arg-dsm-obj">dsm.obj<a class="anchor" aria-label="anchor" href="#arg-dsm-obj"></a></dt>
 <dd><p>a fitted dsm object.</p></dd>
 
 
-<dt>Transect.Label</dt>
+<dt id="arg-transect-label">Transect.Label<a class="anchor" aria-label="anchor" href="#arg-transect-label"></a></dt>
 <dd><p>label for the transect (default: <code>Transect.Label</code>).
 Using different labels can be useful when transects are split over
 geographical features or when transects are surveyed multiple times.</p></dd>
 
 
-<dt>Segment.Label</dt>
+<dt id="arg-segment-label">Segment.Label<a class="anchor" aria-label="anchor" href="#arg-segment-label"></a></dt>
 <dd><p>label for the segments (default: <code>Segment.Label</code>).The
 result of calling <code><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></code> must make sense.</p></dd>
 
 
-<dt>max.lag</dt>
+<dt id="arg-max-lag">max.lag<a class="anchor" aria-label="anchor" href="#arg-max-lag"></a></dt>
 <dd><p>maximum lag to calculate at.</p></dd>
 
 
-<dt>resid.type</dt>
+<dt id="arg-resid-type">resid.type<a class="anchor" aria-label="anchor" href="#arg-resid-type"></a></dt>
 <dd><p>the type of residuals used, see
 <code>residuals.gam</code>. Defaults to <code>"scaled.pearson"</code> in the GAM
 case and <code>"normalized"</code> in the GAMM case (which are equivalent).</p></dd>
 
 
-<dt>fun</dt>
+<dt id="arg-fun">fun<a class="anchor" aria-label="anchor" href="#arg-fun"></a></dt>
 <dd><p>the function to use, by default <code><a href="https://rdrr.io/r/stats/cor.html" class="external-link">cor</a></code>, must take two
 column vectors as arguments.</p></dd>
 
 
-<dt>ylim</dt>
+<dt id="arg-ylim">ylim<a class="anchor" aria-label="anchor" href="#arg-ylim"></a></dt>
 <dd><p>user defined limits in y direction.</p></dd>
 
 
-<dt>subset</dt>
+<dt id="arg-subset">subset<a class="anchor" aria-label="anchor" href="#arg-subset"></a></dt>
 <dd><p>which subset of the data should the correlation function be
 calculated on?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>other options to pass to <code><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></code>.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -132,14 +113,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm.html b/docs/reference/dsm.html
index 10f0ced..57ad4d6 100644
--- a/docs/reference/dsm.html
+++ b/docs/reference/dsm.html
@@ -1,71 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fits a density surface model (DSM) to detection adjusted counts from a
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fit a density surface model to segment-specific estimates of abundance or density. — dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit a density surface model to segment-specific estimates of abundance or density. — dsm"><meta name="description" content="Fits a density surface model (DSM) to detection adjusted counts from a
 spatially-referenced distance sampling analysis. dsm takes observations of
 animals, allocates them to segments of line (or strip transects) and
 optionally adjusts the counts based on detectability using a supplied
 detection function model. A generalized additive model, generalized mixed
 model or generalized linear model is then used to model these adjusted
-counts based on a formula involving environmental covariates."><title>Fit a density surface model to segment-specific estimates of abundance or density. — dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit a density surface model to segment-specific estimates of abundance or density. — dsm"><meta property="og:description" content="Fits a density surface model (DSM) to detection adjusted counts from a
+counts based on a formula involving environmental covariates."><meta property="og:description" content="Fits a density surface model (DSM) to detection adjusted counts from a
 spatially-referenced distance sampling analysis. dsm takes observations of
 animals, allocates them to segments of line (or strip transects) and
 optionally adjusts the counts based on detectability using a supplied
 detection function model. A generalized additive model, generalized mixed
 model or generalized linear model is then used to model these adjusted
-counts based on a formula involving environmental covariates."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+counts based on a formula involving environmental covariates."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-    <span class="fab fa fab fa-github fa-lg"></span>
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Fit a density surface model to segment-specific estimates of abundance or density.</h1>
+
+      <h1>Fit a density surface model to segment-specific estimates of abundance or density.</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm.R" class="external-link"><code>R/dsm.R</code></a></small>
       <div class="d-none name"><code>dsm.Rd</code></div>
     </div>
@@ -102,12 +81,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>formula</dt>
+
+
+<dl><dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>formula for the surface. This should be a valid formula. See
 "Details", below, for how to define the response.</p></dd>
 
 
-<dt>ddf.obj</dt>
+<dt id="arg-ddf-obj">ddf.obj<a class="anchor" aria-label="anchor" href="#arg-ddf-obj"></a></dt>
 <dd><p>result from call to <code><a href="https://rdrr.io/pkg/mrds/man/ddf.html" class="external-link">ddf</a></code> or
 <code><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></code>. If multiple detection functions are required a <code>list</code>
 can be provided. For strip/circle transects where it is assumed all objects
@@ -116,44 +97,44 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 allowed.</p></dd>
 
 
-<dt>segment.data</dt>
+<dt id="arg-segment-data">segment.data<a class="anchor" aria-label="anchor" href="#arg-segment-data"></a></dt>
 <dd><p>segment data, see <code><a href="dsm-data.html">dsm-data</a></code>.</p></dd>
 
 
-<dt>observation.data</dt>
+<dt id="arg-observation-data">observation.data<a class="anchor" aria-label="anchor" href="#arg-observation-data"></a></dt>
 <dd><p>observation data, see <code><a href="dsm-data.html">dsm-data</a></code>.</p></dd>
 
 
-<dt>engine</dt>
+<dt id="arg-engine">engine<a class="anchor" aria-label="anchor" href="#arg-engine"></a></dt>
 <dd><p>which fitting engine should be used for the DSM
 (<code>"glm"</code>/<code>"gam"</code>/<code>"gamm"</code>/<code>"bam"</code>).</p></dd>
 
 
-<dt>convert.units</dt>
+<dt id="arg-convert-units">convert.units<a class="anchor" aria-label="anchor" href="#arg-convert-units"></a></dt>
 <dd><p>conversion factor to multiply the area of the segments
 by. See 'Units' below.</p></dd>
 
 
-<dt>family</dt>
+<dt id="arg-family">family<a class="anchor" aria-label="anchor" href="#arg-family"></a></dt>
 <dd><p>response distribution (popular choices include
 <code><a href="https://rdrr.io/r/stats/family.html" class="external-link">quasipoisson</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">Tweedie</a></code>/<code><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></code>
 and <code><a href="https://rdrr.io/pkg/mgcv/man/negbin.html" class="external-link">negbin</a></code>/<code><a href="https://rdrr.io/pkg/mgcv/man/negbin.html" class="external-link">nb</a></code>). Defaults
 <code><a href="https://rdrr.io/r/stats/family.html" class="external-link">quasipoisson</a></code>.</p></dd>
 
 
-<dt>group</dt>
+<dt id="arg-group">group<a class="anchor" aria-label="anchor" href="#arg-group"></a></dt>
 <dd><p>if <code>TRUE</code> the abundance of <em>groups</em> will be calculated rather
 than the abundance of <em>individuals</em>. Setting this option to <code>TRUE</code> is
 equivalent to setting the size of each group to be 1.</p></dd>
 
 
-<dt>control</dt>
+<dt id="arg-control">control<a class="anchor" aria-label="anchor" href="#arg-control"></a></dt>
 <dd><p>the usual <code>control</code> argument for a <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>;
 <code>keepData</code> must be <code>TRUE</code> for variance estimation to work (though this
 option cannot be set for GLMs or GAMMs).</p></dd>
 
 
-<dt>availability</dt>
+<dt id="arg-availability">availability<a class="anchor" aria-label="anchor" href="#arg-availability"></a></dt>
 <dd><p>an estimate of availability bias. For count models used
 to multiply the effective strip width (must be a vector of length 1 or
 length the number of rows in <code>segment.data</code>); for estimated
@@ -162,7 +143,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 Uncertainty in the availability is not handled at present.</p></dd>
 
 
-<dt>segment.area</dt>
+<dt id="arg-segment-area">segment.area<a class="anchor" aria-label="anchor" href="#arg-segment-area"></a></dt>
 <dd><p>if <code>NULL</code> (default) segment areas will be calculated by
 multiplying the <code>Effort</code> column in <code>segment.data</code> by the (right minus left)
 truncation distance for the <code>ddf.obj</code> or by <code>strip.width</code>. Alternatively a
@@ -172,29 +153,27 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 specified it takes precedent.</p></dd>
 
 
-<dt>weights</dt>
+<dt id="arg-weights">weights<a class="anchor" aria-label="anchor" href="#arg-weights"></a></dt>
 <dd><p>weights for each observation used in model fitting. The
 default, <code>weights=NULL</code>, weights each observation by its area (see Details).
 Setting a scalar value (e.g., <code>weights=1</code>) all observations are equally
 weighted.</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>The smoothing parameter estimation method. Default is
 <code>"REML"</code>, using Restricted Maximum Likelihood. See <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code> for
 other options. Ignored for <code>engine="glm"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>anything else to be passed straight to <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code>,
 <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/gamm.html" class="external-link">gamm</a></code> or <code><a href="https://rdrr.io/pkg/mgcv/man/bam.html" class="external-link">bam</a></code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/gamm.html" class="external-link">gamm</a></code> or
+    <p>a <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/pkg/mgcv/man/gamm.html" class="external-link">gamm</a></code> or
 <code><a href="https://rdrr.io/pkg/mgcv/man/bam.html" class="external-link">bam</a></code> object, with an additional element, <code>$ddf</code> which holds the
 detection function object.</p>
     </div>
@@ -226,7 +205,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="units">Units<a class="anchor" aria-label="anchor" href="#units"></a></h2>
-    
+
 
 
 <p>It is often the case that distances are collected in metres and segment
@@ -239,7 +218,7 @@ <h2 id="units">Units<a class="anchor" aria-label="anchor" href="#units"></a></h2
     </div>
     <div class="section level2">
     <h2 id="large-models">Large models<a class="anchor" aria-label="anchor" href="#large-models"></a></h2>
-    
+
 
 
 <p>For large models, <code>engine="bam"</code> with <code>method="fREML"</code> may be useful. Models
@@ -268,7 +247,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -292,10 +271,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># plot the smooth</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mod1</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -304,14 +283,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm.var.gam.html b/docs/reference/dsm.var.gam.html
index cd19698..d5f916d 100644
--- a/docs/reference/dsm.var.gam.html
+++ b/docs/reference/dsm.var.gam.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance estimation assuming independence — dsm.var.gam • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance estimation assuming independence — dsm.var.gam"><meta name="description" content="This function is deprecated, use dsm_var_gam."><meta property="og:description" content="This function is deprecated, use dsm_var_gam."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Prediction variance estimation assuming independence</h1>
+
+      <h1>Prediction variance estimation assuming independence</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
       <div class="d-none name"><code>dsm.var.gam.Rd</code></div>
     </div>
@@ -75,17 +54,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.obj</dt>
+
+
+<dl><dt id="arg-dsm-obj">dsm.obj<a class="anchor" aria-label="anchor" href="#arg-dsm-obj"></a></dt>
 <dd><p>a model object fitted by <code><a href="dsm.html">dsm</a></code>.</p></dd>
 
 
-<dt>pred.data</dt>
+<dt id="arg-pred-data">pred.data<a class="anchor" aria-label="anchor" href="#arg-pred-data"></a></dt>
 <dd><p>either: a single prediction grid or list of prediction
 grids. Each grid should be a <code>data.frame</code> with the same columns as the
 original data.</p></dd>
 
 
-<dt>off.set</dt>
+<dt id="arg-off-set">off.set<a class="anchor" aria-label="anchor" href="#arg-off-set"></a></dt>
 <dd><p>a a vector or list of vectors with as many elements as there
 are in <code>pred.data</code>. Each vector is as long as the number of rows in the
 corresponding element of <code>pred.data</code>. These give the area associated with
@@ -93,18 +74,18 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 for the length of <code>pred.data</code>.</p></dd>
 
 
-<dt>seglen.varname</dt>
+<dt id="arg-seglen-varname">seglen.varname<a class="anchor" aria-label="anchor" href="#arg-seglen-varname"></a></dt>
 <dd><p>name for the column which holds the segment length
 (default value <code>"Effort"</code>).</p></dd>
 
 
-<dt>type.pred</dt>
+<dt id="arg-type-pred">type.pred<a class="anchor" aria-label="anchor" href="#arg-type-pred"></a></dt>
 <dd><p>should the predictions be on the "response" or "link"
 scale? (default <code>"response"</code>).</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -113,14 +94,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm.var.movblk.html b/docs/reference/dsm.var.movblk.html
index 0f12548..767ade9 100644
--- a/docs/reference/dsm.var.movblk.html
+++ b/docs/reference/dsm.var.movblk.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use dsm_var_movblk."><title>Variance estimation via parametric moving block bootstrap — dsm.var.movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm.var.movblk"><meta property="og:description" content="This function is deprecated, use dsm_var_movblk."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance estimation via parametric moving block bootstrap — dsm.var.movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm.var.movblk"><meta name="description" content="This function is deprecated, use dsm_var_movblk."><meta property="og:description" content="This function is deprecated, use dsm_var_movblk."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Variance estimation via parametric moving block bootstrap</h1>
+
+      <h1>Variance estimation via parametric moving block bootstrap</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
       <div class="d-none name"><code>dsm.var.movblk.Rd</code></div>
     </div>
@@ -80,25 +59,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.object</dt>
+
+
+<dl><dt id="arg-dsm-object">dsm.object<a class="anchor" aria-label="anchor" href="#arg-dsm-object"></a></dt>
 <dd><p>object returned from <code><a href="dsm.html">dsm</a></code>.</p></dd>
 
 
-<dt>pred.data</dt>
+<dt id="arg-pred-data">pred.data<a class="anchor" aria-label="anchor" href="#arg-pred-data"></a></dt>
 <dd><p>either: a single prediction grid or list of prediction
 grids. Each grid should be a <code>data.frame</code> with the same columns as the
 original data.</p></dd>
 
 
-<dt>n.boot</dt>
+<dt id="arg-n-boot">n.boot<a class="anchor" aria-label="anchor" href="#arg-n-boot"></a></dt>
 <dd><p>number of bootstrap resamples.</p></dd>
 
 
-<dt>block.size</dt>
+<dt id="arg-block-size">block.size<a class="anchor" aria-label="anchor" href="#arg-block-size"></a></dt>
 <dd><p>number of segments in each block.</p></dd>
 
 
-<dt>off.set</dt>
+<dt id="arg-off-set">off.set<a class="anchor" aria-label="anchor" href="#arg-off-set"></a></dt>
 <dd><p>a a vector or list of vectors with as many elements as there
 are in <code>pred.data</code>. Each vector is as long as the number of rows in the
 corresponding element of <code>pred.data</code>. These give the area associated with
@@ -106,33 +87,33 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 for the length of <code>pred.data</code>.</p></dd>
 
 
-<dt>ds.uncertainty</dt>
+<dt id="arg-ds-uncertainty">ds.uncertainty<a class="anchor" aria-label="anchor" href="#arg-ds-uncertainty"></a></dt>
 <dd><p>incorporate uncertainty in the detection function? See
 Details, below. Note that this feature is EXPERIMENTAL at the moment.</p></dd>
 
 
-<dt>samp.unit.name</dt>
+<dt id="arg-samp-unit-name">samp.unit.name<a class="anchor" aria-label="anchor" href="#arg-samp-unit-name"></a></dt>
 <dd><p>name sampling unit to resample (default
 'Transect.Label').</p></dd>
 
 
-<dt>progress.file</dt>
+<dt id="arg-progress-file">progress.file<a class="anchor" aria-label="anchor" href="#arg-progress-file"></a></dt>
 <dd><p>path to a file to be used (usually by Distance) to
-generate a progress bar (default <code>NULL</code> -- no file written).</p></dd>
+generate a progress bar (default <code>NULL</code> – no file written).</p></dd>
 
 
-<dt>bs.file</dt>
+<dt id="arg-bs-file">bs.file<a class="anchor" aria-label="anchor" href="#arg-bs-file"></a></dt>
 <dd><p>path to a file to store each bootstrap round. This stores all
 of the bootstrap results rather than just the summaries, enabling
 outliers to be detected and removed. (Default <code>NULL</code>).</p></dd>
 
 
-<dt>bar</dt>
+<dt id="arg-bar">bar<a class="anchor" aria-label="anchor" href="#arg-bar"></a></dt>
 <dd><p>should a progress bar be printed to screen? (Default <code>TRUE</code>).</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +122,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm.var.prop.html b/docs/reference/dsm.var.prop.html
index ad6ac3a..d7733a5 100644
--- a/docs/reference/dsm.var.prop.html
+++ b/docs/reference/dsm.var.prop.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use dsm_var_prop."><title>Prediction variance propagation for DSMs — dsm.var.prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm.var.prop"><meta property="og:description" content="This function is deprecated, use dsm_var_prop."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance propagation for DSMs — dsm.var.prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm.var.prop"><meta name="description" content="This function is deprecated, use dsm_var_prop."><meta property="og:description" content="This function is deprecated, use dsm_var_prop."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Prediction variance propagation for DSMs</h1>
+
+      <h1>Prediction variance propagation for DSMs</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
       <div class="d-none name"><code>dsm.var.prop.Rd</code></div>
     </div>
@@ -75,17 +54,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.obj</dt>
+
+
+<dl><dt id="arg-dsm-obj">dsm.obj<a class="anchor" aria-label="anchor" href="#arg-dsm-obj"></a></dt>
 <dd><p>a model object fitted by <code><a href="dsm.html">dsm</a></code>.</p></dd>
 
 
-<dt>pred.data</dt>
+<dt id="arg-pred-data">pred.data<a class="anchor" aria-label="anchor" href="#arg-pred-data"></a></dt>
 <dd><p>either: a single prediction grid or list of prediction
 grids. Each grid should be a <code>data.frame</code> with the same columns as the
 original data.</p></dd>
 
 
-<dt>off.set</dt>
+<dt id="arg-off-set">off.set<a class="anchor" aria-label="anchor" href="#arg-off-set"></a></dt>
 <dd><p>a a vector or list of vectors with as many elements as there
 are in <code>pred.data</code>. Each vector is as long as the number of rows in the
 corresponding element of <code>pred.data</code>. These give the area associated with
@@ -93,18 +74,18 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 for the length of <code>pred.data</code>.</p></dd>
 
 
-<dt>seglen.varname</dt>
+<dt id="arg-seglen-varname">seglen.varname<a class="anchor" aria-label="anchor" href="#arg-seglen-varname"></a></dt>
 <dd><p>name for the column which holds the segment length
 (default value <code>"Effort"</code>).</p></dd>
 
 
-<dt>type.pred</dt>
+<dt id="arg-type-pred">type.pred<a class="anchor" aria-label="anchor" href="#arg-type-pred"></a></dt>
 <dd><p>should the predictions be on the "response" or "link"
 scale? (default <code>"response"</code>).</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -113,14 +94,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm_cor.html b/docs/reference/dsm_cor.html
index 6d63f74..d2c8741 100644
--- a/docs/reference/dsm_cor.html
+++ b/docs/reference/dsm_cor.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Once a DSM has been fitted to data, this function can be used to check for
-autocorrelation in the residuals."><title>Check for autocorrelation in residuals — dsm_cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm_cor"><meta property="og:description" content="Once a DSM has been fitted to data, this function can be used to check for
-autocorrelation in the residuals."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check for autocorrelation in residuals — dsm_cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm_cor"><meta name="description" content="Once a DSM has been fitted to data, this function can be used to check for
+autocorrelation in the residuals."><meta property="og:description" content="Once a DSM has been fitted to data, this function can be used to check for
+autocorrelation in the residuals."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
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-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
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-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Check for autocorrelation in residuals</h1>
+
+      <h1>Check for autocorrelation in residuals</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_cor.R" class="external-link"><code>R/dsm_cor.R</code></a></small>
       <div class="d-none name"><code>dsm_cor.Rd</code></div>
     </div>
@@ -82,54 +61,54 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.obj</dt>
+
+
+<dl><dt id="arg-dsm-obj">dsm.obj<a class="anchor" aria-label="anchor" href="#arg-dsm-obj"></a></dt>
 <dd><p>a fitted dsm object.</p></dd>
 
 
-<dt>Transect.Label</dt>
+<dt id="arg-transect-label">Transect.Label<a class="anchor" aria-label="anchor" href="#arg-transect-label"></a></dt>
 <dd><p>label for the transect (default: <code>Transect.Label</code>).
 Using different labels can be useful when transects are split over
 geographical features or when transects are surveyed multiple times.</p></dd>
 
 
-<dt>Segment.Label</dt>
+<dt id="arg-segment-label">Segment.Label<a class="anchor" aria-label="anchor" href="#arg-segment-label"></a></dt>
 <dd><p>label for the segments (default: <code>Segment.Label</code>).The
 result of calling <code><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></code> must make sense.</p></dd>
 
 
-<dt>max.lag</dt>
+<dt id="arg-max-lag">max.lag<a class="anchor" aria-label="anchor" href="#arg-max-lag"></a></dt>
 <dd><p>maximum lag to calculate at.</p></dd>
 
 
-<dt>resid.type</dt>
+<dt id="arg-resid-type">resid.type<a class="anchor" aria-label="anchor" href="#arg-resid-type"></a></dt>
 <dd><p>the type of residuals used, see
 <code><a href="https://rdrr.io/pkg/mgcv/man/residuals.gam.html" class="external-link">residuals.gam</a></code>. Defaults to <code>"scaled.pearson"</code> in the GAM
 case and <code>"normalized"</code> in the GAMM case (which are equivalent).</p></dd>
 
 
-<dt>fun</dt>
+<dt id="arg-fun">fun<a class="anchor" aria-label="anchor" href="#arg-fun"></a></dt>
 <dd><p>the function to use, by default <code><a href="https://rdrr.io/r/stats/cor.html" class="external-link">cor</a></code>, must take two
 column vectors as arguments.</p></dd>
 
 
-<dt>ylim</dt>
+<dt id="arg-ylim">ylim<a class="anchor" aria-label="anchor" href="#arg-ylim"></a></dt>
 <dd><p>user defined limits in y direction.</p></dd>
 
 
-<dt>subset</dt>
+<dt id="arg-subset">subset<a class="anchor" aria-label="anchor" href="#arg-subset"></a></dt>
 <dd><p>which subset of the data should the correlation function be
 calculated on?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>other options to pass to <code><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a plot or a vector of <code>fun</code> applied at the lags.</p>
+    <p>a plot or a vector of <code>fun</code> applied at the lags.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -174,7 +153,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span> <span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
 <span class="r-in"><span>                key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Fitting hazard-rate key function</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> AIC= 677.418</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> AIC= 677.433</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> No survey area information supplied, only estimating detection function.</span>
 <span class="r-in"><span> <span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Some observations are outside of detection function 1 truncation!</span>
@@ -187,7 +166,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -196,14 +175,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/docs/reference/dsm_var_gam.html b/docs/reference/dsm_var_gam.html
index cdc1c2f..3697fd7 100644
--- a/docs/reference/dsm_var_gam.html
+++ b/docs/reference/dsm_var_gam.html
@@ -1,65 +1,44 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="If one is willing to assume the the detection function and spatial model are
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 independent, this function will produce estimates of variance of predictions
 of abundance, using the result that squared coefficients of variation will
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+add."><meta property="og:description" content="If one is willing to assume the the detection function and spatial model are
 independent, this function will produce estimates of variance of predictions
 of abundance, using the result that squared coefficients of variation will
-add."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Prediction variance estimation assuming independence</h1>
+
+      <h1>Prediction variance estimation assuming independence</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_var_gam.R" class="external-link"><code>R/dsm_var_gam.R</code></a></small>
       <div class="d-none name"><code>dsm_var_gam.Rd</code></div>
     </div>
@@ -84,17 +63,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.obj</dt>
+
+
+<dl><dt id="arg-dsm-obj">dsm.obj<a class="anchor" aria-label="anchor" href="#arg-dsm-obj"></a></dt>
 <dd><p>a model object fitted by <code><a href="dsm.html">dsm</a></code>.</p></dd>
 
 
-<dt>pred.data</dt>
+<dt id="arg-pred-data">pred.data<a class="anchor" aria-label="anchor" href="#arg-pred-data"></a></dt>
 <dd><p>either: a single prediction grid or list of prediction
 grids. Each grid should be a <code>data.frame</code> with the same columns as the
 original data.</p></dd>
 
 
-<dt>off.set</dt>
+<dt id="arg-off-set">off.set<a class="anchor" aria-label="anchor" href="#arg-off-set"></a></dt>
 <dd><p>a a vector or list of vectors with as many elements as there
 are in <code>pred.data</code>. Each vector is as long as the number of rows in the
 corresponding element of <code>pred.data</code>. These give the area associated with
@@ -102,21 +83,19 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 for the length of <code>pred.data</code>.</p></dd>
 
 
-<dt>seglen.varname</dt>
+<dt id="arg-seglen-varname">seglen.varname<a class="anchor" aria-label="anchor" href="#arg-seglen-varname"></a></dt>
 <dd><p>name for the column which holds the segment length
 (default value <code>"Effort"</code>).</p></dd>
 
 
-<dt>type.pred</dt>
+<dt id="arg-type-pred">type.pred<a class="anchor" aria-label="anchor" href="#arg-type-pred"></a></dt>
 <dd><p>should the predictions be on the "response" or "link"
 scale? (default <code>"response"</code>).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a <code>list</code> with elements</p><ul><li><p><code>model</code> the fitted model object</p></li>
+    <p>a <code>list</code> with elements</p><ul><li><p><code>model</code> the fitted model object</p></li>
 <li><p><code>pred.var</code> variance of the regions given in <code>pred.data</code>.</p></li>
 <li><p><code>bootstrap</code> logical, always <code>FALSE</code></p></li>
 <li><p><code>model</code> the fitted model with the extra term</p></li>
@@ -129,7 +108,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -147,10 +126,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span> <span class="co"># Calculate the variance</span></span></span>
 <span class="r-in"><span> <span class="co"># this will give a summary over the whole area in mexdolphins$preddata</span></span></span>
 <span class="r-in"><span> <span class="va">mod1.var</span> <span class="op">&lt;-</span> <span class="fu">dsm_var_gam</span><span class="op">(</span><span class="va">mod1</span>, <span class="va">preddata</span>, off.set<span class="op">=</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -159,14 +138,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm_var_movblk.html b/docs/reference/dsm_var_movblk.html
index f4fbf74..58e508d 100644
--- a/docs/reference/dsm_var_movblk.html
+++ b/docs/reference/dsm_var_movblk.html
@@ -1,63 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Estimate the variance in abundance over an area using a moving block
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance estimation via parametric moving block bootstrap — dsm_var_movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm_var_movblk"><meta name="description" content="Estimate the variance in abundance over an area using a moving block
 bootstrap. Two procedures are implemented, one incorporating detection
-function uncertainty, one not."><title>Variance estimation via parametric moving block bootstrap — dsm_var_movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm_var_movblk"><meta property="og:description" content="Estimate the variance in abundance over an area using a moving block
+function uncertainty, one not."><meta property="og:description" content="Estimate the variance in abundance over an area using a moving block
 bootstrap. Two procedures are implemented, one incorporating detection
-function uncertainty, one not."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+function uncertainty, one not."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
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     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Variance estimation via parametric moving block bootstrap</h1>
+
+      <h1>Variance estimation via parametric moving block bootstrap</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_var_movblk.R" class="external-link"><code>R/dsm_var_movblk.R</code></a></small>
       <div class="d-none name"><code>dsm_var_movblk.Rd</code></div>
     </div>
@@ -86,25 +65,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.object</dt>
+
+
+<dl><dt id="arg-dsm-object">dsm.object<a class="anchor" aria-label="anchor" href="#arg-dsm-object"></a></dt>
 <dd><p>object returned from <code><a href="dsm.html">dsm</a></code>.</p></dd>
 
 
-<dt>pred.data</dt>
+<dt id="arg-pred-data">pred.data<a class="anchor" aria-label="anchor" href="#arg-pred-data"></a></dt>
 <dd><p>either: a single prediction grid or list of prediction
 grids. Each grid should be a <code>data.frame</code> with the same columns as the
 original data.</p></dd>
 
 
-<dt>n.boot</dt>
+<dt id="arg-n-boot">n.boot<a class="anchor" aria-label="anchor" href="#arg-n-boot"></a></dt>
 <dd><p>number of bootstrap resamples.</p></dd>
 
 
-<dt>block.size</dt>
+<dt id="arg-block-size">block.size<a class="anchor" aria-label="anchor" href="#arg-block-size"></a></dt>
 <dd><p>number of segments in each block.</p></dd>
 
 
-<dt>off.set</dt>
+<dt id="arg-off-set">off.set<a class="anchor" aria-label="anchor" href="#arg-off-set"></a></dt>
 <dd><p>a a vector or list of vectors with as many elements as there
 are in <code>pred.data</code>. Each vector is as long as the number of rows in the
 corresponding element of <code>pred.data</code>. These give the area associated with
@@ -112,34 +93,34 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 for the length of <code>pred.data</code>.</p></dd>
 
 
-<dt>ds.uncertainty</dt>
+<dt id="arg-ds-uncertainty">ds.uncertainty<a class="anchor" aria-label="anchor" href="#arg-ds-uncertainty"></a></dt>
 <dd><p>incorporate uncertainty in the detection function? See
 Details, below. Note that this feature is EXPERIMENTAL at the moment.</p></dd>
 
 
-<dt>samp.unit.name</dt>
+<dt id="arg-samp-unit-name">samp.unit.name<a class="anchor" aria-label="anchor" href="#arg-samp-unit-name"></a></dt>
 <dd><p>name sampling unit to resample (default
 'Transect.Label').</p></dd>
 
 
-<dt>progress.file</dt>
+<dt id="arg-progress-file">progress.file<a class="anchor" aria-label="anchor" href="#arg-progress-file"></a></dt>
 <dd><p>path to a file to be used (usually by Distance) to
-generate a progress bar (default <code>NULL</code> -- no file written).</p></dd>
+generate a progress bar (default <code>NULL</code> – no file written).</p></dd>
 
 
-<dt>bs.file</dt>
+<dt id="arg-bs-file">bs.file<a class="anchor" aria-label="anchor" href="#arg-bs-file"></a></dt>
 <dd><p>path to a file to store each bootstrap round. This stores all
 of the bootstrap results rather than just the summaries, enabling
 outliers to be detected and removed. (Default <code>NULL</code>).</p></dd>
 
 
-<dt>bar</dt>
+<dt id="arg-bar">bar<a class="anchor" aria-label="anchor" href="#arg-bar"></a></dt>
 <dd><p>should a progress bar be printed to screen? (Default <code>TRUE</code>).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    
+
 
 <p>Setting <code>ds.uncertainty=TRUE</code> will incorporate detection function
 uncertainty directly into the bootstrap. This is done by generating
@@ -157,7 +138,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -178,10 +159,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>   block.size <span class="op">=</span> <span class="fl">3</span>, samp.unit.name <span class="op">=</span> <span class="st">"Transect.Label"</span>,</span></span>
 <span class="r-in"><span>   off.set <span class="op">=</span> <span class="va">preddata</span><span class="op">$</span><span class="va">area</span>,</span></span>
 <span class="r-in"><span>   bar <span class="op">=</span> <span class="cn">TRUE</span>, bs.file <span class="op">=</span> <span class="st">"mexico-bs.csv"</span>, ds.uncertainty <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -190,14 +171,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm_var_prop.html b/docs/reference/dsm_var_prop.html
index e3357ad..e1af30d 100644
--- a/docs/reference/dsm_var_prop.html
+++ b/docs/reference/dsm_var_prop.html
@@ -1,65 +1,44 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="To ensure that uncertainty from the detection function is correctly
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance propagation for DSMs — dsm_var_prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm_var_prop"><meta name="description" content="To ensure that uncertainty from the detection function is correctly
 propagated to the final variance estimate of abundance, this function uses a
 method first detailed in Williams et al (2011), further explanation is given
-in Bravington et al. (2021)."><title>Prediction variance propagation for DSMs — dsm_var_prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm_var_prop"><meta property="og:description" content="To ensure that uncertainty from the detection function is correctly
+in Bravington et al. (2021)."><meta property="og:description" content="To ensure that uncertainty from the detection function is correctly
 propagated to the final variance estimate of abundance, this function uses a
 method first detailed in Williams et al (2011), further explanation is given
-in Bravington et al. (2021)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+in Bravington et al. (2021)."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-     
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Prediction variance propagation for DSMs</h1>
+
+      <h1>Prediction variance propagation for DSMs</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_var_prop.R" class="external-link"><code>R/dsm_var_prop.R</code></a></small>
       <div class="d-none name"><code>dsm_var_prop.Rd</code></div>
     </div>
@@ -84,17 +63,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.obj</dt>
+
+
+<dl><dt id="arg-dsm-obj">dsm.obj<a class="anchor" aria-label="anchor" href="#arg-dsm-obj"></a></dt>
 <dd><p>a model object fitted by <code><a href="dsm.html">dsm</a></code>.</p></dd>
 
 
-<dt>pred.data</dt>
+<dt id="arg-pred-data">pred.data<a class="anchor" aria-label="anchor" href="#arg-pred-data"></a></dt>
 <dd><p>either: a single prediction grid or list of prediction
 grids. Each grid should be a <code>data.frame</code> with the same columns as the
 original data.</p></dd>
 
 
-<dt>off.set</dt>
+<dt id="arg-off-set">off.set<a class="anchor" aria-label="anchor" href="#arg-off-set"></a></dt>
 <dd><p>a a vector or list of vectors with as many elements as there
 are in <code>pred.data</code>. Each vector is as long as the number of rows in the
 corresponding element of <code>pred.data</code>. These give the area associated with
@@ -102,21 +83,19 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 for the length of <code>pred.data</code>.</p></dd>
 
 
-<dt>seglen.varname</dt>
+<dt id="arg-seglen-varname">seglen.varname<a class="anchor" aria-label="anchor" href="#arg-seglen-varname"></a></dt>
 <dd><p>name for the column which holds the segment length
 (default value <code>"Effort"</code>).</p></dd>
 
 
-<dt>type.pred</dt>
+<dt id="arg-type-pred">type.pred<a class="anchor" aria-label="anchor" href="#arg-type-pred"></a></dt>
 <dd><p>should the predictions be on the "response" or "link"
 scale? (default <code>"response"</code>).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a <code>list</code> with elements</p><ul><li><p><code>model</code> the fitted model object</p></li>
+    <p>a <code>list</code> with elements</p><ul><li><p><code>model</code> the fitted model object</p></li>
 <li><p><code>pred.var</code> variance of each region given in <code>pred.data</code></p></li>
 <li><p><code>bootstrap</code> logical, always <code>FALSE</code></p></li>
 <li><p><code>pred.data</code> as above</p></li>
@@ -144,7 +123,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="diagnostics">Diagnostics<a class="anchor" aria-label="anchor" href="#diagnostics"></a></h2>
-    
+
 
 <p>The summary output from the function includes a simply diagnostic that shows
 the average probability of detection from the "original" fitted model (the
@@ -162,7 +141,7 @@ <h2 id="diagnostics">Diagnostics<a class="anchor" aria-label="anchor" href="#dia
     </div>
     <div class="section level2">
     <h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a></h2>
-    
+
 
 <p>Note that this routine is only useful if a detection function has been used
 in the DSM. It cannot be used when the <code>abundance.est</code> or <code>density.est</code>
@@ -185,7 +164,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>Mark V. Bravington, Sharon L. Hedley. Bugs added by David L. Miller.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -194,14 +173,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dsm_varprop.html b/docs/reference/dsm_varprop.html
index c8882d2..8a9b28d 100644
--- a/docs/reference/dsm_varprop.html
+++ b/docs/reference/dsm_varprop.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Calculate the uncertainty in predictions from a fitted DSM, including
-uncertainty from the detection function."><title>Variance propagation for density surface models — dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance propagation for density surface models — dsm_varprop"><meta property="og:description" content="Calculate the uncertainty in predictions from a fitted DSM, including
-uncertainty from the detection function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance propagation for density surface models — dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance propagation for density surface models — dsm_varprop"><meta name="description" content="Calculate the uncertainty in predictions from a fitted DSM, including
+uncertainty from the detection function."><meta property="og:description" content="Calculate the uncertainty in predictions from a fitted DSM, including
+uncertainty from the detection function."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
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+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
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       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Variance propagation for density surface models</h1>
+
+      <h1>Variance propagation for density surface models</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm_varprop.R" class="external-link"><code>R/dsm_varprop.R</code></a></small>
       <div class="d-none name"><code>dsm_varprop.Rd</code></div>
     </div>
@@ -78,35 +57,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>model</dt>
+
+
+<dl><dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>a fitted <code><a href="dsm.html">dsm</a></code>.</p></dd>
 
 
-<dt>newdata</dt>
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
 <dd><p>the prediction grid. Set to <code>NULL</code> to avoid making
 predictions and just return model objects.</p></dd>
 
 
-<dt>trace</dt>
+<dt id="arg-trace">trace<a class="anchor" aria-label="anchor" href="#arg-trace"></a></dt>
 <dd><p>for debugging, see how the scale parameter estimation is going.</p></dd>
 
 
-<dt>var.type</dt>
+<dt id="arg-var-type">var.type<a class="anchor" aria-label="anchor" href="#arg-var-type"></a></dt>
 <dd><p>which variance-covariance matrix should be used (<code>"Vp"</code> for
 variance-covariance conditional on smoothing parameter(s), <code>"Vc"</code> for
 unconditional). See <code><a href="https://rdrr.io/pkg/mgcv/man/gamObject.html" class="external-link">gamObject</a></code> for an details/explanation. If
 in doubt, stick with the default, <code>"Vp"</code>.</p></dd>
 
 
-<dt>var_type</dt>
+<dt id="arg-var-type">var_type<a class="anchor" aria-label="anchor" href="#arg-var-type"></a></dt>
 <dd><p>deprecated, use <code>var.type</code> instead.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a <code>list</code> with elements:</p><ul><li><p><code>old_model</code> fitted model supplied to the function as <code>model</code></p></li>
+    <p>a <code>list</code> with elements:</p><ul><li><p><code>old_model</code> fitted model supplied to the function as <code>model</code></p></li>
 <li><p><code>refit</code> refitted model object, with extra term</p></li>
 <li><p><code>pred</code> point estimates of predictions at <code>newdata</code></p></li>
 <li><p><code>var</code> total variance calculated over all of <code>newdata</code></p></li>
@@ -139,7 +118,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="diagnostics">Diagnostics<a class="anchor" aria-label="anchor" href="#diagnostics"></a></h2>
-    
+
 
 <p>The summary output from the function includes a simply diagnostic that shows
 the average probability of detection from the "original" fitted model (the
@@ -174,7 +153,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 Hedley.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -183,14 +162,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/dummy_ddf.html b/docs/reference/dummy_ddf.html
index 18d9d6c..60bf4b2 100644
--- a/docs/reference/dummy_ddf.html
+++ b/docs/reference/dummy_ddf.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Create a detection function object for strip/plot surveys for use in density
-surface models."><title>Detection function objects when detection is certain — dummy_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Detection function objects when detection is certain — dummy_ddf"><meta property="og:description" content="Create a detection function object for strip/plot surveys for use in density
-surface models."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Detection function objects when detection is certain — dummy_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Detection function objects when detection is certain — dummy_ddf"><meta name="description" content="Create a detection function object for strip/plot surveys for use in density
+surface models."><meta property="og:description" content="Create a detection function object for strip/plot surveys for use in density
+surface models."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Detection function objects when detection is certain</h1>
+
+      <h1>Detection function objects when detection is certain</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dummy_ddf.R" class="external-link"><code>R/dummy_ddf.R</code></a></small>
       <div class="d-none name"><code>dummy_ddf.Rd</code></div>
     </div>
@@ -72,24 +51,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>numeric vector of object identifiers, relating to the <code>object</code>
 field in the observation data of the DSM.</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>group size for each observation (default all groups size 1)</p></dd>
 
 
-<dt>width</dt>
+<dt id="arg-width">width<a class="anchor" aria-label="anchor" href="#arg-width"></a></dt>
 <dd><p>right truncation</p></dd>
 
 
-<dt>left</dt>
+<dt id="arg-left">left<a class="anchor" aria-label="anchor" href="#arg-left"></a></dt>
 <dd><p>left truncation (default 0, no left truncation)</p></dd>
 
 
-<dt>transect</dt>
+<dt id="arg-transect">transect<a class="anchor" aria-label="anchor" href="#arg-transect"></a></dt>
 <dd><p><code>"line"</code> or <code>"point"</code> transect</p></dd>
 
 </dl></div>
@@ -98,7 +79,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -107,14 +88,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/generate.ds.uncertainty.html b/docs/reference/generate.ds.uncertainty.html
index ddcd2a8..51e01b5 100644
--- a/docs/reference/generate.ds.uncertainty.html
+++ b/docs/reference/generate.ds.uncertainty.html
@@ -1,63 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generate data from a fitted detection function — generate.ds.uncertainty • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generate data from a fitted detection function — generate.ds.uncertainty"><meta name="description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
 procedure generates data from the fitted detection function (assuming that
-it is correct)."><title>Generate data from a fitted detection function — generate.ds.uncertainty • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generate data from a fitted detection function — generate.ds.uncertainty"><meta property="og:description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
+it is correct)."><meta property="og:description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
 procedure generates data from the fitted detection function (assuming that
-it is correct)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+it is correct)."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
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-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
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-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Generate data from a fitted detection function</h1>
+
+      <h1>Generate data from a fitted detection function</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/generate.ds.uncertainty.R" class="external-link"><code>R/generate.ds.uncertainty.R</code></a></small>
       <div class="d-none name"><code>generate.ds.uncertainty.Rd</code></div>
     </div>
@@ -75,7 +54,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ds.object</dt>
+
+
+<dl><dt id="arg-ds-object">ds.object<a class="anchor" aria-label="anchor" href="#arg-ds-object"></a></dt>
 <dd><p>a fitted detection function object (as returned by a call
 to <code><a href="https://rdrr.io/pkg/mrds/man/ddf.ds.html" class="external-link">ddf.ds</a></code>).</p></dd>
 
@@ -91,7 +72,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -100,14 +81,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/generate.mb.sample.html b/docs/reference/generate.mb.sample.html
index 1f11fd6..85c15ab 100644
--- a/docs/reference/generate.mb.sample.html
+++ b/docs/reference/generate.mb.sample.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Not usually used on its own, called from within
-dsm.var.movblk."><title>Moving block bootstrap sampler — generate.mb.sample • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Moving block bootstrap sampler — generate.mb.sample"><meta property="og:description" content="Not usually used on its own, called from within
-dsm.var.movblk."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Moving block bootstrap sampler — generate.mb.sample • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Moving block bootstrap sampler — generate.mb.sample"><meta name="description" content="Not usually used on its own, called from within
+dsm.var.movblk."><meta property="og:description" content="Not usually used on its own, called from within
+dsm.var.movblk."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Moving block bootstrap sampler</h1>
+
+      <h1>Moving block bootstrap sampler</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/generate.mb.sample.R" class="external-link"><code>R/generate.mb.sample.R</code></a></small>
       <div class="d-none name"><code>generate.mb.sample.Rd</code></div>
     </div>
@@ -79,38 +58,38 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num.blocks.required</dt>
+
+
+<dl><dt id="arg-num-blocks-required">num.blocks.required<a class="anchor" aria-label="anchor" href="#arg-num-blocks-required"></a></dt>
 <dd><p>number of blocks that we need.</p></dd>
 
 
-<dt>block.size</dt>
+<dt id="arg-block-size">block.size<a class="anchor" aria-label="anchor" href="#arg-block-size"></a></dt>
 <dd><p>number of segments per block.</p></dd>
 
 
-<dt>which.blocks</dt>
+<dt id="arg-which-blocks">which.blocks<a class="anchor" aria-label="anchor" href="#arg-which-blocks"></a></dt>
 <dd><p>which blocks should be sampled.</p></dd>
 
 
-<dt>dsm.data</dt>
+<dt id="arg-dsm-data">dsm.data<a class="anchor" aria-label="anchor" href="#arg-dsm-data"></a></dt>
 <dd><p>the <code>$data</code> element of the result of a call to <code><a href="dsm.html">dsm</a></code>.</p></dd>
 
 
-<dt>unit.info</dt>
+<dt id="arg-unit-info">unit.info<a class="anchor" aria-label="anchor" href="#arg-unit-info"></a></dt>
 <dd><p>result of calling <code><a href="block.info.per.su.html">block.info.per.su</a></code>.</p></dd>
 
 
-<dt>n.units</dt>
+<dt id="arg-n-units">n.units<a class="anchor" aria-label="anchor" href="#arg-n-units"></a></dt>
 <dd><p>number of sampling units.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>vector of log-residuals</p>
+    <p>vector of log-residuals</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -119,14 +98,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/index.html b/docs/reference/index.html
index f485577..0285be9 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -1,265 +1,283 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function reference • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function reference"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Package index • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Package index"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-index">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Function reference</h1>
+
+      <h1>Package index</h1>
     </div>
 
     <div class="section level2">
       <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="#all-functions"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="block.info.per.su.html">block.info.per.su()</a></code> 
+
+          <code><a href="block.info.per.su.html">block.info.per.su()</a></code>
+
         </dt>
         <dd>Find the block information</dd>
       </dl><dl><dt>
-          
-          <code><a href="check.cols.html">check.cols()</a></code> 
+
+          <code><a href="check.cols.html">check.cols()</a></code>
+
         </dt>
         <dd>Check column names exist</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm-data.html">dsm-data</a></code> 
+
+          <code><a href="dsm-data.html">dsm-data</a></code>
+
         </dt>
         <dd>Data format for DSM</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm-package.html">dsm-package</a></code> 
+
+          <code><a href="dsm-package.html">dsm-package</a></code>
+
         </dt>
         <dd>Density surface modelling</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm.cor.html">dsm.cor()</a></code> 
+
+          <code><a href="dsm.cor.html">dsm.cor()</a></code>
+
         </dt>
         <dd>Check for autocorrelation in residuals</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm.html">dsm()</a></code> 
+
+          <code><a href="dsm.html">dsm()</a></code>
+
         </dt>
         <dd>Fit a density surface model to segment-specific estimates of abundance or density.</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm.var.gam.html">dsm.var.gam()</a></code> 
+
+          <code><a href="dsm.var.gam.html">dsm.var.gam()</a></code>
+
         </dt>
         <dd>Prediction variance estimation assuming independence</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm.var.movblk.html">dsm.var.movblk()</a></code> 
+
+          <code><a href="dsm.var.movblk.html">dsm.var.movblk()</a></code>
+
         </dt>
         <dd>Variance estimation via parametric moving block bootstrap</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm.var.prop.html">dsm.var.prop()</a></code> 
+
+          <code><a href="dsm.var.prop.html">dsm.var.prop()</a></code>
+
         </dt>
         <dd>Prediction variance propagation for DSMs</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm_cor.html">dsm_cor()</a></code> 
+
+          <code><a href="dsm_cor.html">dsm_cor()</a></code>
+
         </dt>
         <dd>Check for autocorrelation in residuals</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm_varprop.html">dsm_varprop()</a></code> 
+
+          <code><a href="dsm_varprop.html">dsm_varprop()</a></code>
+
         </dt>
         <dd>Variance propagation for density surface models</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm_var_gam.html">dsm_var_gam()</a></code> 
+
+          <code><a href="dsm_var_gam.html">dsm_var_gam()</a></code>
+
         </dt>
         <dd>Prediction variance estimation assuming independence</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm_var_movblk.html">dsm_var_movblk()</a></code> 
+
+          <code><a href="dsm_var_movblk.html">dsm_var_movblk()</a></code>
+
         </dt>
         <dd>Variance estimation via parametric moving block bootstrap</dd>
       </dl><dl><dt>
-          
-          <code><a href="dsm_var_prop.html">dsm_var_prop()</a></code> 
+
+          <code><a href="dsm_var_prop.html">dsm_var_prop()</a></code>
+
         </dt>
         <dd>Prediction variance propagation for DSMs</dd>
       </dl><dl><dt>
-          
-          <code><a href="dummy_ddf.html">dummy_ddf()</a></code> 
+
+          <code><a href="dummy_ddf.html">dummy_ddf()</a></code>
+
         </dt>
         <dd>Detection function objects when detection is certain</dd>
       </dl><dl><dt>
-          
-          <code><a href="generate.ds.uncertainty.html">generate.ds.uncertainty()</a></code> 
+
+          <code><a href="generate.ds.uncertainty.html">generate.ds.uncertainty()</a></code>
+
         </dt>
         <dd>Generate data from a fitted detection function</dd>
       </dl><dl><dt>
-          
-          <code><a href="generate.mb.sample.html">generate.mb.sample()</a></code> 
+
+          <code><a href="generate.mb.sample.html">generate.mb.sample()</a></code>
+
         </dt>
         <dd>Moving block bootstrap sampler</dd>
       </dl><dl><dt>
-          
-          <code><a href="latlong2km.html">latlong2km()</a></code> 
+
+          <code><a href="latlong2km.html">latlong2km()</a></code>
+
         </dt>
         <dd>Convert latitude and longitude to Northings and Eastings</dd>
       </dl><dl><dt>
-          
-          <code><a href="make.soapgrid.html">make.soapgrid()</a></code> 
+
+          <code><a href="make.soapgrid.html">make.soapgrid()</a></code>
+
         </dt>
         <dd>Create a knot grid for the internal part of a soap film smoother.</dd>
       </dl><dl><dt>
-          
-          <code><a href="mexdolphins.html">mexdolphins</a></code> <code><a href="mexdolphins.html">obsdata</a></code> <code><a href="mexdolphins.html">segdata</a></code> <code><a href="mexdolphins.html">preddata</a></code> <code><a href="mexdolphins.html">pred.polys</a></code> <code><a href="mexdolphins.html">distdata</a></code> <code><a href="mexdolphins.html">survey.area</a></code> 
+
+          <code><a href="mexdolphins.html">mexdolphins</a></code> <code><a href="mexdolphins.html">obsdata</a></code> <code><a href="mexdolphins.html">segdata</a></code> <code><a href="mexdolphins.html">preddata</a></code> <code><a href="mexdolphins.html">pred.polys</a></code> <code><a href="mexdolphins.html">distdata</a></code> <code><a href="mexdolphins.html">survey.area</a></code>
+
         </dt>
         <dd>Pan-tropical spotted dolphins in the Gulf of Mexico</dd>
       </dl><dl><dt>
-          
-          <code><a href="obs_exp.html">obs_exp()</a></code> 
+
+          <code><a href="obs_exp.html">obs_exp()</a></code>
+
         </dt>
         <dd>Observed versus expected diagnostics for fitted DSMs</dd>
       </dl><dl><dt>
-          
-          <code><a href="plot.dsm.html">plot(<i>&lt;dsm&gt;</i>)</a></code> 
+
+          <code><a href="plot.dsm.html">plot(<i>&lt;dsm&gt;</i>)</a></code>
+
         </dt>
         <dd>Plot a density surface model.</dd>
       </dl><dl><dt>
-          
-          <code><a href="plot.dsm.var.html">plot(<i>&lt;dsm.var&gt;</i>)</a></code> 
+
+          <code><a href="plot.dsm.var.html">plot(<i>&lt;dsm.var&gt;</i>)</a></code>
+
         </dt>
         <dd>Create plots of abundance uncertainty</dd>
       </dl><dl><dt>
-          
-          <code><a href="plot_pred_by_term.html">plot_pred_by_term()</a></code> 
+
+          <code><a href="plot_pred_by_term.html">plot_pred_by_term()</a></code>
+
         </dt>
         <dd>Spatially plot predictions per model term</dd>
       </dl><dl><dt>
-          
-          <code><a href="predict.dsm.html">predict(<i>&lt;dsm&gt;</i>)</a></code> 
+
+          <code><a href="predict.dsm.html">predict(<i>&lt;dsm&gt;</i>)</a></code>
+
         </dt>
         <dd>Predict from a fitted density surface model</dd>
       </dl><dl><dt>
-          
-          <code><a href="predict.fake_ddf.html">predict(<i>&lt;fake_ddf&gt;</i>)</a></code> 
+
+          <code><a href="predict.fake_ddf.html">predict(<i>&lt;fake_ddf&gt;</i>)</a></code>
+
         </dt>
         <dd>Prediction for fake detection functions</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.dsm.html">print(<i>&lt;dsm&gt;</i>)</a></code> 
+
+          <code><a href="print.dsm.html">print(<i>&lt;dsm&gt;</i>)</a></code>
+
         </dt>
         <dd>Print a description of a density surface model object</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.dsm.var.html">print(<i>&lt;dsm.var&gt;</i>)</a></code> 
+
+          <code><a href="print.dsm.var.html">print(<i>&lt;dsm.var&gt;</i>)</a></code>
+
         </dt>
         <dd>Print a description of a density surface model variance object</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.dsm_varprop.html">print(<i>&lt;dsm_varprop&gt;</i>)</a></code> 
+
+          <code><a href="print.dsm_varprop.html">print(<i>&lt;dsm_varprop&gt;</i>)</a></code>
+
         </dt>
         <dd>Print a description of a density surface model variance object</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.summary.dsm.var.html">print(<i>&lt;summary.dsm.var&gt;</i>)</a></code> 
+
+          <code><a href="print.summary.dsm.var.html">print(<i>&lt;summary.dsm.var&gt;</i>)</a></code>
+
         </dt>
         <dd>Print summary of density surface model variance object</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.summary.dsm_varprop.html">print(<i>&lt;summary.dsm_varprop&gt;</i>)</a></code> 
+
+          <code><a href="print.summary.dsm_varprop.html">print(<i>&lt;summary.dsm_varprop&gt;</i>)</a></code>
+
         </dt>
         <dd>Print summary of density surface model variance object</dd>
       </dl><dl><dt>
-          
-          <code><a href="rqgam.check.html">rqgam.check()</a></code> 
+
+          <code><a href="rqgam.check.html">rqgam.check()</a></code>
+
         </dt>
         <dd>Randomised quantile residuals check plot for GAMs/DSMs</dd>
       </dl><dl><dt>
-          
-          <code><a href="rqgam_check.html">rqgam_check()</a></code> 
+
+          <code><a href="rqgam_check.html">rqgam_check()</a></code>
+
         </dt>
         <dd>Randomised quantile residuals check plot for GAMs/DSMs</dd>
       </dl><dl><dt>
-          
-          <code><a href="summary.dsm.html">summary(<i>&lt;dsm&gt;</i>)</a></code> 
+
+          <code><a href="summary.dsm.html">summary(<i>&lt;dsm&gt;</i>)</a></code>
+
         </dt>
         <dd>Summarize a fitted density surface model</dd>
       </dl><dl><dt>
-          
-          <code><a href="summary.dsm.var.html">summary(<i>&lt;dsm.var&gt;</i>)</a></code> 
+
+          <code><a href="summary.dsm.var.html">summary(<i>&lt;dsm.var&gt;</i>)</a></code>
+
         </dt>
         <dd>Summarize the variance of a density surface model</dd>
       </dl><dl><dt>
-          
-          <code><a href="summary.dsm_varprop.html">summary(<i>&lt;dsm_varprop&gt;</i>)</a></code> 
+
+          <code><a href="summary.dsm_varprop.html">summary(<i>&lt;dsm_varprop&gt;</i>)</a></code>
+
         </dt>
         <dd>Summarize the variance of a density surface model</dd>
       </dl><dl><dt>
-          
-          <code><a href="trim.var.html">trim.var()</a></code> 
+
+          <code><a href="trim.var.html">trim.var()</a></code>
+
         </dt>
         <dd>Trimmed variance</dd>
       </dl><dl><dt>
-          
-          <code><a href="vis.concurvity.html">vis.concurvity()</a></code> 
+
+          <code><a href="vis.concurvity.html">vis.concurvity()</a></code>
+
         </dt>
         <dd>Visualise concurvity between terms in a GAM</dd>
       </dl><dl><dt>
-          
-          <code><a href="vis_concurvity.html">vis_concurvity()</a></code> 
+
+          <code><a href="vis_concurvity.html">vis_concurvity()</a></code>
+
         </dt>
         <dd>Visualise concurvity between terms in a GAM</dd>
       </dl></div>
@@ -271,14 +289,14 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/latlong2km.html b/docs/reference/latlong2km.html
index 1afcf45..b471995 100644
--- a/docs/reference/latlong2km.html
+++ b/docs/reference/latlong2km.html
@@ -1,63 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Convert latitude and longitude to Northings and Eastings — latlong2km • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert latitude and longitude to Northings and Eastings — latlong2km"><meta name="description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
 south-north from axes through (lon0,lat0) using the
-"spherical law of cosines".'><title>Convert latitude and longitude to Northings and Eastings — latlong2km • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert latitude and longitude to Northings and Eastings — latlong2km"><meta property="og:description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
+"spherical law of cosines".'><meta property="og:description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
 south-north from axes through (lon0,lat0) using the
-"spherical law of cosines".'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+"spherical law of cosines".'></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Convert latitude and longitude to Northings and Eastings</h1>
+
+      <h1>Convert latitude and longitude to Northings and Eastings</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/latlong2km.R" class="external-link"><code>R/latlong2km.R</code></a></small>
       <div class="d-none name"><code>latlong2km.Rd</code></div>
     </div>
@@ -75,27 +54,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>lon</dt>
+
+
+<dl><dt id="arg-lon">lon<a class="anchor" aria-label="anchor" href="#arg-lon"></a></dt>
 <dd><p>longitude</p></dd>
 
 
-<dt>lat</dt>
+<dt id="arg-lat">lat<a class="anchor" aria-label="anchor" href="#arg-lat"></a></dt>
 <dd><p>latitude</p></dd>
 
 
-<dt>lon0</dt>
+<dt id="arg-lon-">lon0<a class="anchor" aria-label="anchor" href="#arg-lon-"></a></dt>
 <dd><p>longitude reference point (defaults to mean longitude)</p></dd>
 
 
-<dt>lat0</dt>
+<dt id="arg-lat-">lat0<a class="anchor" aria-label="anchor" href="#arg-lat-"></a></dt>
 <dd><p>latitude reference point (defaults to mean latitude)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list with elements <code>km.e</code> and <code>km.n</code>.</p>
+    <p>list with elements <code>km.e</code> and <code>km.n</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -108,7 +87,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>Simon N. Wood</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -117,14 +96,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/make.soapgrid.html b/docs/reference/make.soapgrid.html
index 19000dd..7d443df 100644
--- a/docs/reference/make.soapgrid.html
+++ b/docs/reference/make.soapgrid.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='This routine simply creates a grid of knots (in the correct format) to be
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-used as in the "internal" part of the soap film smoother'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+used as in the "internal" part of the soap film smoother'><meta property="og:description" content='This routine simply creates a grid of knots (in the correct format) to be
+used as in the "internal" part of the soap film smoother'></head><body>
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-      <img src="" class="logo" alt=""><h1>Create a knot grid for the internal part of a soap film smoother.</h1>
+
+      <h1>Create a knot grid for the internal part of a soap film smoother.</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/make.soapgrid.R" class="external-link"><code>R/make.soapgrid.R</code></a></small>
       <div class="d-none name"><code>make.soapgrid.Rd</code></div>
     </div>
@@ -72,12 +51,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>bnd</dt>
+
+
+<dl><dt id="arg-bnd">bnd<a class="anchor" aria-label="anchor" href="#arg-bnd"></a></dt>
 <dd><p><code>list</code> with elements <code>x</code> and <code>y</code> which give the locations of the
 boundary vertices. The first and last elements should be the same.</p></dd>
 
 
-<dt>n.grid</dt>
+<dt id="arg-n-grid">n.grid<a class="anchor" aria-label="anchor" href="#arg-n-grid"></a></dt>
 <dd><p>either one number giving the number of points along the <code>x</code>
 and <code>y</code> axes that should be used to create the grid, or a vector giving the
 number in the <code>x</code> direction, then <code>y</code> direction.</p></dd>
@@ -85,16 +66,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a list with elements <code>x</code> and <code>y</code>, containing the knot locations.</p>
+    <p>a list with elements <code>x</code> and <code>y</code>, containing the knot locations.</p>
     </div>
     <div class="section level2">
     <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>David L Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -103,14 +82,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/docs/reference/mexdolphins.html b/docs/reference/mexdolphins.html
index 22c93f5..d3137f8 100644
--- a/docs/reference/mexdolphins.html
+++ b/docs/reference/mexdolphins.html
@@ -1,71 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Data from a combination of several NOAA shipboard surveys conducted on
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins"><meta name="description" content="Data from a combination of several NOAA shipboard surveys conducted on
 pan-tropical spotted dolphins in the Gulf of Mexico. The data consist of 47
 observations of groups of dolphins. The group size was recorded, as well as
 the Beaufort sea state at the time of the observation. Coordinates for each
 observation and bathymetry data were also available as covariates for the
 analysis. A complete example analysis (and description of the data) is
-provided at http://distancesampling.org/R/vignettes/mexico-analysis.html."><title>Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins"><meta property="og:description" content="Data from a combination of several NOAA shipboard surveys conducted on
+provided at http://distancesampling.org/R/vignettes/mexico-analysis.html."><meta property="og:description" content="Data from a combination of several NOAA shipboard surveys conducted on
 pan-tropical spotted dolphins in the Gulf of Mexico. The data consist of 47
 observations of groups of dolphins. The group size was recorded, as well as
 the Beaufort sea state at the time of the observation. Coordinates for each
 observation and bathymetry data were also available as covariates for the
 analysis. A complete example analysis (and description of the data) is
-provided at http://distancesampling.org/R/vignettes/mexico-analysis.html."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Pan-tropical spotted dolphins in the Gulf of Mexico</h1>
+
+      <h1>Pan-tropical spotted dolphins in the Gulf of Mexico</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dsm-package.R" class="external-link"><code>R/dsm-package.R</code></a></small>
       <div class="d-none name"><code>mexdolphins.Rd</code></div>
     </div>
@@ -101,14 +80,14 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/obs_exp.html b/docs/reference/obs_exp.html
index 111c6c3..d0b80c0 100644
--- a/docs/reference/obs_exp.html
+++ b/docs/reference/obs_exp.html
@@ -1,63 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Given a covariate, calculate the observed and expected counts for each
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Observed versus expected diagnostics for fitted DSMs — obs_exp • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Observed versus expected diagnostics for fitted DSMs — obs_exp"><meta name="description" content="Given a covariate, calculate the observed and expected counts for each
 unique value of the covariate. This can be a useful goodness of fit check
-for DSMs."><title>Observed versus expected diagnostics for fitted DSMs — obs_exp • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Observed versus expected diagnostics for fitted DSMs — obs_exp"><meta property="og:description" content="Given a covariate, calculate the observed and expected counts for each
+for DSMs."><meta property="og:description" content="Given a covariate, calculate the observed and expected counts for each
 unique value of the covariate. This can be a useful goodness of fit check
-for DSMs."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+for DSMs."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Observed versus expected diagnostics for fitted DSMs</h1>
+
+      <h1>Observed versus expected diagnostics for fitted DSMs</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/obs_exp.R" class="external-link"><code>R/obs_exp.R</code></a></small>
       <div class="d-none name"><code>obs_exp.Rd</code></div>
     </div>
@@ -75,24 +54,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>model</dt>
+
+
+<dl><dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>a fitted <code><a href="dsm.html">dsm</a></code> model object</p></dd>
 
 
-<dt>covar</dt>
+<dt id="arg-covar">covar<a class="anchor" aria-label="anchor" href="#arg-covar"></a></dt>
 <dd><p>covariate to aggregate by (<code>character</code>)</p></dd>
 
 
-<dt>cut</dt>
+<dt id="arg-cut">cut<a class="anchor" aria-label="anchor" href="#arg-cut"></a></dt>
 <dd><p>vector of cut points to aggregate at. If not supplied, the unique
 values of <code>covar</code> are used.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p><code>data.frame</code> with values of observed and expected counts.</p>
+    <p><code>data.frame</code> with values of observed and expected counts.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -107,7 +86,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -116,10 +95,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
 <span class="r-in"><span>               key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">mod1</span> <span class="op">&lt;-</span> <span class="fu"><a href="dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">hr.model</span>, <span class="va">segdata</span>, <span class="va">obsdata</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -128,14 +107,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/plot.dsm.html b/docs/reference/plot.dsm.html
index 6bcb323..efb5e8a 100644
--- a/docs/reference/plot.dsm.html
+++ b/docs/reference/plot.dsm.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="See plot.gam."><title>Plot a density surface model. — plot.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot a density surface model. — plot.dsm"><meta property="og:description" content="See plot.gam."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
-    <span class="fa fa-twitter"></span>
-     
-  </a>
-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Plot a density surface model.</h1>
+
+      <h1>Plot a density surface model.</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/plot.dsm.R" class="external-link"><code>R/plot.dsm.R</code></a></small>
       <div class="d-none name"><code>plot.dsm.Rd</code></div>
     </div>
@@ -64,25 +43,25 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>a model fitted by <code><a href="dsm.html">dsm</a></code></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>other arguments passed to <code><a href="https://rdrr.io/pkg/mgcv/man/plot.gam.html" class="external-link">plot.gam</a></code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a plot!</p>
+    <p>a plot!</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -93,7 +72,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -102,14 +81,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/plot.dsm.var.html b/docs/reference/plot.dsm.var.html
index 7423568..82b6ad5 100644
--- a/docs/reference/plot.dsm.var.html
+++ b/docs/reference/plot.dsm.var.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Note that the prediction data set must have x and y columns even if
-these were not used in the model."><title>Create plots of abundance uncertainty — plot.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create plots of abundance uncertainty — plot.dsm.var"><meta property="og:description" content="Note that the prediction data set must have x and y columns even if
-these were not used in the model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create plots of abundance uncertainty — plot.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create plots of abundance uncertainty — plot.dsm.var"><meta name="description" content="Note that the prediction data set must have x and y columns even if
+these were not used in the model."><meta property="og:description" content="Note that the prediction data set must have x and y columns even if
+these were not used in the model."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Create plots of abundance uncertainty</h1>
+
+      <h1>Create plots of abundance uncertainty</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/plot.dsm.var.R" class="external-link"><code>R/plot.dsm.var.R</code></a></small>
       <div class="d-none name"><code>plot.dsm.var.Rd</code></div>
     </div>
@@ -67,7 +46,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm.var</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm.var'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  poly <span class="op">=</span> <span class="cn">NULL</span>,</span>
@@ -88,75 +67,75 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>a <code>dsm.var</code> object</p></dd>
 
 
-<dt>poly</dt>
+<dt id="arg-poly">poly<a class="anchor" aria-label="anchor" href="#arg-poly"></a></dt>
 <dd><p>a <code>list</code> or <code>data.frame</code> with columns <code>x</code> and <code>y</code>, which gives
 the coordinates of a polygon to draw. It may also optionally have a column
 <code>group</code>, if there are many polygons.</p></dd>
 
 
-<dt>limits</dt>
+<dt id="arg-limits">limits<a class="anchor" aria-label="anchor" href="#arg-limits"></a></dt>
 <dd><p>limits for the fill colours</p></dd>
 
 
-<dt>breaks</dt>
+<dt id="arg-breaks">breaks<a class="anchor" aria-label="anchor" href="#arg-breaks"></a></dt>
 <dd><p>breaks for the colour fill</p></dd>
 
 
-<dt>legend.breaks</dt>
+<dt id="arg-legend-breaks">legend.breaks<a class="anchor" aria-label="anchor" href="#arg-legend-breaks"></a></dt>
 <dd><p>breaks as they should be displayed</p></dd>
 
 
-<dt>xlab</dt>
+<dt id="arg-xlab">xlab<a class="anchor" aria-label="anchor" href="#arg-xlab"></a></dt>
 <dd><p>label for the <code>x</code> axis</p></dd>
 
 
-<dt>ylab</dt>
+<dt id="arg-ylab">ylab<a class="anchor" aria-label="anchor" href="#arg-ylab"></a></dt>
 <dd><p>label for the <code>y</code> axis</p></dd>
 
 
-<dt>observations</dt>
+<dt id="arg-observations">observations<a class="anchor" aria-label="anchor" href="#arg-observations"></a></dt>
 <dd><p>should observations be plotted?</p></dd>
 
 
-<dt>plot</dt>
+<dt id="arg-plot">plot<a class="anchor" aria-label="anchor" href="#arg-plot"></a></dt>
 <dd><p>actually plot the map, or just return a <code>ggplot2</code> object?</p></dd>
 
 
-<dt>boxplot.coef</dt>
+<dt id="arg-boxplot-coef">boxplot.coef<a class="anchor" aria-label="anchor" href="#arg-boxplot-coef"></a></dt>
 <dd><p>control trimming (as in
 <code><a href="summary.dsm.var.html">summary.dsm.var</a></code>), only has an effect if the bootstrap
 file was saved.</p></dd>
 
 
-<dt>x.name</dt>
+<dt id="arg-x-name">x.name<a class="anchor" aria-label="anchor" href="#arg-x-name"></a></dt>
 <dd><p>name of the variable to plot as the <code>x</code> axis.</p></dd>
 
 
-<dt>y.name</dt>
+<dt id="arg-y-name">y.name<a class="anchor" aria-label="anchor" href="#arg-y-name"></a></dt>
 <dd><p>name of the variable to plot as the <code>y</code> axis.</p></dd>
 
 
-<dt>gg.grad</dt>
+<dt id="arg-gg-grad">gg.grad<a class="anchor" aria-label="anchor" href="#arg-gg-grad"></a></dt>
 <dd><p>optional <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></code> gradient object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>any other arguments</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a plot</p>
+    <p>a plot</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    
+
 
 
 <p>In order to get plotting to work with <code><a href="dsm_var_prop.html">dsm_var_prop</a></code> and
@@ -177,7 +156,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -186,14 +165,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/plot_pred_by_term.html b/docs/reference/plot_pred_by_term.html
index 08a8fe0..c5a4f46 100644
--- a/docs/reference/plot_pred_by_term.html
+++ b/docs/reference/plot_pred_by_term.html
@@ -1,63 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Plot the effect of each smooth in the model spatially. For each term in the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Spatially plot predictions per model term — plot_pred_by_term • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Spatially plot predictions per model term — plot_pred_by_term"><meta name="description" content="Plot the effect of each smooth in the model spatially. For each term in the
 model, plot its effect in space. Plots are made on the same scale, so that
-the relative influence of each smooth can be seen."><title>Spatially plot predictions per model term — plot_pred_by_term • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Spatially plot predictions per model term — plot_pred_by_term"><meta property="og:description" content="Plot the effect of each smooth in the model spatially. For each term in the
+the relative influence of each smooth can be seen."><meta property="og:description" content="Plot the effect of each smooth in the model spatially. For each term in the
 model, plot its effect in space. Plots are made on the same scale, so that
-the relative influence of each smooth can be seen."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
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+
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+  </ul></li>
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+
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Spatially plot predictions per model term</h1>
+
+      <h1>Spatially plot predictions per model term</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/plot_pred_by_term.R" class="external-link"><code>R/plot_pred_by_term.R</code></a></small>
       <div class="d-none name"><code>plot_pred_by_term.Rd</code></div>
     </div>
@@ -75,24 +54,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dsm.obj</dt>
+
+
+<dl><dt id="arg-dsm-obj">dsm.obj<a class="anchor" aria-label="anchor" href="#arg-dsm-obj"></a></dt>
 <dd><p>fitted <code><a href="dsm.html">dsm</a></code> object</p></dd>
 
 
-<dt>data</dt>
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>data to use to plot (often the same as the prediction grid), data
 should also include <code>width</code> and <code>height</code> columns for plotting</p></dd>
 
 
-<dt>location.cov</dt>
+<dt id="arg-location-cov">location.cov<a class="anchor" aria-label="anchor" href="#arg-location-cov"></a></dt>
 <dd><p>deprecated, use <code>location.cov</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a <code>ggplot2</code> plot</p>
+    <p>a <code>ggplot2</code> plot</p>
     </div>
     <div class="section level2">
     <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
@@ -101,7 +80,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -119,10 +98,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># better plot would be</span></span></span>
 <span class="r-in"><span><span class="co"># library(viridis)</span></span></span>
 <span class="r-in"><span><span class="co"># plot_pred_by_term(mod1, preddata, c("x","y")) + scale_fill_viridis()</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -131,14 +110,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/predict.dsm.html b/docs/reference/predict.dsm.html
index c3a9a49..dd0b705 100644
--- a/docs/reference/predict.dsm.html
+++ b/docs/reference/predict.dsm.html
@@ -1,61 +1,40 @@
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+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Predict from a fitted density surface model</h1>
+
+      <h1>Predict from a fitted density surface model</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/predict.dsm.R" class="external-link"><code>R/predict.dsm.R</code></a></small>
       <div class="d-none name"><code>predict.dsm.Rd</code></div>
     </div>
@@ -67,44 +46,44 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, off.set <span class="op">=</span> <span class="cn">NULL</span>, type <span class="op">=</span> <span class="st">"response"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>a fitted <code><a href="dsm.html">dsm</a></code> object</p></dd>
 
 
-<dt>newdata</dt>
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
 <dd><p>spatially referenced covariates e.g. altitude, depth,
 distance to shore, etc. Covariates in the <code>data.frame</code> must have names
 <em>identical</em> to variable names used in fitting the model</p></dd>
 
 
-<dt>off.set</dt>
+<dt id="arg-off-set">off.set<a class="anchor" aria-label="anchor" href="#arg-off-set"></a></dt>
 <dd><p>area of each of the cells in the prediction grid. Should be
 in the same units as the segments/distances given to <code>dsm</code>. Replaces the
 column in <code>newdata</code> called <code>off.set</code> if it is supplied. Ignored if <code>newdata</code>
 is not supplied</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>what scale should the results be on. The default is
 <code>"response"</code>, see <code><a href="https://rdrr.io/pkg/mgcv/man/predict.gam.html" class="external-link">predict.gam</a></code> for an explanation of
 other options (usually not necessary)</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>any other arguments passed to <code><a href="https://rdrr.io/pkg/mgcv/man/predict.gam.html" class="external-link">predict.gam</a></code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>predicted values on the response scale by default (unless <code>type</code> is
+    <p>predicted values on the response scale by default (unless <code>type</code> is
 specified, in which case see <code><a href="https://rdrr.io/pkg/mgcv/man/predict.gam.html" class="external-link">predict.gam</a></code>).</p>
     </div>
     <div class="section level2">
@@ -128,7 +107,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -137,14 +116,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/predict.fake_ddf.html b/docs/reference/predict.fake_ddf.html
index 574e46a..55d80ac 100644
--- a/docs/reference/predict.fake_ddf.html
+++ b/docs/reference/predict.fake_ddf.html
@@ -1,63 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Prediction function for dummy detection functions. The function returns as
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for fake detection functions — predict.fake_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for fake detection functions — predict.fake_ddf"><meta name="description" content="Prediction function for dummy detection functions. The function returns as
 many 1s as there are rows in newdata. If esw=TRUE then the
-strip width is returned."><title>Prediction for fake detection functions — predict.fake_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for fake detection functions — predict.fake_ddf"><meta property="og:description" content="Prediction function for dummy detection functions. The function returns as
+strip width is returned."><meta property="og:description" content="Prediction function for dummy detection functions. The function returns as
 many 1s as there are rows in newdata. If esw=TRUE then the
-strip width is returned."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+strip width is returned."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter">
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-</li>
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Prediction for fake detection functions</h1>
+
+      <h1>Prediction for fake detection functions</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dummy_ddf.R" class="external-link"><code>R/dummy_ddf.R</code></a></small>
       <div class="d-none name"><code>predict.fake_ddf.Rd</code></div>
     </div>
@@ -70,7 +49,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for fake_ddf</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'fake_ddf'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  newdata <span class="op">=</span> <span class="cn">NULL</span>,</span>
@@ -83,27 +62,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>model object</p></dd>
 
 
-<dt>newdata</dt>
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
 <dd><p>how many 1s should we return?</p></dd>
 
 
-<dt>compute</dt>
+<dt id="arg-compute">compute<a class="anchor" aria-label="anchor" href="#arg-compute"></a></dt>
 <dd><p>unused, compatibility with <code><a href="https://rdrr.io/pkg/mrds/man/predict.ds.html" class="external-link">mrds::predict</a></code></p></dd>
 
 
-<dt>int.range</dt>
+<dt id="arg-int-range">int.range<a class="anchor" aria-label="anchor" href="#arg-int-range"></a></dt>
 <dd><p>unused, compatibility with <code><a href="https://rdrr.io/pkg/mrds/man/predict.ds.html" class="external-link">mrds::predict</a></code></p></dd>
 
 
-<dt>esw</dt>
+<dt id="arg-esw">esw<a class="anchor" aria-label="anchor" href="#arg-esw"></a></dt>
 <dd><p>should the strip width be returned?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>for S3 consistency</p></dd>
 
 </dl></div>
@@ -112,7 +93,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -121,14 +102,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/print.dsm.html b/docs/reference/print.dsm.html
index 5ee1e9b..4d29578 100644
--- a/docs/reference/print.dsm.html
+++ b/docs/reference/print.dsm.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This method just gives a short description of the fitted model. Use the
-summary.dsm method for more information."><title>Print a description of a density surface model object — print.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model object — print.dsm"><meta property="og:description" content="This method just gives a short description of the fitted model. Use the
-summary.dsm method for more information."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model object — print.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model object — print.dsm"><meta name="description" content="This method just gives a short description of the fitted model. Use the
+summary.dsm method for more information."><meta property="og:description" content="This method just gives a short description of the fitted model. Use the
+summary.dsm method for more information."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
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-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Print a description of a density surface model object</h1>
+
+      <h1>Print a description of a density surface model object</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.dsm.R" class="external-link"><code>R/print.dsm.R</code></a></small>
       <div class="d-none name"><code>print.dsm.Rd</code></div>
     </div>
@@ -67,34 +46,32 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>a model fitted by <code><a href="dsm.html">dsm</a></code></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>unspecified and unused arguments for S3 consistency</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p><code>NULL</code></p>
-
-
+    <p><code>NULL</code></p>
     </div>
     <div class="section level2">
     <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -103,14 +80,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/print.dsm.var.html b/docs/reference/print.dsm.var.html
index 755a8ba..2eebbe4 100644
--- a/docs/reference/print.dsm.var.html
+++ b/docs/reference/print.dsm.var.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This method only provides a short summary, use the
-summary.dsm.var method for information."><title>Print a description of a density surface model variance object — print.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm.var"><meta property="og:description" content="This method only provides a short summary, use the
-summary.dsm.var method for information."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model variance object — print.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm.var"><meta name="description" content="This method only provides a short summary, use the
+summary.dsm.var method for information."><meta property="og:description" content="This method only provides a short summary, use the
+summary.dsm.var method for information."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
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     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
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-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Print a description of a density surface model variance object</h1>
+
+      <h1>Print a description of a density surface model variance object</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.dsm.var.R" class="external-link"><code>R/print.dsm.var.R</code></a></small>
       <div class="d-none name"><code>print.dsm.var.Rd</code></div>
     </div>
@@ -67,27 +46,25 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm.var</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm.var'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>a <code>dsm</code> variance object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>unspecified and unused arguments for S3 consistency</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p><code>NULL</code></p>
-
-
+    <p><code>NULL</code></p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -98,7 +75,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -107,14 +84,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/print.dsm_varprop.html b/docs/reference/print.dsm_varprop.html
index 4148aa8..0f96ea3 100644
--- a/docs/reference/print.dsm_varprop.html
+++ b/docs/reference/print.dsm_varprop.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This method only provides a short summary, see
-summary.dsm_varprop."><title>Print a description of a density surface model variance object — print.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm_varprop"><meta property="og:description" content="This method only provides a short summary, see
-summary.dsm_varprop."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model variance object — print.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm_varprop"><meta name="description" content="This method only provides a short summary, see
+summary.dsm_varprop."><meta property="og:description" content="This method only provides a short summary, see
+summary.dsm_varprop."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
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-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Print a description of a density surface model variance object</h1>
+
+      <h1>Print a description of a density surface model variance object</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.dsm_varprop.R" class="external-link"><code>R/print.dsm_varprop.R</code></a></small>
       <div class="d-none name"><code>print.dsm_varprop.Rd</code></div>
     </div>
@@ -67,17 +46,19 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm_varprop</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm_varprop'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>a <code>dsm</code> variance object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>unspecified and unused arguments for S3 consistency</p></dd>
 
 </dl></div>
@@ -90,7 +71,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -99,14 +80,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/docs/reference/print.summary.dsm.var.html b/docs/reference/print.summary.dsm.var.html
index c6f0422..eb45394 100644
--- a/docs/reference/print.summary.dsm.var.html
+++ b/docs/reference/print.summary.dsm.var.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="See summary.dsm.var for information."><title>Print summary of density surface model variance object — print.summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm.var"><meta property="og:description" content="See summary.dsm.var for information."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print summary of density surface model variance object — print.summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm.var"><meta name="description" content="See summary.dsm.var for information."><meta property="og:description" content="See summary.dsm.var for information."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Print summary of density surface model variance object</h1>
+
+      <h1>Print summary of density surface model variance object</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.summary.dsm.var.R" class="external-link"><code>R/print.summary.dsm.var.R</code></a></small>
       <div class="d-none name"><code>print.summary.dsm.var.Rd</code></div>
     </div>
@@ -64,27 +43,25 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for summary.dsm.var</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'summary.dsm.var'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>a summary of <code>dsm</code> variance object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>unspecified and unused arguments for S3 consistency</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p><code>NULL</code></p>
-
-
+    <p><code>NULL</code></p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -95,7 +72,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -104,14 +81,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/print.summary.dsm_varprop.html b/docs/reference/print.summary.dsm_varprop.html
index c00d93a..af00fe1 100644
--- a/docs/reference/print.summary.dsm_varprop.html
+++ b/docs/reference/print.summary.dsm_varprop.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="See summary.dsm_varprop for information."><title>Print summary of density surface model variance object — print.summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm_varprop"><meta property="og:description" content="See summary.dsm_varprop for information."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print summary of density surface model variance object — print.summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm_varprop"><meta name="description" content="See summary.dsm_varprop for information."><meta property="og:description" content="See summary.dsm_varprop for information."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-</li>
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-    <span class="fab fa fab fa-github fa-lg"></span>
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Print summary of density surface model variance object</h1>
+
+      <h1>Print summary of density surface model variance object</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/print.summary.dsm_varprop.R" class="external-link"><code>R/print.summary.dsm_varprop.R</code></a></small>
       <div class="d-none name"><code>print.summary.dsm_varprop.Rd</code></div>
     </div>
@@ -64,27 +43,25 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for summary.dsm_varprop</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'summary.dsm_varprop'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>a summary of <code>dsm</code> variance object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>unspecified and unused arguments for S3 consistency</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p><code>NULL</code></p>
-
-
+    <p><code>NULL</code></p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -95,7 +72,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -104,14 +81,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/rqgam.check.html b/docs/reference/rqgam.check.html
index 794f623..c7d2b1d 100644
--- a/docs/reference/rqgam.check.html
+++ b/docs/reference/rqgam.check.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use rqgam_check."><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check"><meta property="og:description" content="This function is deprecated, use rqgam_check."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check"><meta name="description" content="This function is deprecated, use rqgam_check."><meta property="og:description" content="This function is deprecated, use rqgam_check."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Randomised quantile residuals check plot for GAMs/DSMs</h1>
+
+      <h1>Randomised quantile residuals check plot for GAMs/DSMs</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
       <div class="d-none name"><code>rqgam.check.Rd</code></div>
     </div>
@@ -69,17 +48,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>gam.obj</dt>
+
+
+<dl><dt id="arg-gam-obj">gam.obj<a class="anchor" aria-label="anchor" href="#arg-gam-obj"></a></dt>
 <dd><p>a <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code> or <code><a href="dsm.html">dsm</a></code>
 object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arguments passed on to all plotting functions</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -88,14 +69,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/rqgam_check.html b/docs/reference/rqgam_check.html
index 3a24f14..0cd97ad 100644
--- a/docs/reference/rqgam_check.html
+++ b/docs/reference/rqgam_check.html
@@ -1,69 +1,48 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Reproduces the "Resids vs. linear pred" plot from
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check"><meta name="description" content='Reproduces the "Resids vs. linear pred" plot from
 gam.check but using randomised quantile residuals, a la
 Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking
 for "funnel"-type structures in the points, which is much easier with
 randomised quantile residuals than with deviance residuals, when your model
-uses a count distribution as the response.'><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check"><meta property="og:description" content='Reproduces the "Resids vs. linear pred" plot from
+uses a count distribution as the response.'><meta property="og:description" content='Reproduces the "Resids vs. linear pred" plot from
 gam.check but using randomised quantile residuals, a la
 Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking
 for "funnel"-type structures in the points, which is much easier with
 randomised quantile residuals than with deviance residuals, when your model
-uses a count distribution as the response.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+uses a count distribution as the response.'></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Randomised quantile residuals check plot for GAMs/DSMs</h1>
+
+      <h1>Randomised quantile residuals check plot for GAMs/DSMs</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/rqgam_check.R" class="external-link"><code>R/rqgam_check.R</code></a></small>
       <div class="d-none name"><code>rqgam_check.Rd</code></div>
     </div>
@@ -84,20 +63,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>gam.obj</dt>
+
+
+<dl><dt id="arg-gam-obj">gam.obj<a class="anchor" aria-label="anchor" href="#arg-gam-obj"></a></dt>
 <dd><p>a <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">gam</a></code>, <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></code> or <code><a href="dsm.html">dsm</a></code>
 object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arguments passed on to all plotting functions</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>just plots!</p>
+    <p>just plots!</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -127,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">hr.model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, truncation<span class="op">=</span><span class="fl">6000</span>,</span></span>
 <span class="r-in"><span>               key <span class="op">=</span> <span class="st">"hr"</span>, adjustment <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Fitting hazard-rate key function</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> AIC= 677.418</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> AIC= 677.433</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> No survey area information supplied, only estimating detection function.</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># fit a simple smooth of x and y with a Tweedie response with estimated</span></span></span>
@@ -139,7 +118,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/summary.dsm.html b/docs/reference/summary.dsm.html
index 8726352..8d72845 100644
--- a/docs/reference/summary.dsm.html
+++ b/docs/reference/summary.dsm.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Gives a brief summary of a fitted dsm object."><title>Summarize a fitted density surface model — summary.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize a fitted density surface model — summary.dsm"><meta property="og:description" content="Gives a brief summary of a fitted dsm object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize a fitted density surface model — summary.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize a fitted density surface model — summary.dsm"><meta name="description" content="Gives a brief summary of a fitted dsm object."><meta property="og:description" content="Gives a brief summary of a fitted dsm object."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
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       </ul></div>
 
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Summarize a fitted density surface model</h1>
+
+      <h1>Summarize a fitted density surface model</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/summary.dsm.R" class="external-link"><code>R/summary.dsm.R</code></a></small>
       <div class="d-none name"><code>summary.dsm.Rd</code></div>
     </div>
@@ -64,25 +43,25 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>a model fitted by <code><a href="dsm.html">dsm</a></code></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>other arguments passed to <code><a href="https://rdrr.io/pkg/mgcv/man/summary.gam.html" class="external-link">summary.gam</a></code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a summary object</p>
+    <p>a summary object</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -93,7 +72,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -102,14 +81,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/summary.dsm.var.html b/docs/reference/summary.dsm.var.html
index 432923c..1137279 100644
--- a/docs/reference/summary.dsm.var.html
+++ b/docs/reference/summary.dsm.var.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Gives a brief summary of a fitted dsm variance object."><title>Summarize the variance of a density surface model — summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm.var"><meta property="og:description" content="Gives a brief summary of a fitted dsm variance object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize the variance of a density surface model — summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm.var"><meta name="description" content="Gives a brief summary of a fitted dsm variance object."><meta property="og:description" content="Gives a brief summary of a fitted dsm variance object."></head><body>
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Summarize the variance of a density surface model</h1>
+
+      <h1>Summarize the variance of a density surface model</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/summary.dsm.var.R" class="external-link"><code>R/summary.dsm.var.R</code></a></small>
       <div class="d-none name"><code>summary.dsm.var.Rd</code></div>
     </div>
@@ -64,7 +43,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm.var</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm.var'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  alpha <span class="op">=</span> <span class="fl">0.05</span>,</span>
@@ -76,37 +55,37 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>a <code>dsm.var</code> object</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>alpha level for confidence intervals (default 0.05 to give a
 95\<!-- % confidence internal, i.e. we generate \code{100*c(alpha/2, 1-alpha/2)} -->
 confidence intervals)</p></dd>
 
 
-<dt>boxplot.coef</dt>
+<dt id="arg-boxplot-coef">boxplot.coef<a class="anchor" aria-label="anchor" href="#arg-boxplot-coef"></a></dt>
 <dd><p>the value of <code>coef</code> used to calculate the outliers see
 <code><a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></code>.</p></dd>
 
 
-<dt>bootstrap.subregions</dt>
+<dt id="arg-bootstrap-subregions">bootstrap.subregions<a class="anchor" aria-label="anchor" href="#arg-bootstrap-subregions"></a></dt>
 <dd><p>list of vectors of logicals or indices for
 subregions for which variances need to be calculated (only for bootstraps
 (see <code><a href="dsm.var.prop.html">dsm.var.prop</a></code> for how to use subregions with variance
 propagation).</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>unused arguments for S3 compatibility</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a summary object</p>
+    <p>a summary object</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -117,7 +96,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -126,14 +105,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/summary.dsm_varprop.html b/docs/reference/summary.dsm_varprop.html
index 76e417f..43e644d 100644
--- a/docs/reference/summary.dsm_varprop.html
+++ b/docs/reference/summary.dsm_varprop.html
@@ -1,61 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Gives a brief summary of a fitted dsm_varprop variance
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-object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize the variance of a density surface model — summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm_varprop"><meta name="description" content="Gives a brief summary of a fitted dsm_varprop variance
+object."><meta property="og:description" content="Gives a brief summary of a fitted dsm_varprop variance
+object."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
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       </ul></div>
 
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+
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Summarize the variance of a density surface model</h1>
+
+      <h1>Summarize the variance of a density surface model</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/summary.dsm_varprop.R" class="external-link"><code>R/summary.dsm_varprop.R</code></a></small>
       <div class="d-none name"><code>summary.dsm_varprop.Rd</code></div>
     </div>
@@ -67,30 +46,30 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dsm_varprop</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dsm_varprop'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, alpha <span class="op">=</span> <span class="fl">0.05</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>a <code>dsm.var</code> object</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>alpha level for confidence intervals (default 0.05 to give a
 95% confidence internal)</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>unused arguments for S3 compatibility</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a summary object</p>
+    <p>a summary object</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -101,7 +80,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <p>David L. Miller</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -110,14 +89,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/trim.var.html b/docs/reference/trim.var.html
index f41013f..ff21ce2 100644
--- a/docs/reference/trim.var.html
+++ b/docs/reference/trim.var.html
@@ -1,63 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Trim the variance estimates from the bootstrap. This is defined as the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Trimmed variance — trim.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Trimmed variance — trim.var"><meta name="description" content="Trim the variance estimates from the bootstrap. This is defined as the
 percentage defined as amount necessary to bring median and trimmed mean
-within 8% of each other these are defined as 'outliers'."><title>Trimmed variance — trim.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Trimmed variance — trim.var"><meta property="og:description" content="Trim the variance estimates from the bootstrap. This is defined as the
+within 8% of each other these are defined as 'outliers'."><meta property="og:description" content="Trim the variance estimates from the bootstrap. This is defined as the
 percentage defined as amount necessary to bring median and trimmed mean
-within 8% of each other these are defined as 'outliers'."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+within 8% of each other these are defined as 'outliers'."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
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-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
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-</li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Trimmed variance</h1>
+
+      <h1>Trimmed variance</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/trim.var.R" class="external-link"><code>R/trim.var.R</code></a></small>
       <div class="d-none name"><code>trim.var.Rd</code></div>
     </div>
@@ -75,28 +54,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>untrimmed.bootstraps</dt>
+
+
+<dl><dt id="arg-untrimmed-bootstraps">untrimmed.bootstraps<a class="anchor" aria-label="anchor" href="#arg-untrimmed-bootstraps"></a></dt>
 <dd><p>(usually the <code>$study.area.total</code> element of a
 returned <code><a href="dsm.var.movblk.html">dsm.var.movblk</a></code> bootstrap object.</p></dd>
 
 
-<dt>boxplot.coef</dt>
+<dt id="arg-boxplot-coef">boxplot.coef<a class="anchor" aria-label="anchor" href="#arg-boxplot-coef"></a></dt>
 <dd><p>the value of <code>coef</code> used to calculate the outliers see
 <code><a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>trimmed variance</p>
+    <p>trimmed variance</p>
     </div>
     <div class="section level2">
     <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Louise Burt</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -105,14 +84,14 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/vis.concurvity.html b/docs/reference/vis.concurvity.html
index acbf75c..4f84ce4 100644
--- a/docs/reference/vis.concurvity.html
+++ b/docs/reference/vis.concurvity.html
@@ -1,59 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function is deprecated, use vis_concurvity."><title>Visualise concurvity between terms in a GAM — vis.concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis.concurvity"><meta property="og:description" content="This function is deprecated, use vis_concurvity."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualise concurvity between terms in a GAM — vis.concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis.concurvity"><meta name="description" content="This function is deprecated, use vis_concurvity."><meta property="og:description" content="This function is deprecated, use vis_concurvity."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a>
-    <a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/DistanceDevelopment/dsm/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Visualise concurvity between terms in a GAM</h1>
+
+      <h1>Visualise concurvity between terms in a GAM</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/dead_functions.R" class="external-link"><code>R/dead_functions.R</code></a></small>
       <div class="d-none name"><code>vis.concurvity.Rd</code></div>
     </div>
@@ -69,16 +48,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>model</dt>
+
+
+<dl><dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>fitted model</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>concurvity measure to plot, see <code><a href="https://rdrr.io/pkg/mgcv/man/concurvity.html" class="external-link">concurvity</a></code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -87,14 +68,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/vis_concurvity.html b/docs/reference/vis_concurvity.html
index cea59cf..a385bbf 100644
--- a/docs/reference/vis_concurvity.html
+++ b/docs/reference/vis_concurvity.html
@@ -1,65 +1,44 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Plot measures of how much one term in the model could be explained by
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualise concurvity between terms in a GAM — vis_concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis_concurvity"><meta name="description" content="Plot measures of how much one term in the model could be explained by
 another. When values are high, one should consider re-running variable
 selection with one of the offending variables removed to check for stability
-in term selection."><title>Visualise concurvity between terms in a GAM — vis_concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis_concurvity"><meta property="og:description" content="Plot measures of how much one term in the model could be explained by
+in term selection."><meta property="og:description" content="Plot measures of how much one term in the model could be explained by
 another. When values are high, one should consider re-running variable
 selection with one of the offending variables removed to check for stability
-in term selection."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+in term selection."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">dsm</a>
 
     <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.3.3.9001</small>
 
-    
+
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_format/dsm-data-formatting.html">`dsm` data formatting example</a></li>
+    <li><a class="dropdown-item" href="../articles/lines_gomex/mexico-analysis.html">Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://twitter.com/distancesamp" aria-label="Twitter"><span class="fa fa-twitter"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Visualise concurvity between terms in a GAM</h1>
+
+      <h1>Visualise concurvity between terms in a GAM</h1>
       <small class="dont-index">Source: <a href="https://github.com/DistanceDevelopment/dsm/blob/HEAD/R/vis_concurvity.R" class="external-link"><code>R/vis_concurvity.R</code></a></small>
       <div class="d-none name"><code>vis_concurvity.Rd</code></div>
     </div>
@@ -78,11 +57,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>model</dt>
+
+
+<dl><dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>fitted model</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>concurvity measure to plot, see <code><a href="https://rdrr.io/pkg/mgcv/man/concurvity.html" class="external-link">concurvity</a></code></p></dd>
 
 </dl></div>
@@ -99,7 +80,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/dsm" class="external-link">dsm</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -115,10 +96,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># visualise concurvity using the "estimate" metric</span></span></span>
 <span class="r-in"><span><span class="fu">vis_concurvity</span><span class="op">(</span><span class="va">mod1</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -127,14 +108,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>If you wish to donate to development and maintenance</p>
+  <p>If you wish to donate to development and maintenance, please <a href="mailto:distance@st-andrews.ac.uk">email us</a>.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/search.json b/docs/search.json
index 79b3f0b..09d1db7 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"/articles/data_format/dsm-data-formatting.html","id":"introduction","dir":"Articles > Data_format","previous_headings":"","what":"Introduction","title":"`dsm` data formatting example","text":"dsm package expects data specific format. example ’ll show get data format give explanation requirements exist. recommend getting grips fitting models dsm reading tutorial know end point look like trying grapple data. example Gulf Mexico pantropical spotted dolphins available .","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"prerequisites","dir":"Articles > Data_format","previous_headings":"","what":"Prerequisites","title":"`dsm` data formatting example","text":"run code , need ggplot2 sf libraries installed. can following code: Rhode Island data required downloaded : loon observations coastline dbf coastline projection coastline shapefile coastline shx transects dbf transects projection transects shapefile transects shx placed folder called data code work.","code":"install.packages(c(\"ggplot2\", \"sf\"))"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"overview","dir":"Articles > Data_format","previous_headings":"","what":"Overview","title":"`dsm` data formatting example","text":"collect distance sampling data, need record (1) distance observation (along detectability covariates like weather), (2) transect currently , (3) much effort expended transect (length number visits). want fit DSM information needs expanded include transect , can build spatial model. point transects, simple always point, line transects need know position detection (equivalently, far along transect ). abstractly, need information detections effort. also need link two. dsm use three objects hold information: fitted detection function (argument ddf.obj) holds detections ignores spatial information “segments” (argument segment.data) holds chunks effort (parts transect point visits) locations. might also include environmental covariate information. table linking two (argument observation.data), making sure detection corresponding segment. image gives overview different data.frames interact, along corresponds real life situation collecting data transect. rest example goes table turn, explaining construction.","code":""},{"path":[]},{"path":"/articles/data_format/dsm-data-formatting.html","id":"detection-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Detection data","title":"`dsm` data formatting example","text":"data requirements similar packages mrds Distance. cases, detection corresponding row. analysis using dsm additional requirements data (can auto-generated Distance recommend ensure know records link correctly). Distance need: distance : distance detection object : unique detection identifier (link observation table) size : number individuals detection (1 objects occur alone) mrds requires following extra columns (allow double observer surveys): detected : 1 detected observer 0 missed (always 1 single observer) observer : observer number (1 2) (always 1 single observer) covariates affect detectability recorded (e.g., sex weather) can included data analysis mrds Distance. model fitted, deal fitted model object dsm analysis.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segment-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Segment data","title":"`dsm` data formatting example","text":"use term “segment” refer points point transects small chunks transect line transect case. ’ll assume ’ve already segmented lines first describing data format, go example segment line transect data. data.frame required segments needs following columns: Effort : effort expended segment (either length segment lines, number visits points) Sample.Label : unique identifier segment (engaged complicated survey, might take format like “YEAR-AREA-LINE-SEGMENT”, labels like “2020-North--15”, can useful keep track data came later). addition, environmental covariates like location relevant covariates (sea surface temperature, foliage type, altitude, bathymetry, etc) can included data.frame used spatial model.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segmenting","dir":"Articles > Data_format","previous_headings":"Table construction > Segment data","what":"Segmenting","title":"`dsm` data formatting example","text":"data lines want chop segments, section give sample R code . things work extremely dependent input data, hopefully gives template want least. jump : fluent ArcGIS QGIS recommend task software familiar first. simple guide task QGIS ArcGIS recommend MGET toolbox. example, look aerial survey seabirds coast Rhode Island, USA. Data surveys collected University Rhode Island analysed K. Winiarski, Miller, Paton, & McWilliams (2013), K. J. Winiarski, Burt, et al. (2014) K. J. Winiarski, Miller, Paton, & McWilliams (2014). transects saved shapefile “chop-” segments. start let’s plot data coastline:  code loaded sf package. package use spatial work segmenting data. tools expected GIS, though package still developed functionality always increasing. Investigating transects data loaded, can see consists data.frame extra bits (spatial information, including locations (geometry) projection (CRS)): one row per transect (total 24 transects), consists line joining start end points make transects. want can plot individual rows via plot(transects[1,]); can useful check row single line, processing needed (can tricky covered tutorial see “information” ). data looks good shape, can use st_segmentize function take transect make segments . make sure everything works correctly, need project data first. ’m using appropriate projected coordinate system (EPSG code 6348), Rhode Island State Plane. know truncation used detection function 1000m (distances collected bins bin), since ’re trying make segments approximately square, set length twice (since truncation applies either side transect), 2000m. Looking : See now row many coordinates attached , just looking first row (transect 1) comparing coordinates transects segs transects just two coordinates (start end points line). Whereas: segs 5, corresponding segment cut points. now need break rows segs multiple rows, one per segment. bit fiddly. use function provided dieghernan site . first load function (don’t worry understanding code!): can use separate segments look results: now 590 segments 1km 2km long. can check lengths using st_length function plot histogram segment lengths:  Note setting dfMaxLength argument st_segmentize giving rough guide segment length algorithm inside st_segmentize tries get segments equal length possible (note \\(x\\) axis histogram). Note also Transect column segs now duplicate entries transect multiple segments . can now create required columns dsm. First, Effort column can generated using st_length: can generate Sample.Labels. ’ll use complicated naming scheme show ’s done, one simply write segs$Sample.Label <- 1:nrow(segs) sufficient. Instead, like Sample.Label “YEAR-AREA-LINE-SEGMENT” scheme suggested . fancier ways , clarity, let’s use loop: Now can check looks right: required columns taken care , can also calculate centroids segments get locations segment use spatial model. Handily can use st_centroid one step keep data time. get warning centroids might calculated correctly latitude/longitude used. need project data first (Rhode Island State Plane) step using st_transform. Notice geometry type now POINT. can plot centroids previous map:  present, dsm package doesn’t know talk sf objects, final step need simplify segs regular data.frame, dsm can interpret. can extract coordinates centroids using st_coordinates append onto data.frame version object geometry dropped, like : can double check looks right: done. See next section link segments detections. want include spatial covariate information segment table, use (example) rerrdap obtain remotely-sensed data. situ data (.e., weather conditions recorded effort), can complicated require use summarize, see thread information deal situation.","code":"library(ggplot2) library(sf)  # coast data, just for reference coastline <- read_sf(\"data/ri_coast.shp\")  # our transect lines transects <- read_sf(\"data/ri_transects.shp\")  # now make the plot p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects, aes(colour=Transect)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal()  print(p) transects ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86581 41.25891) ##  2        2  (-71.82872 41.30738, -71.82145 41.2258) ##  3        3 (-71.78805 41.31562, -71.75676 40.94846) ##  4        4 (-71.74674 41.31728, -71.71394 40.92971) ##  5        5  (-71.70589 41.3241, -71.67156 40.91612) ##  6        6 (-71.66578 41.33943, -71.62329 40.82944) ##  7        7   (-71.5435 41.36397, -71.5064 40.91516) ##  8        8  (-71.5015 41.35994, -71.45266 40.82978) ##  9        9 (-71.46131 41.37378, -71.42458 40.92496) ## 10       10   (-71.421 41.38723, -71.37562 40.83641) ## # ℹ 16 more rows # project transects transects <- st_transform(transects, 6348)  # do the segmenting segs <- st_segmentize(transects, dfMaxLength=units::set_units(2000, \"metres\"))  # transform back to lat/long segs <- st_transform(segs, 4326) transects <- st_transform(transects, 4326) segs ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                                             geometry ##  *    <dbl>                                                     <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86826 41.2861, -71.86703 41.2725, -71.8658… ##  2        2 (-71.82872 41.30738, -71.82727 41.29106, -71.82581 41.27475, -71.82… ##  3        3 (-71.78805 41.31562, -71.78654 41.29813, -71.78505 41.28065, -71.78… ##  4        4 (-71.74674 41.31728, -71.74524 41.29967, -71.74373 41.28205, -71.74… ##  5        5 (-71.70589 41.3241, -71.70438 41.30636, -71.70288 41.28863, -71.701… ##  6        6 (-71.66578 41.33943, -71.6643 41.32185, -71.66283 41.30426, -71.661… ##  7        7 (-71.5435 41.36397, -71.542 41.34602, -71.54051 41.32807, -71.53901… ##  8        8 (-71.5015 41.35994, -71.49986 41.34227, -71.49821 41.3246, -71.4965… ##  9        9 (-71.46131 41.37378, -71.45983 41.35583, -71.45835 41.33788, -71.45… ## 10       10 (-71.421 41.38723, -71.41952 41.36946, -71.41804 41.3517, -71.41656… ## # ℹ 16 more rows st_coordinates(transects[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86581 41.25891  1 st_coordinates(segs[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86826 41.28610  1 ## [3,] -71.86703 41.27250  1 ## [4,] -71.86581 41.25891  1 stdh_cast_substring <- function(x, to = \"MULTILINESTRING\") {   ggg <- st_geometry(x)    if (!unique(st_geometry_type(ggg)) %in% c(\"POLYGON\", \"LINESTRING\")) {     stop(\"Input should be  LINESTRING or POLYGON\")   }   for (k in 1:length(st_geometry(ggg))) {     sub <- ggg[k]     geom <- lapply(       1:(length(st_coordinates(sub)[, 1]) - 1),       function(i)         rbind(           as.numeric(st_coordinates(sub)[i, 1:2]),           as.numeric(st_coordinates(sub)[i + 1, 1:2])         )     ) %>%       st_multilinestring() %>%       st_sfc()      if (k == 1) {       endgeom <- geom     }     else {       endgeom <- rbind(endgeom, geom)     }   }   endgeom <- endgeom %>% st_sfc(crs = st_crs(x))   if (class(x)[1] == \"sf\") {     endgeom <- st_set_geometry(x, endgeom)   }   if (to == \"LINESTRING\") {     endgeom <- endgeom %>% st_cast(\"LINESTRING\")   }   return(endgeom) } segs <- stdh_cast_substring(segs, to=\"LINESTRING\") ## Warning in st_cast.sf(., \"LINESTRING\"): repeating attributes for all ## sub-geometries for which they may not be constant segs ## Simple feature collection with 590 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1  (-71.86948 41.29969, -71.86826 41.2861) ##  2        1   (-71.86826 41.2861, -71.86703 41.2725) ##  3        1  (-71.86703 41.2725, -71.86581 41.25891) ##  4        2 (-71.82872 41.30738, -71.82727 41.29106) ##  5        2 (-71.82727 41.29106, -71.82581 41.27475) ##  6        2 (-71.82581 41.27475, -71.82436 41.25843) ##  7        2  (-71.82436 41.25843, -71.8229 41.24212) ##  8        2   (-71.8229 41.24212, -71.82145 41.2258) ##  9        3 (-71.78805 41.31562, -71.78654 41.29813) ## 10        3 (-71.78654 41.29813, -71.78505 41.28065) ## # ℹ 580 more rows hist(st_length(segs), xlab=\"Segment lengths (m)\", main=\"\") segs$Effort <- st_length(segs) # create a dummy column that we can fill in as we go segs$Sample.Label <- NA  # set the year and area once for this data year <- 2010 area <- \"RI\"  # loop over the transect IDs for(this_transect in unique(segs$Transect)){   # how many segments in this transect?   n_segs <- nrow(subset(segs, Transect==this_transect))   # generate the n_segs labels that we need   segs$Sample.Label[segs$Transect==this_transect] <- paste(year, area, this_transect,                                                            1:n_segs, sep=\"-\") } segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect                                 geometry Effort Sample.Label ##  *    <dbl>                         <LINESTRING [°]>    [m] <chr>        ##  1        1  (-71.86948 41.29969, -71.86826 41.2861)  1515. 2010-RI-1-1  ##  2        1   (-71.86826 41.2861, -71.86703 41.2725)  1515. 2010-RI-1-2  ##  3        1  (-71.86703 41.2725, -71.86581 41.25891)  1515. 2010-RI-1-3  ##  4        2 (-71.82872 41.30738, -71.82727 41.29106)  1818. 2010-RI-2-1  ##  5        2 (-71.82727 41.29106, -71.82581 41.27475)  1818. 2010-RI-2-2  ##  6        2 (-71.82581 41.27475, -71.82436 41.25843)  1818. 2010-RI-2-3  ##  7        2  (-71.82436 41.25843, -71.8229 41.24212)  1818. 2010-RI-2-4  ##  8        2   (-71.8229 41.24212, -71.82145 41.2258)  1818. 2010-RI-2-5  ##  9        3 (-71.78805 41.31562, -71.78654 41.29813)  1948. 2010-RI-3-1  ## 10        3 (-71.78654 41.29813, -71.78505 41.28065)  1948. 2010-RI-3-2  ## # ℹ 580 more rows # save the line version of segs for plotting later segs_lines <- segs  # project segs segs <- st_transform(segs, 6348)  # find centroids segs <- st_centroid(segs) ## Warning: st_centroid assumes attributes are constant over geometries # project back to lat/long segs <- st_transform(segs, 4326)  # how does that look? segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86887 ymin: 40.83823 xmax: -70.89506 ymax: 41.45646 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect             geometry Effort Sample.Label ##  *    <dbl>          <POINT [°]>    [m] <chr>        ##  1        1 (-71.86887 41.29289)  1515. 2010-RI-1-1  ##  2        1  (-71.86764 41.2793)  1515. 2010-RI-1-2  ##  3        1  (-71.86642 41.2657)  1515. 2010-RI-1-3  ##  4        2 (-71.82799 41.29922)  1818. 2010-RI-2-1  ##  5        2  (-71.82654 41.2829)  1818. 2010-RI-2-2  ##  6        2 (-71.82508 41.26659)  1818. 2010-RI-2-3  ##  7        2 (-71.82363 41.25028)  1818. 2010-RI-2-4  ##  8        2 (-71.82218 41.23396)  1818. 2010-RI-2-5  ##  9        3 (-71.78729 41.30687)  1948. 2010-RI-3-1  ## 10        3  (-71.7858 41.28939)  1948. 2010-RI-3-2  ## # ℹ 580 more rows p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=segs) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) segs_df <- cbind(as.data.frame(st_drop_geometry(segs)),               st_coordinates(segs)) head(segs_df) ##   Transect       Effort Sample.Label         X        Y ## 1        1 1515.147 [m]  2010-RI-1-1 -71.86887 41.29289 ## 2        1 1515.151 [m]  2010-RI-1-2 -71.86764 41.27930 ## 3        1 1515.155 [m]  2010-RI-1-3 -71.86642 41.26570 ## 4        2 1818.179 [m]  2010-RI-2-1 -71.82799 41.29922 ## 5        2 1818.184 [m]  2010-RI-2-2 -71.82654 41.28290 ## 6        2 1818.190 [m]  2010-RI-2-3 -71.82508 41.26659"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"linking-observations-and-segments","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Linking observations and segments","title":"`dsm` data formatting example","text":"link together data detection function spatial data giving effort, use observation data.frame. really just cross-reference two tables, detection lives one segment. observation data.frame must (least) following columns: object : unique object identifier (corresponding identifiers detection function) Sample.Label : identifier segment observation occurred size : size observed group (e.g., 1 animals occurred individually) distance : distance observation observation data many rows detection function.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"relating-observations-and-segments-in-practice","dir":"Articles > Data_format","previous_headings":"Table construction > Linking observations and segments","what":"Relating observations and segments in practice","title":"`dsm` data formatting example","text":"methods building observation table. ’ll illustrate one assigning detections segments based distance (.e., detections associated closest segment centroid). appropriate don’t see forwards far, ’s unlikely detections miss assigned. true, instance, aerial survey observers look windows located sides plane might inappropriate shipboard survey observers flying bridge can see way ahead ship. First loading detection data survey (use fit detection function, post-processing): can see columns latitude longitude (Lat Long). can make data.frame sf object telling sf columns contain spatial coordinates (sf lingo, “geometry”): can overlay previous map transects:  Going back segs object , can use st_join function, combined st_nearest_feature function control tables linked. , need project data avoid issues. Now obs acquired additional columns segs, including Sample.Label (want) Effort (don’t). check worked okay, can plot obs segs_lines (line version segments saved earlier) colour coding Sample.Label. randomized colour order easier tell observations misallocated.  segs can remove unnecessary columns geometry get required columns : (Note since binned data, just keep Bin column need process later.) different way approach timestamps waypoints GPS, related segments. One look whether observation made start end times segment.","code":"obs <- read.csv(\"data/loons.csv\") head(obs) ##         Date     Time Bin      Lat      Long Observer object size ## 1 2011-04-19 18991230   B 40.85296 -71.45511       JV     46    1 ## 2 2011-02-17 18991230   A 40.87022 -71.54842       JV    106    1 ## 3 2011-05-02 18991230   A 40.87595 -71.30304       KW    121    1 ## 4 2011-12-04 18991230   C 40.91753 -71.38273       KW    286    1 ## 5 2011-02-23 18991230   A 40.94153 -71.38517       KW    446    1 ## 6 2011-02-07 18991230   A 40.94225 -71.46438       KW    457    1 obs <- st_as_sf(obs, coords=c(\"Long\", \"Lat\")) # set the coordinate system st_crs(obs) <- 4326 obs ## Simple feature collection with 941 features and 6 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size                   geometry ## 1  2011-04-19 18991230   B       JV     46    1 POINT (-71.45511 40.85296) ## 2  2011-02-17 18991230   A       JV    106    1 POINT (-71.54842 40.87022) ## 3  2011-05-02 18991230   A       KW    121    1 POINT (-71.30304 40.87595) ## 4  2011-12-04 18991230   C       KW    286    1 POINT (-71.38273 40.91753) ## 5  2011-02-23 18991230   A       KW    446    1 POINT (-71.38517 40.94153) ## 6  2011-02-07 18991230   A       KW    457    1 POINT (-71.46438 40.94225) ## 7  2011-04-19 18991230   A       JV    500    2 POINT (-71.59495 40.94761) ## 8  2011-05-02 18991230   A       KW    506    1   POINT (-71.552 40.94795) ## 9  2011-02-23 18991230   B       JV    814    1 POINT (-71.51172 40.97912) ## 10 2011-04-19 18991230   A       KW    911    1 POINT (-71.59762 40.98571) p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=obs, size=0.5) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) # project segs segs <- st_transform(segs, 6348) obs <- st_transform(obs, 6348)  # do the join obs <- st_join(obs, segs, join=st_nearest_feature)   # project back to lat/long segs <- st_transform(segs, 4326) obs <- st_transform(obs, 4326)  # how does that look? obs ## Simple feature collection with 941 features and 9 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size Transect       Effort ## 1  2011-04-19 18991230   B       JV     46    1        8 1969.873 [m] ## 2  2011-02-17 18991230   A       JV    106    1       26 1894.100 [m] ## 3  2011-05-02 18991230   A       KW    121    1       12 1988.809 [m] ## 4  2011-12-04 18991230   C       KW    286    1       10 1979.630 [m] ## 5  2011-02-23 18991230   A       KW    446    1       10 1979.624 [m] ## 6  2011-02-07 18991230   A       KW    457    1        8 1969.839 [m] ## 7  2011-04-19 18991230   A       JV    500    2       24 1923.218 [m] ## 8  2011-05-02 18991230   A       KW    506    1       26 1894.075 [m] ## 9  2011-02-23 18991230   B       JV    814    1        7 2000.139 [m] ## 10 2011-04-19 18991230   A       KW    911    1       24 1923.199 [m] ##     Sample.Label                   geometry ## 1   2010-RI-8-29 POINT (-71.45511 40.85296) ## 2  2010-RI-26-16 POINT (-71.54842 40.87022) ## 3  2010-RI-12-31 POINT (-71.30304 40.87595) ## 4  2010-RI-10-27 POINT (-71.38273 40.91753) ## 5  2010-RI-10-26 POINT (-71.38517 40.94153) ## 6   2010-RI-8-24 POINT (-71.46438 40.94225) ## 7  2010-RI-24-12 POINT (-71.59495 40.94761) ## 8  2010-RI-26-12   POINT (-71.552 40.94795) ## 9   2010-RI-7-22 POINT (-71.51172 40.97912) ## 10  2010-RI-24-9 POINT (-71.59762 40.98571) # make a random colour palette to avoid similar colours being near each other pal <- rainbow(nrow(segs), s=.6, v=.9)[sample(1:nrow(segs),nrow(segs))] p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=segs_lines, aes(colour=Sample.Label), pch=21) +   geom_sf(data=obs, size=0.5, aes(colour=Sample.Label)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   scale_colour_manual(values=pal) +   # make the plot look simpler   theme_minimal() +   theme(legend.position = \"none\") print(p) # get rid of \"spatialness\" obs <- st_drop_geometry(obs) # select the columns we need obs <- obs[, c(\"object\", \"Sample.Label\", \"size\", \"Bin\")] head(obs) ##   object  Sample.Label size Bin ## 1     46  2010-RI-8-29    1   B ## 2    106 2010-RI-26-16    1   A ## 3    121 2010-RI-12-31    1   A ## 4    286 2010-RI-10-27    1   C ## 5    446 2010-RI-10-26    1   A ## 6    457  2010-RI-8-24    1   A"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"more-information","dir":"Articles > Data_format","previous_headings":"","what":"More information","title":"`dsm` data formatting example","text":"provided information segmenting wiki page, part US Navy-funded project DenMod. information summarized ?\"dsm-data\" dsm package. may also useful look data example dataset mexdolphins can loaded data(mexdolphins). vignette analysis data available . hard give general information segment lines, extent depends data originally formatted (going way back make model GPS unit used). information dealing spatial data R using sf package available R spatial website (see “Articles” drop header). Another source help distance sampling mailing list. sure search archives prior posting several threads segmenting previously might helpful.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"acknowledgements","dir":"Articles > Data_format","previous_headings":"","what":"Acknowledgements","title":"`dsm` data formatting example","text":"Thanks Phil Bouchet providing helpful comments early version document Iúri Correia finding important bug.","code":""},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"introduction","dir":"Articles > Lines_gomex","previous_headings":"","what":"Introduction","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"analysis based dataset observations pantropical dolphins Gulf Mexico (shipped Distance 6.0 later). convenience data bundled R-friendly format, although code necessary creating data Distance project files available github. OBIS-SEAMAP page data may found SEFSC GoMex Oceanic 1996 survey page. intention highlight features dsm package, rather perform full analysis data. reason, important steps fully explored. familiarity density surface modelling (Miller, Burt, Rexstad, & Thomas, 2013) (Hedley & Buckland, 2004) assumed.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"preamble","dir":"Articles > Lines_gomex","previous_headings":"","what":"Preamble","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"start, load dsm package (dependencies) set options: order run vignette, ’ll need install R packages. can done via following call install.packages:","code":"library(dsm) library(ggplot2)  # plotting options gg.opts <- theme(panel.grid.major=element_blank(),                  panel.grid.minor=element_blank(),                  panel.background=element_blank()) install.packages(c(\"dsm\", \"Distance\", \"knitr\", \"distill\", \"ggplot2\", \"rgdal\",                    \"maptools\", \"plyr\", \"tweedie\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"the-data","dir":"Articles > Lines_gomex","previous_headings":"","what":"The data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data need included dsm package, two additional objects needed plotting required can downloaded put directory file. data can loaded R using following code: add objects survey.area pred.polys environment.","code":"load(\"mexdolphins-extra.rda\")"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"observation-and-segment-data","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Observation and segment data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data analysis nicely pre-formatted shipped dsm. Loading data, can see four data.frames, first lines shown: segdata holds segment data: transects already “chopped” segments. distdata holds distance sampling data used fit detection function. obsdata links distance data segments. preddata holds prediction grid (includes necessary covariates). Typically (.e. datasets) necessary divide transects segments, allocate observations correct segments using GIS similar package1, starting analysis using dsm.","code":"data(mexdolphins) head(segdata) ##   longitude latitude        x        y Effort Transect.Label Sample.Label depth ## 1 -86.92712 29.94378 836105.9 -1011416  13800       19960417   19960417-1 135.0 ## 2 -86.83176 29.84030 846012.9 -1021407  14000       19960417   19960417-2 147.7 ## 3 -86.74445 29.75279 855022.6 -1029785  14000       19960417   19960417-3 152.1 ## 4 -86.65230 29.65522 864610.3 -1039168  13900       19960417   19960417-4 163.8 ## 5 -86.56648 29.56088 873598.1 -1048266  13800       19960417   19960417-5 179.7 ## 6 -86.49290 29.49000 881203.7 -1055004  13800       19960417   19960417-6 188.5 head(distdata) ##     object size  distance Effort detected beaufort latitude longitude ## 45      45   21 3296.6363  36300        1        4 27.72872 -86.00159 ## 61      61  150  929.1937  17800        1        4 25.99896 -87.62712 ## 63      63  125 6051.0009  21000        1        2 26.00693 -87.94881 ## 85      85   75 5499.6971  21800        1        1 27.50344 -90.44891 ## 114    114   50 7258.9837  13400        1        3 27.40568 -94.99483 ## 120    120   45 1454.7962  20900        1        5 26.01765 -95.97449 ##              x        y ## 45  948000.065 -1236192 ## 61  812161.653 -1436899 ## 63  780969.520 -1438985 ## 85  528656.807 -1297833 ## 114  95910.149 -1324562 ## 120   2477.665 -1473909 head(obsdata) ##     object Sample.Label size  distance Effort ## 45      45   19960421-9   21 3296.6363  36300 ## 61      61   19960423-7  150  929.1937  17800 ## 63      63   19960423-9  125 6051.0009  21000 ## 85      85   19960427-1   75 5499.6971  21800 ## 114    114   19960430-8   50 7258.9837  13400 ## 120    120   19960501-5   45 1454.7962  20900 head(preddata) ##   latitude longitude        x          y depth      area ## 1 30.08333 -87.58333 774402.9 -1002759.1    35 271236913 ## 2 30.08333 -87.41667 789688.6 -1001264.5    30 271236913 ## 3 30.08333 -87.25000 804971.3  -999740.6    27 271236913 ## 4 30.08333 -87.08333 820251.1  -998187.5    22 271236913 ## 5 30.08333 -86.91667 835528.0  -996605.2    46 271236913 ## 6 29.91667 -87.75000 760783.1 -1021810.3    14 271236913"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"shapefiles-and-converting-units","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Shapefiles and converting units","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often data spatial analysis comes many different sources. important ensure measurements used analysis compatible units, otherwise resulting estimates incorrect hard interpret. measurements SI units outset removes need conversion later, making life much easier. data already appropriate units (Northings Eastings: kilometres centroid, projected using North American Lambert Conformal Conic projection). extensive literature particular projections latitude longitude appropriate highly recommend reader review particular study area; (Bivand, Pebesma, & Gómez-Rubio, 2008) good starting point. data frames already measurements appropriately converted. convention directions named x y. Using latitude longitude performing spatial smoothing can problematic certain smoother bases used. particular bivariate isotropic bases used non-isotropic nature latitude longitude inconsistent (moving one degree one direction moving one degree ). give example projecting polygon defines survey area (simply read R using readShapeSpatial shapefile produced GIS). Next bit code works study.area outline, massaging use soap film smoother. using soap film smoothers, code extraneous. code generates plot, shows survey area transect lines overlaid (using data segdata).  Also note since projected prediction grid, “squares” look quite like squares. plotting use polygons saved, polygons (stored pred.polys) read shapefile created GIS, object class SpatialPolygons sp package. needs project proper plotting using functions sf package. predsf <- st_as_sf(pred.polys)  Tips plotting polygons available ggplot2 pkg_down.","code":"library(sf) library(plyr)  # tell R that the survey.area object is currently in lat/long sp::proj4string(survey.area) <- sp::CRS(\"+proj=longlat +datum=WGS84\")  predsf <- st_as_sf(pred.polys)  area.sf <- st_as_sf(survey.area) st_crs(area.sf) <- \"WGS84\" area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))  # Convert preddata to a spatial object preddata_sf <- st_as_sf(preddata, coords=c(\"x\", \"y\")) st_crs(preddata_sf) <- st_crs(area.sf.proj) # Perform the intersection preddata_sf <- st_intersection(preddata_sf, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries coords_preddata <- data.frame(st_coordinates(preddata_sf))  preddata_sf$x <- coords_preddata$X preddata_sf$y <- coords_preddata$Y # proj 4 string # using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/ lcc_proj4 <- sp::CRS(\"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs \")  # project using LCC survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)  # simplify the object survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords) names(survey.area) <- c(\"x\", \"y\") segdata_sf <- st_as_sf(segdata, coords = c(\"x\",\"y\")) st_crs(segdata_sf) <- st_crs(area.sf.proj) # study area outline and segment centres plot_segments <- ggplot() +   geom_sf(data = area.sf.proj, fill=\"lightblue\", color = \"blue\", linewidth=.1) +   geom_sf(data=segdata_sf, fill=NA, color=\"black\", linewidth=.3) +   labs(title=\"1996 SE Fisheries Science Center Gulf of Mexico cruise\",        subtitle = \"Points are segment centres\") +   scale_fill_viridis_c(option = \"magma\", guide = \"none\") plot_segments predsf <- st_as_sf(pred.polys) # plot as projected plot(st_geometry(predsf), axes=TRUE)"},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"distance-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Distance data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top panels EDA plots show histograms observed distances cluster size, bottom panels show relationship observed distance observed cluster size, relationship observed distance Beaufort sea state. plots show relationship cluster size observed distance (fewer smaller clusters seem seen larger distances). following code generates EDA plots:  Top row, left right: histograms distance cluster size; bottom row: plot distance cluster size plot distances Beaufort sea state.","code":"par(mfrow=c(2,2))  # histograms hist(distdata$distance,main=\"\",xlab=\"Distance (m)\") hist(distdata$size,main=\"\",xlab=\"Cluster size\")  # plots of distance vs. cluster size plot(distdata$distance, distdata$size, main=\"\", xlab=\"Distance (m)\",      ylab=\"Group size\", pch=19, cex=0.5, col=gray(0.7))  # lm fit l.dat <- data.frame(distance=seq(0,8000,len=1000)) lo <- lm(size~distance, data=distdata) lines(l.dat$distance, as.vector(predict(lo,l.dat)))  plot(distdata$distance,distdata$beaufort, main=\"\", xlab=\"Distance (m)\",      ylab=\"Beaufort sea state\", pch=19, cex=0.5, col=gray(0.7))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Spatial data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Looking separately spatial data without thinking distances, can plot observed group sizes space (point size proportional group size observation). Circle size indicates size group observation. rather large areas observations, might cause variance estimates abundance rather large. also see depth data use depth later explanatory covariate spatial model.","code":"prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid) cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries depth <- ggplot() +          geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +          labs(title = \"Spotted dolphins, Gulf of Mexico\",               subtitle = \"Depth in meters, size of detected dolphin groups\") +          xlab(\"Longitude\") + ylab(\"Latitude\") +          geom_point(aes(x, y, size=size), data=distdata, colour=\"red\",alpha=I(0.5)) +          scale_fill_viridis_c(option = \"viridis\", direction = 1) depth"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"estimating-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"","what":"Estimating the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"use ds function package Distance fit detection function. (Distance package intended make standard distance sampling R relatively straightforward. flexible complex alternative, see function ddf mrds library.) First, loading Distance library: can fit detection function hazard-rate key adjustment terms: Calling summary gives us information parameter estimates, probability detection, AIC, etc: following code generates plot fitted detection function (left) quantile-quantile plot (right):  quantile-quantile plot show relatively good goodness fit hazard-rate detection function.","code":"library(Distance) detfc.hr.null <- ds(distdata, max(distdata$distance), key=\"hr\", adjustment=NULL) ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). summary(detfc.hr.null) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  841.1841  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##             estimate        se ## (Intercept) 7.986391 0.5239151 ##  ## Shape coefficient(s):   ##                estimate         se ## (Intercept) 0.000237466 0.02958901 ##  ##                       Estimate         SE        CV ## Average p            0.5918795  0.1667006 0.2816462 ## N in covered region 79.4080596 23.5573051 0.2966614 plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2) gof_ds(detfc.hr.null) ##  ## Goodness of fit results for ddf object ##  ## Distance sampling Cramer-von Mises test (unweighted) ## Test statistic = 0.0976846 p-value = 0.596345"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-covariates-to-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Estimating the detection function","what":"Adding covariates to the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"common include covariates detection function (-called Multiple Covariate Distance Sampling MCDS). dataset two covariates collected individual: Beaufort sea state size. brevity fit hazard-rate detection functions sea state included factor covariate follows: looking summary, detection function covariates give lower AIC model without covariates (843.71 vs. 841.18 hazard-rate model without covariates). Looking back bottom-right panel EDA plots, can see discernible pattern plot Beaufort vs distance. brevity, detection function model selection omitted . practise fit many different forms detection function (select model based goodness fit testing AIC).","code":"detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),                   key=\"hr\", adjustment=NULL) summary(detfc.hr.beau) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  843.7115  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##                         estimate        se ## (Intercept)           7.66291936  1.078972 ## as.factor(beaufort)2  2.24809809 14.754677 ## as.factor(beaufort)3  0.28581820  1.020809 ## as.factor(beaufort)4  0.07133776  1.225360 ## as.factor(beaufort)5 -0.36421115  1.540324 ##  ## Shape coefficient(s):   ##              estimate        se ## (Intercept) 0.2994644 0.5184204 ##  ##                       Estimate         SE        CV ## Average p            0.5420677  0.1753555 0.3234937 ## N in covered region 86.7050383 29.4646397 0.3398262"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"fitting-a-dsm","dir":"Articles > Lines_gomex","previous_headings":"","what":"Fitting a DSM","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"fitting dsm model, data must segmented; consists chopping transects attributing counts segments. mentioned , data already segmented.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"a-simple-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"A simple model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"begin simple model. assume number individuals segment quasi-Poisson distributed smooth function spatial coordinates (note formula exactly one specify gam mgcv). setting group=TRUE, abundance clusters/groups rather individuals can estimated (though ignore ). Note set method=\"REML\" ensure smooth terms estimated reliably. Running model: can obtain summary fitted model: exact interpretation model summary results can found (S. N. Wood, 2017); can see various information smooth components fitted general model statistics. can use deviance explained compare models2. can also get rough idea smooth space looks like using vis.gam (white/yellow indicates high values, red low indicates low values):  type=\"response\" argument ensures plot scale abundance values relative (offsets set median values). means plot useful get idea general shape smooth interpreted directly.","code":"dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)   -18.20       0.53  -34.34   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##         edf Ref.df     F  p-value     ## s(x,y) 24.8  27.49 2.354 0.000733 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.121   Deviance explained = 43.4% ## -REML = 936.04  Scale est. = 94.367    n = 387 vis.gam(dsm.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-another-environmental-covariate-to-the-spatial-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Adding another environmental covariate to the spatial model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data set also contains depth covariate (plotted ). can include model simply: see drop deviance explained, perhaps model useful first. discuss setting k parameter Model checking, . Setting select=TRUE (argument gam) impose extra shrinkage terms smooth model (allowing smooth terms removed model fitting; see ?gam information). particularly useful , include . However many environmental predictors model can good way (along looking \\(p\\)-values) perform term selection. Simply calling plot model object allows us look relationship depth linear predictor:  Omitting argument select call plot plot smooth terms, one time.","code":"dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy.depth) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, k = 10) + s(depth, k = 20) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.741      1.236  -15.16   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F p-value   ## s(x,y)   6.062  7.371 0.923  0.5038   ## s(depth) 9.443 11.466 1.585  0.0824 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0909   Deviance explained = 34.3% ## -REML = 939.52  Scale est. = 124.9     n = 387 plot(dsm.xy.depth, select=2)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-models-when-there-are-covariates-in-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Spatial models when there are covariates in the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"code fit DSM covariates detection function similar models, . However since detection function observation-level covariates, must estimate abundance per segment using Horvitz-Thompson-like estimator modelling, change response abundance.est: can see, summary results rather similar: resulting spatial smooth (though resulting surface somewhat “amplified”):","code":"dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method=\"REML\") summary(dsm.est.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## abundance.est ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -18.0233     0.4739  -38.03   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value     ## s(x,y) 24.64  27.44 2.349 0.00029 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.129   Deviance explained = 42.2% ## -REML = 1043.6  Scale est. = 174.1     n = 387 vis.gam(dsm.est.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", zlim=c(0, 300), contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-response-distributions","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other response distributions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often quasi-Poisson distribution doesn’t give adequate flexibility capture overdispersion response data (see Model checking Model selection ), illustrate two additional distributions can used count data. models section, ’ll move back count response, though estimated abundance also work.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"tweedie","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Tweedie","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Response distributions quasi-Poisson can used, example Tweedie distribution. Tweedie distribution available dsm setting family=tw().","code":"dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method=\"REML\") summary(dsm.xy.tweedie) ##  ## Family: Tweedie(p=1.347)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -17.2651     0.2363  -73.08   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value   ## s(x,y) 12.82  17.13 1.628  0.0562 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0684   Deviance explained = 26.8% ## -REML = 351.49  Scale est. = 60.26     n = 387"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"negative-binomial","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Negative binomial","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Though used , similarly, two options negative binomial distribution: negbin nb. former requires user specification single parameter theta range values parameter (specified vector), latter estimates value theta model fitting process (generally faster). latter recommended users.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-spatial-modelling-options","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other spatial modelling options","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"large literature spatial modelling using GAMs, much can harnessed DSM context. highlights.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"soap-film-smoothing","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other spatial modelling options","what":"Soap film smoothing","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"account complex region (e.g., region includes peninsulae) can use soap film smoother (Simon N. Wood, Bravington, & Hedley, 2008). use soap film smoother spatial part model must create set knots smoother use. easily done using make.soapgrid() function dsm: second argument specifies number points (direction) grid used create knots (knots grid outside survey.area removed). saw exploratory analysis, transect lines outside survey area. cause soap film smoother fail, remove : Note soap_checker script available can useful ensuring boundary, data knots correct format use soap film smoother. can run model depth covariate along spatial (soap film) smooth. Note k argument now refers complexity boundary smooth soap film, complexity film controlled knots given xt argument.","code":"soap.knots <- make.soapgrid(survey.area,c(15,10)) x <- segdata$x; y<-segdata$y onoff <- inSide(x=x,y=y, bnd=as.list(survey.area)) rm(x,y) segdata.soap <- segdata[onoff,] dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs=\"so\", k=15, xt=list(bnd=list(survey.area))) +             s(depth),           family=tw(), method=\"REML\",           detfc.hr.null, segdata.soap, obsdata, knots=soap.knots) summary(dsm.xy.tweedie.soap) ##  ## Family: Tweedie(p=1.356)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, bs = \"so\", k = 15, xt = list(bnd = list(survey.area))) +  ##     s(depth) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.018      0.405  -44.49   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F  p-value     ## s(x,y)   3.050 72.000 0.064 0.114944     ## s(depth) 5.135  6.196 3.895 0.000793 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0653   Deviance explained = 33.1% ## -REML = 339.26  Scale est. = 54.022    n = 365"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-checking","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model checking","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Fitting models well good, ’d like confirm models reasonable; dsm provides functions model checking. can use gam.check generate diagnostic plots:  show deviation Q-Q plot. “line” points plot residuals vs. linear predictor plot corresponds zeros data. Note well plots, gam.check also produces information model fitting. particular interest us last lines tell us basis size. k parameter provided s (te) terms dsm controls complexity smooths model. setting k parameter specify largest complexity smooth term model; long high enough, can sure enough flexibility. output gam.check , can see “p-value” calculated size basis, can good guide whether basis size needs increased. ?choose.k manual page mgcv gives guidance technical details matter. can look model form Tweedie distribution specified response:  Q-Q plot now seems much better (closer \\(y=x\\) line). plots histogram residuals rather hard interpret due large number zeros data. guidance interpreting gam.check output can found (S. N. Wood, 2017).","code":"gam.check(dsm.xy) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 6 iterations. ## Gradient range [-7.105947e-08,-9.826417e-09] ## (score 936.0363 & scale 94.3674). ## Hessian positive definite, eigenvalue range [5.800328,192.5304]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 24.8    1.01    0.99 gam.check(dsm.xy.tweedie) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 8 iterations. ## Gradient range [-1.513175e-06,3.981813e-06] ## (score 351.486 & scale 60.26034). ## Hessian positive definite, eigenvalue range [0.6230615,103.5094]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 12.8    0.73    0.34"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"randomised-quantile-residuals","dir":"Articles > Lines_gomex","previous_headings":"Model checking","what":"Randomised quantile residuals","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top right panel gam.check plots residuals vs. linear predictor plot includes odd line predictions. artifact link function, showing exact zeros data. can misleading distracting, making difficult see whether residuals show heteroskedasticity. Randomised quantile residuals (Dunn & Smyth, 1996) avoid issue transforming residuals exactly normally distributed. makes residuals vs. linear predictor plot much easier interpret therefore doesn’t include artifacts generated link function. plots can produced using rqgam.check dsm:  can see issue heteroskedasticity (increase spread residuals vs. linear predictor plot increasing values linear predictor). One can also plot residuals covariate values check pattern residuals. Note general, plots “Resids vs. linear pred.” interpreted caution output rqgam.check residuals generated normal construction (example Q-Q plot histogram residuals always look fine).","code":"rqgam_check(dsm.xy.tweedie)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-selection","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model selection","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Assuming models “passed” checks gam.check, rqgam.check sufficiently flexible, may left choice model “best”. several methods choosing best model – AIC, REML/GCV scores, deviance explained, full cross-validation test data . Though document intend full analysis pantropical dolphin data, can create results table compare various models fitted far terms abundance estimates associated uncertainties. can use resulting data.frame build table results using kable function:","code":"# make a data.frame to print out mod_results <- data.frame(\"Model name\" = c(\"`dsm.xy`\", \"`dsm.xy.depth`\", \"`dsm.xy.tweedie`\", \"`dsm.xy.tweedie.soap`\",                                            \"`dsm.est.xy`\"),                           \"Description\" = c(\"Bivariate smooth of location, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie\",                                             \"Soap film smooth of location, smooth of depth, Tweedie\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function\"),                           \"Deviance explained\" = c(unlist(lapply(list(dsm.xy,                                                                       dsm.xy.depth,                                                                       dsm.xy.tweedie,                                                                       dsm.xy.tweedie.soap),                  function(x){paste0(round(summary(x)$dev.expl*100,2),\"%\")})),NA)) kable(mod_results, col.names=c(\"Model name\", \"Description\", \"Deviance explained\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"abundance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Abundance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"model checked selected, can make predictions grid calculate abundance. offset stored area column3. Given predicted abundance estimates cell prediction grid, connected prediction grid object mapping:  can calculate abundance survey area simply summing predictions: can compare plot predictions dsm.xy.depth:  can see inclusion depth model noticeable effect distribution (note difference legend scale two plots). can also look total abundance: see much change abundance, terms abundance alone isn’t much two models. Next ’ll go look variance next can see bigger differences models.","code":"dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area) prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid)  preddata_sf$Prediction_xy <- dsm.xy.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.pred) ## [1] 28430.76 dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)  preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, smooth of depth, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.depth.pred) ## [1] 27054.6"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"variance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Variance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Obviously point estimates abundance important, also calculate uncertainty around abundance estimates. Fortunately dsm provides functions perform calculations display resulting uncertainty estimates. two approaches estimating uncertainty abundance estimate dsm. dsm_var_gam assumes spatial model detection function parts model independent . case squared coefficients variation model component added. dsm_var_prop takes account fact detection probability may correlated spatial part model. uses methods described (Bravington, Miller, & Hedley, 2021). dsm_var_prop can applied covariate detection function varies level segments, recorded segment (example Beaufort). don’t situation , opt dsm_var_gam. methods estimate variance abundance element list provided pred.data. case wish obtain abundance prediction cells, use split chop data set list elements give dsm_var_gam (dsm_var_prop). Calling summary give information uncertainty estimation: can also make plot CVs (transect lines observations overlaid) using following code.  Note increase CV away transect lines. can revisit model included depth location smooths observe coefficient variation model larger model location smooth.","code":"preddata.var <- split(preddata, 1:nrow(preddata)) dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,                           off.set=preddata$area) summary(dsm.xy.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 15252.81 28430.76 52994.04  ## (Using log-Normal approximation) ##  ## Point estimate                 : 28430.76  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.164  ## Total standard error           : 9265.663  ## Total coefficient of variation : 0.3259 preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries CV <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, uncertainty (CV)\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) CV dsm.xy.depth.var <- dsm_var_gam(dsm.xy.depth, pred.data=preddata.var,                                 off.set=preddata$area) summary(dsm.xy.depth.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 14377.57 27054.60 50909.23  ## (Using log-Normal approximation) ##  ## Point estimate                 : 27054.6  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.1741  ## Total standard error           : 8958.444  ## Total coefficient of variation : 0.3311"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"conclusions","dir":"Articles > Lines_gomex","previous_headings":"","what":"Conclusions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"document outlined analysis spatially-explicit distance sampling data using dsm package. Note many possible models can fitted using dsm aim show just options. Results models can rather different, care must taken performing model selection, discrimination criticism.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"software","dir":"Articles > Lines_gomex","previous_headings":"","what":"Software","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Distance available http://github.com/DistanceDevelopment/Distance well CRAN. dsm available http://github.com/DistanceDevelopment/dsm, well CRAN.","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"David L. Miller. Author. Eric Rexstad. Contributor. Louise Burt. Contributor. Mark V. Bravington. Contributor. Sharon Hedley. Contributor. Megan Ferguson. Contributor. Natalie Kelly. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller DL (2024). dsm: Density Surface Modelling Distance Sampling Data. R package version 2.3.3.9001, https://github.com/DistanceDevelopment/dsm.","code":"@Manual{,   title = {dsm: Density Surface Modelling of Distance Sampling Data},   author = {David L. Miller},   year = {2024},   note = {R package version 2.3.3.9001},   url = {https://github.com/DistanceDevelopment/dsm}, }"},{"path":[]},{"path":"/index.html","id":"what-is-dsm","dir":"","previous_headings":"","what":"Density Surface Modelling of Distance Sampling Data","title":"Density Surface Modelling of Distance Sampling Data","text":"dsm provides Generalized Additive Model (GAM)-based approach calculate spatially-explicit estimates animal abundance distance sampling (also presence/absence strip transect) data. Several utility functions provided model checking, plotting variance estimation. Open Access paper describing methods: Miller, DL, ML Burt, EA Rexstad L Thomas (2013). Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution.","code":""},{"path":"/index.html","id":"using-dsm","dir":"","previous_headings":"","what":"Using dsm","title":"Density Surface Modelling of Distance Sampling Data","text":"Examples use dsm distance sampling R packages available examples.distancesampling.org.","code":""},{"path":"/reference/block.info.per.su.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the block information — block.info.per.su","title":"Find the block information — block.info.per.su","text":"Takes transect data works many blocks given size (segment terms) fit .","code":""},{"path":"/reference/block.info.per.su.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the block information — block.info.per.su","text":"","code":"block.info.per.su(block.size, data, name.su)"},{"path":"/reference/block.info.per.su.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the block information — block.info.per.su","text":"block.size number segments per block data data used build model name.su names sampling units (.e., transects)","code":""},{"path":"/reference/block.info.per.su.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the block information — block.info.per.su","text":"data.frame following columns name        sample unit name (e.g. transect label) num.seg     number segments transect num.block   number blocks available start.block block number first block end.block   block number last block num.req     number blocks needed unit","code":""},{"path":"/reference/check.cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names exist — check.cols","title":"Check column names exist — check.cols","text":"Internal function check supplied data.frames correct columns checks sample labels unique.","code":""},{"path":"/reference/check.cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names exist — check.cols","text":"","code":"check.cols(ddf.obj, segment.data, observation.data, segment.area)"},{"path":"/reference/check.cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names exist — check.cols","text":"ddf.obj ddf object mrds segment.data segment data defined dsm observation.data observation data defined dsm segment.area area segments","code":""},{"path":"/reference/check.cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names exist — check.cols","text":"nothing, throws error something went wrong","code":""},{"path":"/reference/check.cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check column names exist — check.cols","text":"David Lawrence Miller","code":""},{"path":"/reference/dsm-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Data format for DSM — dsm-data","title":"Data format for DSM — dsm-data","text":"Two data.frames must provided dsm. referred observation.data segment.data.","code":""},{"path":"/reference/dsm-data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data format for DSM — dsm-data","text":"segment.data table sample identifiers define segments, corresponding effort (line length) expended environmental covariates used model abundance/density. observation.data provides link table observations used detection function samples (segments), can aggregate observations segments (.e., observation.data \"look-table\" observations segments). observation.data - observation data.frame must (least) following columns: object unique object identifier Sample.Label identifier segment observation occurred size size observed group (e.g., 1 animals occurred individually) distance distance observation One can often also use observation.data fit detection function (additional columns detection function covariates allowed table). segment.data: segment data.frame must (least) following columns: Effort effort (terms length segment) Sample.Label identifier segment (unique!) ??? environmental covariates, example location (projected latitude longitude), relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).","code":""},{"path":"/reference/dsm-data.html","id":"multiple-detection-functions","dir":"Reference","previous_headings":"","what":"Multiple detection functions","title":"Data format for DSM — dsm-data","text":"multiple detection functions used, column named ddfobj must included observation.data segment.data. lets model know detection function observation . numeric ordered ddf.obj argument dsm, e.g., ddf.obj=list(ship_ddf, aerial_ddf) means ship detections ddfobj=1 aerial detections ddfobj=2 observation data.","code":""},{"path":"/reference/dsm-data.html","id":"mark-recapture-distance-sampling-models","dir":"Reference","previous_headings":"","what":"Mark-recapture distance sampling models","title":"Data format for DSM — dsm-data","text":"using mrds models include mark-recapture components (currently independent observer trial modes supported) format observation data needs checked ensure observations duplicated. observer column also required observation.data. Independent observer mode unique observations (unique object IDs) required. Trial mode observations made observer 1 required.","code":""},{"path":"/reference/dsm-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Density surface modelling — dsm-package","title":"Density surface modelling — dsm-package","text":"dsm implements spatial models distance sampling data. Models detectability can fitted using packages mrds Distance. dsm fits generalized additive models spatially-referenced data. See Miller et al (2013) introduction.","code":""},{"path":"/reference/dsm-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density surface modelling — dsm-package","text":"information distance sampling methods example code available http://distancesampling.org/R/. help distance sampling package, Google Group https://groups.google.com/forum/#!forum/distance-sampling. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density surface modelling — dsm-package","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm.cor","title":"Check for autocorrelation in residuals — dsm.cor","text":"function deprecated, use dsm_cor.","code":""},{"path":"/reference/dsm.cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm.cor","text":"","code":"dsm.cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm.cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm.cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Fits density surface model (DSM) detection adjusted counts spatially-referenced distance sampling analysis. dsm takes observations animals, allocates segments line (strip transects) optionally adjusts counts based detectability using supplied detection function model. generalized additive model, generalized mixed model generalized linear model used model adjusted counts based formula involving environmental covariates.","code":""},{"path":"/reference/dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"dsm(   formula,   ddf.obj,   segment.data,   observation.data,   engine = \"gam\",   convert.units = 1,   family = quasipoisson(link = \"log\"),   group = FALSE,   control = list(keepData = TRUE),   availability = 1,   segment.area = NULL,   weights = NULL,   method = \"REML\",   ... )"},{"path":"/reference/dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"formula formula surface. valid formula. See \"Details\", , define response. ddf.obj result call ddf ds. multiple detection functions required list can provided. strip/circle transects assumed objects observed, see dummy_ddf. Mark-recapture distance sampling (mrds) models type io (independent observers) trial allowed. segment.data segment data, see dsm-data. observation.data observation data, see dsm-data. engine fitting engine used DSM (\"glm\"/\"gam\"/\"gamm\"/\"bam\"). convert.units conversion factor multiply area segments . See 'Units' . family response distribution (popular choices include quasipoisson, Tweedie/tw negbin/nb). Defaults quasipoisson. group TRUE abundance groups calculated rather abundance individuals. Setting option TRUE equivalent setting size group 1. control usual control argument gam; keepData must TRUE variance estimation work (though option set GLMs GAMMs). availability estimate availability bias. count models used multiply effective strip width (must vector length 1 length number rows segment.data); estimated abundance/estimated density models used scale response (must vector length 1 length number rows observation.data). Uncertainty availability handled present. segment.area NULL (default) segment areas calculated multiplying Effort column segment.data (right minus left) truncation distance ddf.obj strip.width. Alternatively vector segment areas can provided (must length number rows segment.data) character string giving name column segment.data contains areas. segment.area specified takes precedent. weights weights observation used model fitting. default, weights=NULL, weights observation area (see Details). Setting scalar value (e.g., weights=1) observations equally weighted. method smoothing parameter estimation method. Default \"REML\", using Restricted Maximum Likelihood. See gam options. Ignored engine=\"glm\". ... anything else passed straight glm, gam, gamm bam.","code":""},{"path":"/reference/dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"glm, gam, gamm bam object, additional element, $ddf holds detection function object.","code":""},{"path":"/reference/dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"response (LHS formula) can one following (restrictions outlined ): count count segment abundance.est estimated abundance per segment, estimation via Horvitz-Thompson estimator density.est density per segment offset used model dependent response: count area segment multiplied average probability detection segment abundance.est area segment density zero count response can used detection function covariates vary segments/points (within). example, weather conditions (like visibility sea state) foliage cover usually acceptable change within segment, animal sex behaviour work. abundance.est response can used covariates detection function. density case, observations can weighted segment areas via weights= argument. default (weights=NULL), density estimated weights set segment areas (using segment.area calculated detection function object metadata Effort data). Alternatively weights=1 set weights equal. third alternative pass vector length equal number segments, containing appropriate weights. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"often case distances collected metres segment lengths recorded kilometres. dsm allows provide conversion factor (convert.units) multiply areas . example: distances metres segment lengths kilometres setting convert.units=1000 lead analysis metres. Setting convert.units=1/1000 lead analysis kilometres. conversion factor applied segment.area specified.","code":""},{"path":"/reference/dsm.html","id":"large-models","dir":"Reference","previous_headings":"","what":"Large models","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"large models, engine=\"bam\" method=\"fREML\" may useful. Models specified bam gam. Read bam using option; option considered EXPERIMENTAL moment. particular note default basis choice (thin plate regression splines) slow general fitting less stable using gam. negative binomial response, theta must specified using bam.","code":""},{"path":"/reference/dsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"David L. Miller","code":""},{"path":"/reference/dsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) summary(mod1)  # predict over a grid mod1.pred <- predict(mod1, preddata, preddata$area)  # calculate the predicted abundance over the grid sum(mod1.pred)  # plot the smooth plot(mod1) }"},{"path":"/reference/dsm.var.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm.var.gam","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"function deprecated, use dsm_var_gam.","code":""},{"path":"/reference/dsm.var.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"","code":"dsm.var.gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm.var.movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"function deprecated, use dsm_var_movblk.","code":""},{"path":"/reference/dsm.var.movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"","code":"dsm.var.movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm.var.movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL -- file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm.var.prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm.var.prop","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"function deprecated, use dsm_var_prop.","code":""},{"path":"/reference/dsm.var.prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"","code":"dsm.var.prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm_cor","title":"Check for autocorrelation in residuals — dsm_cor","text":"DSM fitted data, function can used check autocorrelation residuals.","code":""},{"path":"/reference/dsm_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"dsm_cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm_cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for autocorrelation in residuals — dsm_cor","text":"plot vector fun applied lags.","code":""},{"path":"/reference/dsm_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for autocorrelation in residuals — dsm_cor","text":"Within Transect.Label, segments sorted according Segment.Labels. may require time get right particular data. one multiple surveys transects revisited, example, one may want make Transect.Label unique transect-survey identifier. Neither label need included model, must just present $data field model. usually means segment data passed dsm. current iteration function plot correlations nicely, things can get function return data (assigning result object). NA values residuals correlogram calculated. usually occurs due NA values covariates (smoother fitted values ). Code like (.na(dsm.obj$data)) might helpful.","code":""},{"path":"/reference/dsm_cor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for autocorrelation in residuals — dsm_cor","text":"David L. Miller","code":""},{"path":"/reference/dsm_cor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"# \\donttest{  library(Distance) #>  #> Attaching package: 'Distance' #> The following object is masked from 'package:dsm': #>  #>     dummy_ddf #> The following object is masked from 'package:mrds': #>  #>     create.bins  library(dsm)   # load the data, see ?mexdolphins  data(mexdolphins)   # fit a model  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.418 #> No survey area information supplied, only estimating detection function.  mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) #> Warning: Some observations are outside of detection function 1 truncation!   # look at lag 1 differences up to a maximum of lag 9, using deviance  # residuals  dsm_cor(mod1, resid.type=\"deviance\", max.lag=9,          Segment.Label=\"Sample.Label\")  # }"},{"path":"/reference/dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance propagation for density surface models — dsm_varprop","title":"Variance propagation for density surface models — dsm_varprop","text":"Calculate uncertainty predictions fitted DSM, including uncertainty detection function.","code":""},{"path":"/reference/dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance propagation for density surface models — dsm_varprop","text":"","code":"dsm_varprop(   model,   newdata = NULL,   trace = FALSE,   var.type = \"Vp\",   var_type = NULL )"},{"path":"/reference/dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance propagation for density surface models — dsm_varprop","text":"model fitted dsm. newdata prediction grid. Set NULL avoid making predictions just return model objects. trace debugging, see scale parameter estimation going. var.type variance-covariance matrix used (\"Vp\" variance-covariance conditional smoothing parameter(s), \"Vc\" unconditional). See gamObject details/explanation. doubt, stick default, \"Vp\". var_type deprecated, use var.type instead.","code":""},{"path":"/reference/dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance propagation for density surface models — dsm_varprop","text":"list elements: old_model fitted model supplied function model refit refitted model object, extra term pred point estimates predictions newdata var total variance calculated newdata ses standard error prediction cell newdata newdata=NULL last three entries NA.","code":""},{"path":"/reference/dsm_varprop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance propagation for density surface models — dsm_varprop","text":"make predictions spatial model, also want know uncertainty abundance estimate. Since density surface models 2 () stage models, need incorporate uncertainty earlier stages (.e. detection function) \"final\" uncertainty estimate. function refit spatial model include Hessian offset extra term. Variance estimates using new model can used calculate variance predicted abundance estimates incorporate detection function uncertainty. Importantly requires detection function covariates, vary within segment (, example covariates like sex used). information construct prediction grid data.frame, newdata, see predict.dsm. routine useful detection function covariates used DSM. Note can use var.type=\"Vc\" (see gamObject), variance-covariance matrix spatial model, corrected smoothing parameter uncertainty. See Wood, Pya & S\\\"afken (2016) information. Models fixed scale parameters (e.g., negative binomial) require extra round optimisation.","code":""},{"path":"/reference/dsm_varprop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Variance propagation for density surface models — dsm_varprop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_varprop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance propagation for density surface models — dsm_varprop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535. Wood, S.N., Pya, N. S\\\"afken, B. (2016) Smoothing parameter model selection general smooth models. Journal American Statistical Association, 1-45.","code":""},{"path":"/reference/dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance propagation for density surface models — dsm_varprop","text":"David L. Miller, based code Mark V. Bravington Sharon L. Hedley.","code":""},{"path":"/reference/dsm_var_gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm_var_gam","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"one willing assume detection function spatial model independent, function produce estimates variance predictions abundance, using result squared coefficients variation add.","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"dsm_var_gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"list elements model fitted model object pred.var variance regions given pred.data. bootstrap logical, always FALSE model fitted model extra term dsm.object original model (dsm.obj )","code":""},{"path":"/reference/dsm_var_gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"David L. Miller","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"if (FALSE) {  library(Distance)  library(dsm)   # load the Gulf of Mexico dolphin data (see ?mexdolphins)  data(mexdolphins)   # fit a detection function and look at the summary  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL)  summary(hr.model)   # fit a simple smooth of x and y  mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)   # Calculate the variance  # this will give a summary over the whole area in mexdolphins$preddata  mod1.var <- dsm_var_gam(mod1, preddata, off.set=preddata$area) }"},{"path":"/reference/dsm_var_movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Estimate variance abundance area using moving block bootstrap. Two procedures implemented, one incorporating detection function uncertainty, one .","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"dsm_var_movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm_var_movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL -- file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm_var_movblk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Setting ds.uncertainty=TRUE incorporate detection function uncertainty directly bootstrap. done generating observations fitted detection function re-fitting new detection function (form), calculating new effective strip width. Rejection sampling used generate observations (except half-normal case) procedure can rather slow. Note currently supported covariates detection function. Setting ds.uncertainty=FALSE incorporate detection function uncertainty using delta method. assumes detection function spatial model INDEPENDENT. probably reasonable.","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata) summary(mod1)  # calculate the variance by 500 moving block bootstraps mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,    block.size = 3, samp.unit.name = \"Transect.Label\",    off.set = preddata$area,    bar = TRUE, bs.file = \"mexico-bs.csv\", ds.uncertainty = TRUE) }"},{"path":"/reference/dsm_var_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm_var_prop","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"ensure uncertainty detection function correctly propagated final variance estimate abundance, function uses method first detailed Williams et al (2011), explanation given Bravington et al. (2021).","code":""},{"path":"/reference/dsm_var_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"","code":"dsm_var_prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"list elements model fitted model object pred.var variance region given pred.data bootstrap logical, always FALSE pred.data .set model fitted model extra term dsm.object original model, model.check simple check subtracting coefficients two models see large difference deriv numerically calculated Hessian offset","code":""},{"path":"/reference/dsm_var_prop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"idea refit spatial model including extra random effect. random effect zero mean hence effect point estimates. variance Hessian detection function. Variance estimates incorporate detection function uncertainty. mathematical details given paper references . Many prediction grids can supplied supplying list data.frames function. Note routine simply calls dsm_varprop. require multiple prediction grids, routine probably faster. routine useful detection function covariates used DSM.","code":""},{"path":"/reference/dsm_var_prop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_var_prop.html","id":"limitations","dir":"Reference","previous_headings":"","what":"Limitations","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Note routine useful detection function used DSM. used abundance.est density.est responses used. Importantly requires detection function covariates, vary within segment (, example covariates like sex used).","code":""},{"path":"/reference/dsm_var_prop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535.","code":""},{"path":"/reference/dsm_var_prop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Mark V. Bravington, Sharon L. Hedley. Bugs added David L. Miller.","code":""},{"path":"/reference/dummy_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection function objects when detection is certain — dummy_ddf","title":"Detection function objects when detection is certain — dummy_ddf","text":"Create detection function object strip/plot surveys use density surface models.","code":""},{"path":"/reference/dummy_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection function objects when detection is certain — dummy_ddf","text":"","code":"dummy_ddf(object, size = 1, width, left = 0, transect = \"line\")"},{"path":"/reference/dummy_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection function objects when detection is certain — dummy_ddf","text":"object numeric vector object identifiers, relating object field observation data DSM. size group size observation (default groups size 1) width right truncation left left truncation (default 0, left truncation) transect \"line\" \"point\" transect","code":""},{"path":"/reference/dummy_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection function objects when detection is certain — dummy_ddf","text":"David L Miller","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data from a fitted detection function — generate.ds.uncertainty","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"using dsm.var.movblk ds.uncertainty=TRUE, procedure generates data fitted detection function (assuming correct).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"","code":"generate.ds.uncertainty(ds.object)"},{"path":"/reference/generate.ds.uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"ds.object fitted detection function object (returned call ddf.ds).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"function changes random number generator seed. avoid potential side-effects, use something like: seed <- get(\".Random.seed\",envir=.GlobalEnv) running code assign(\".Random.seed\",seed,envir=.GlobalEnv) .","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"David L. Miller","code":""},{"path":"/reference/generate.mb.sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Moving block bootstrap sampler — generate.mb.sample","title":"Moving block bootstrap sampler — generate.mb.sample","text":"usually used , called within dsm.var.movblk.","code":""},{"path":"/reference/generate.mb.sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Moving block bootstrap sampler — generate.mb.sample","text":"","code":"generate.mb.sample(   num.blocks.required,   block.size,   which.blocks,   dsm.data,   unit.info,   n.units )"},{"path":"/reference/generate.mb.sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Moving block bootstrap sampler — generate.mb.sample","text":"num.blocks.required number blocks need. block.size number segments per block. .blocks blocks sampled. dsm.data $data element result call dsm. unit.info result calling block.info.per.su. n.units number sampling units.","code":""},{"path":"/reference/generate.mb.sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Moving block bootstrap sampler — generate.mb.sample","text":"vector log-residuals","code":""},{"path":"/reference/latlong2km.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert latitude and longitude to Northings and Eastings — latlong2km","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Convert longitude latitude co-ordinates kilometres west-east south-north axes (lon0,lat0) using \"spherical law cosines\".","code":""},{"path":"/reference/latlong2km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"","code":"latlong2km(lon, lat, lon0 = sum(range(lon))/2, lat0 = sum(range(lat))/2)"},{"path":"/reference/latlong2km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"lon longitude lat latitude lon0 longitude reference point (defaults mean longitude) lat0 latitude reference point (defaults mean latitude)","code":""},{"path":"/reference/latlong2km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"list elements km.e km.n.","code":""},{"path":"/reference/latlong2km.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"WARNING: approximate procedure converting latitude/ longitude Northing/Easting. Consider using projection conversions available packages sp, sf rgdal better results.","code":""},{"path":"/reference/latlong2km.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Simon N. Wood","code":""},{"path":"/reference/make.soapgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"routine simply creates grid knots (correct format) used \"internal\" part soap film smoother","code":""},{"path":"/reference/make.soapgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"","code":"make.soapgrid(bnd, n.grid)"},{"path":"/reference/make.soapgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"bnd list elements x y give locations boundary vertices. first last elements . n.grid either one number giving number points along x y axes used create grid, vector giving number x direction, y direction.","code":""},{"path":"/reference/make.soapgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"list elements x y, containing knot locations.","code":""},{"path":"/reference/make.soapgrid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"David L Miller","code":""},{"path":"/reference/mexdolphins.html","id":null,"dir":"Reference","previous_headings":"","what":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Data combination several NOAA shipboard surveys conducted pan-tropical spotted dolphins Gulf Mexico. data consist 47 observations groups dolphins. group size recorded, well Beaufort sea state time observation. Coordinates observation bathymetry data also available covariates analysis. complete example analysis (description data) provided http://distancesampling.org/R/vignettes/mexico-analysis.html.","code":""},{"path":"/reference/mexdolphins.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Halpin, P.N., .J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, . Dimatteo, J. Cleary, C. Good, L.B. Crowder, K.D. Hyrenbach. 2009. OBIS-SEAMAP: world data center marine mammal, sea bird, sea turtle distributions. Oceanography 22(2):104-115 NOAA Southeast Fisheries Science Center. 1996. Report Cetacean Survey Oceanic Selected Continental Shelf Waters Northern Gulf Mexico aboard NOAA Ship Oregon II (Cruise 220)","code":""},{"path":"/reference/obs_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Observed versus expected diagnostics for fitted DSMs — obs_exp","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"Given covariate, calculate observed expected counts unique value covariate. can useful goodness fit check DSMs.","code":""},{"path":"/reference/obs_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"obs_exp(model, covar, cut = NULL)"},{"path":"/reference/obs_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"model fitted dsm model object covar covariate aggregate (character) cut vector cut points aggregate . supplied, unique values covar used.","code":""},{"path":"/reference/obs_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"data.frame values observed expected counts.","code":""},{"path":"/reference/obs_exp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"One strategy model checking calculate observed expected counts different aggregations variable. match well model fit good.","code":""},{"path":"/reference/obs_exp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"David L Miller, suggestion Mark Bravington.","code":""},{"path":"/reference/obs_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # example with the Gulf of Mexico dolphin data data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) }"},{"path":"/reference/plot.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a density surface model. — plot.dsm","title":"Plot a density surface model. — plot.dsm","text":"See plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a density surface model. — plot.dsm","text":"","code":"# S3 method for dsm plot(x, ...)"},{"path":"/reference/plot.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a density surface model. — plot.dsm","text":"x model fitted dsm ... arguments passed plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a density surface model. — plot.dsm","text":"plot!","code":""},{"path":[]},{"path":"/reference/plot.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a density surface model. — plot.dsm","text":"David L. Miller","code":""},{"path":"/reference/plot.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Create plots of abundance uncertainty — plot.dsm.var","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"Note prediction data set must x y columns even used model.","code":""},{"path":"/reference/plot.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"","code":"# S3 method for dsm.var plot(   x,   poly = NULL,   limits = NULL,   breaks = NULL,   legend.breaks = NULL,   xlab = \"x\",   ylab = \"y\",   observations = TRUE,   plot = TRUE,   boxplot.coef = 1.5,   x.name = \"x\",   y.name = \"y\",   gg.grad = NULL,   ... )"},{"path":"/reference/plot.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"x dsm.var object poly list data.frame columns x y, gives coordinates polygon draw. may also optionally column group, many polygons. limits limits fill colours breaks breaks colour fill legend.breaks breaks displayed xlab label x axis ylab label y axis observations observations plotted? plot actually plot map, just return ggplot2 object? boxplot.coef control trimming (summary.dsm.var), effect bootstrap file saved. x.name name variable plot x axis. y.name name variable plot y axis. gg.grad optional ggplot gradient object. ... arguments","code":""},{"path":"/reference/plot.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"plot","code":""},{"path":"/reference/plot.dsm.var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"order get plotting work dsm_var_prop dsm_var_gam, one must first format data correctly since functions designed compute general summaries. One summary calculated element list pred supplied dsm_var_prop dsm_var_gam. plot uncertainty prediction grid, pred (data.frame), say, can create correct format simply using pred.new <- split(pred,1:nrow(pred)).","code":""},{"path":[]},{"path":"/reference/plot.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/plot_pred_by_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially plot predictions per model term — plot_pred_by_term","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"Plot effect smooth model spatially. term model, plot effect space. Plots made scale, relative influence smooth can seen.","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"plot_pred_by_term(dsm.obj, data, location.cov = c(\"x\", \"y\"))"},{"path":"/reference/plot_pred_by_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"dsm.obj fitted dsm object data data use plot (often prediction grid), data also include width height columns plotting location.cov deprecated, use location.cov","code":""},{"path":"/reference/plot_pred_by_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"ggplot2 plot","code":""},{"path":"/reference/plot_pred_by_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"David L Miller (idea taken inlabru)","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data and fit a model data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y) + s(depth), hr.model, segdata, obsdata)  preddata$width <- preddata$height <- sqrt(preddata$area)  # make the plot plot_pred_by_term(mod1, preddata, c(\"x\",\"y\"))  # better plot would be # library(viridis) # plot_pred_by_term(mod1, preddata, c(\"x\",\"y\")) + scale_fill_viridis() }"},{"path":"/reference/predict.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from a fitted density surface model — predict.dsm","title":"Predict from a fitted density surface model — predict.dsm","text":"Make predictions density abundance outside (inside) covered area.","code":""},{"path":"/reference/predict.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from a fitted density surface model — predict.dsm","text":"","code":"# S3 method for dsm predict(object, newdata = NULL, off.set = NULL, type = \"response\", ...)"},{"path":"/reference/predict.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from a fitted density surface model — predict.dsm","text":"object fitted dsm object newdata spatially referenced covariates e.g. altitude, depth, distance shore, etc. Covariates data.frame must names identical variable names used fitting model .set area cells prediction grid. units segments/distances given dsm. Replaces column newdata called .set supplied. Ignored newdata supplied type scale results . default \"response\", see predict.gam explanation options (usually necessary) ... arguments passed predict.gam","code":""},{"path":"/reference/predict.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from a fitted density surface model — predict.dsm","text":"predicted values response scale default (unless type specified, case see predict.gam).","code":""},{"path":"/reference/predict.dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict from a fitted density surface model — predict.dsm","text":"newdata supplied, predictions made data built model. Note order results necessarily segdata (segment data) data.frame supplied dsm. area .set used argument supplied, otherwise look areas column named .set newdata. Either way link function (usually log) applied offsets, need log passing function.","code":""},{"path":[]},{"path":"/reference/predict.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict from a fitted density surface model — predict.dsm","text":"David L. Miller","code":""},{"path":"/reference/predict.fake_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for fake detection functions — predict.fake_ddf","title":"Prediction for fake detection functions — predict.fake_ddf","text":"Prediction function dummy detection functions. function returns many 1s rows newdata. esw=TRUE strip width returned.","code":""},{"path":"/reference/predict.fake_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for fake detection functions — predict.fake_ddf","text":"","code":"# S3 method for fake_ddf predict(   object,   newdata = NULL,   compute = FALSE,   int.range = NULL,   esw = FALSE,   ... )"},{"path":"/reference/predict.fake_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for fake detection functions — predict.fake_ddf","text":"object model object newdata many 1s return? compute unused, compatibility mrds::predict int.range unused, compatibility mrds::predict esw strip width returned? ... S3 consistency","code":""},{"path":"/reference/predict.fake_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction for fake detection functions — predict.fake_ddf","text":"David L Miller","code":""},{"path":"/reference/print.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model object — print.dsm","title":"Print a description of a density surface model object — print.dsm","text":"method just gives short description fitted model. Use summary.dsm method information.","code":""},{"path":"/reference/print.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model object — print.dsm","text":"","code":"# S3 method for dsm print(x, ...)"},{"path":"/reference/print.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model object — print.dsm","text":"x model fitted dsm ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model object — print.dsm","text":"NULL","code":""},{"path":"/reference/print.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model object — print.dsm","text":"David L. Miller","code":""},{"path":"/reference/print.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm.var","title":"Print a description of a density surface model variance object — print.dsm.var","text":"method provides short summary, use summary.dsm.var method information.","code":""},{"path":"/reference/print.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm.var","text":"","code":"# S3 method for dsm.var print(x, ...)"},{"path":"/reference/print.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm.var","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model variance object — print.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm_varprop","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"method provides short summary, see summary.dsm_varprop.","code":""},{"path":"/reference/print.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"","code":"# S3 method for dsm_varprop print(x, ...)"},{"path":"/reference/print.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":[]},{"path":"/reference/print.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm.var","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"See summary.dsm.var information.","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"","code":"# S3 method for summary.dsm.var print(x, ...)"},{"path":"/reference/print.summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm_varprop","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"See summary.dsm_varprop information.","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"","code":"# S3 method for summary.dsm_varprop print(x, ...)"},{"path":"/reference/print.summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/rqgam.check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"function deprecated, use rqgam_check.","code":""},{"path":"/reference/rqgam.check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"","code":"rqgam.check(gam.obj, ...)"},{"path":"/reference/rqgam.check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Reproduces \"Resids vs. linear pred\" plot gam.check using randomised quantile residuals, la Dunn Smyth (1996). Checks heteroskedasticity usual, looking \"funnel\"-type structures points, much easier randomised quantile residuals deviance residuals, model uses count distribution response.","code":""},{"path":"/reference/rqgam_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"rqgam_check(gam.obj, ...)"},{"path":"/reference/rqgam_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"just plots!","code":""},{"path":"/reference/rqgam_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Note function works negative binomial Tweedie response distributions. Earlier versions function produced full gam.check output, confusing one plots really useful.  Checks k computed, need done using gam.check.","code":""},{"path":"/reference/rqgam_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Based code Natalie Kelly, bugs added Dave Miller","code":""},{"path":"/reference/rqgam_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"# \\donttest{ library(Distance) library(dsm) library(tweedie)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.418 #> No survey area information supplied, only estimating detection function.  # fit a simple smooth of x and y with a Tweedie response with estimated #  p parameter mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw()) #> Warning: Some observations are outside of detection function 1 truncation! rqgam_check(mod1)  # }"},{"path":"/reference/summary.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted density surface model — summary.dsm","title":"Summarize a fitted density surface model — summary.dsm","text":"Gives brief summary fitted dsm object.","code":""},{"path":"/reference/summary.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted density surface model — summary.dsm","text":"","code":"# S3 method for dsm summary(object, ...)"},{"path":"/reference/summary.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted density surface model — summary.dsm","text":"object model fitted dsm ... arguments passed summary.gam","code":""},{"path":"/reference/summary.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted density surface model — summary.dsm","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize a fitted density surface model — summary.dsm","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm.var","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"Gives brief summary fitted dsm variance object.","code":""},{"path":"/reference/summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"","code":"# S3 method for dsm.var summary(   object,   alpha = 0.05,   boxplot.coef = 1.5,   bootstrap.subregions = NULL,   ... )"},{"path":"/reference/summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95\\ confidence intervals) boxplot.coef value coef used calculate outliers see boxplot. bootstrap.subregions list vectors logicals indices subregions variances need calculated (bootstraps (see dsm.var.prop use subregions variance propagation). ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm_varprop","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"Gives brief summary fitted dsm_varprop variance object.","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"","code":"# S3 method for dsm_varprop summary(object, alpha = 0.05, ...)"},{"path":"/reference/summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95% confidence internal) ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/trim.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimmed variance — trim.var","title":"Trimmed variance — trim.var","text":"Trim variance estimates bootstrap. defined percentage defined amount necessary bring median trimmed mean within 8% defined 'outliers'.","code":""},{"path":"/reference/trim.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimmed variance — trim.var","text":"","code":"trim.var(untrimmed.bootstraps, boxplot.coef = 1.5)"},{"path":"/reference/trim.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimmed variance — trim.var","text":"untrimmed.bootstraps (usually $study.area.total element returned dsm.var.movblk bootstrap object. boxplot.coef value coef used calculate outliers see boxplot.","code":""},{"path":"/reference/trim.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimmed variance — trim.var","text":"trimmed variance","code":""},{"path":"/reference/trim.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trimmed variance — trim.var","text":"Louise Burt","code":""},{"path":"/reference/vis.concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis.concurvity","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"function deprecated, use vis_concurvity.","code":""},{"path":"/reference/vis.concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"","code":"vis.concurvity(model, type = \"estimate\")"},{"path":"/reference/vis.concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis_concurvity","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"Plot measures much one term model explained another. values high, one consider re-running variable selection one offending variables removed check stability term selection.","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"vis_concurvity(model, type = \"estimate\")"},{"path":"/reference/vis_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"methods considered somewhat experimental time. Consult concurvity information concurvity measures calculated.","code":""},{"path":"/reference/vis_concurvity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"David L Miller","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"if (FALSE) { library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata)  # visualise concurvity using the \"estimate\" metric vis_concurvity(mod1) }"},{"path":"/news/index.html","id":"dsm-234","dir":"Changelog","previous_headings":"","what":"dsm 2.3.4","title":"dsm 2.3.4","text":"fix bug predictions NA (e.g., predictions outside soap boundary), variances NA due propagation (#4)","code":""},{"path":"/news/index.html","id":"dsm-233","dir":"Changelog","previous_headings":"","what":"dsm 2.3.3","title":"dsm 2.3.3","text":"CRAN release: 2022-08-20 dsm.var.prop -> dsm_var_prop dsm.var.gam -> dsm_var_gam dsm.var.movblk -> dsm_var_movblk dsm.cor -> dsm_cor rqgam.check -> rqgam_check vis.concurvity -> vis_concurvity var_type -> var.type","code":""},{"path":"/news/index.html","id":"dsm-232","dir":"Changelog","previous_headings":"","what":"dsm 2.3.2","title":"dsm 2.3.2","text":"CRAN release: 2022-03-17 fixed bug offset calculation dsm_varprop se estimation offsets logged twice. Thanks Megan Ferguson spotting . documentation now rmarkdown format dsm 2.3.1 transect type (line point) now determined detection function rather using transect= argument multiple detection functions now possible, use list() fitted detection functions supplied ddf.obj= argument dsm(). dsm.var.gam, dsm.var.prop dsm_varprop compatible. Vignette coming soon. Thanks Dave Fifield Ewan Wakefield extensive testing suggestions. See ?“dsm-data” information set data. strip transects now handled using dummy_ddf() function. presence~ response type removed Response options “D”, “density”, “Dhat”, “N”, “Nhat” “n” deprecated. Now “count”, “abundance.est” “density.est” allowed. Make predict.dsm work tibbles .set column newdata (error thrown previous code’s check ). Thanks Dave Fifield reporting issue. Fix bug inconsistent predictions density.est used response. Code predict.dsm simplified documentation updated. Thanks Dave Fifield reporting issue. mrds independent observer (io) trial models allowed detection function.Variance can estimated via dsm_varprop (usual limitation covariates may vary segment level) dsm.var.gam. feature currently experimental. Thanks Doug Sigourney Megan Ferguson help suggestions. See ?“dsm-data” information set data. example multiddf mrds analysis dsm https://github.com/densitymodelling/nefsc_fin_mrds_dsm availability= argument now works differently count models. availability must now length number segments (nrow(segment.data)) multiply offset (effective area) rather number animals observed coherent detectability handled case. dsm_varprop longer requies newdata supplied. newdata isn’t present just refitted original models returned, entries NA.","code":""},{"path":"/news/index.html","id":"dsm-230","dir":"Changelog","previous_headings":"","what":"dsm 2.3.0","title":"dsm 2.3.0","text":"CRAN release: 2020-04-22 fixed dsm_varprop (gam.fixed.priors) response distributions fixed scale parameters. Negative binomial results now may incorrect!","code":""},{"path":"/news/index.html","id":"dsm-2218","dir":"Changelog","previous_headings":"","what":"dsm 2.2.18","title":"dsm 2.2.18","text":"Clarification predict.dsm return documentation. Thanks Dave Fifield suggestion. .set=NULL lead predictions 0 density used. Now just returns density. Thanks Dave Fifield catch.","code":""},{"path":"/news/index.html","id":"dsm-2217","dir":"Changelog","previous_headings":"","what":"dsm 2.2.17","title":"dsm 2.2.17","text":"CRAN release: 2019-01-19 Now Suggests package “sp” (needed parts mexdolphin example data)","code":""},{"path":"/news/index.html","id":"dsm-2216","dir":"Changelog","previous_headings":"","what":"dsm 2.2.16","title":"dsm 2.2.16","text":"CRAN release: 2018-06-27 check Sample.Labels match , none throw error. Thanks Madhura Davate bringing issue attention. gamma option removed, since now ignored gam(). using method=“GCV.Cp” gamma=1.4 still used (generally advise using REML rather GCV smoothing parameter selection) dsm now requires mgcv version 1.8-23 higher. dsm.var.prop (dsm_varprop) now throw error covariates detection function can’t anything useful new function obs_exp diagnostic function compare observed expected counts different covariate aggregations","code":""},{"path":"/news/index.html","id":"dsm-2215","dir":"Changelog","previous_headings":"","what":"dsm 2.2.15","title":"dsm 2.2.15","text":"CRAN release: 2017-07-03 mexdolphins loaded using data(), data longer need attach()ed dsm.var.prop throws error count N used response. dsm.var.prop now calls dsm_varprop, keeps scale parameter constant refitting models variance propagation. dsm_varprop provides lower-level interface variance propagation probably faster flexible. add vis.concurvity visually assess concurvity (colinearity extended smooth terms) model new model diagnostic function: plot_pred_by_term – plots effect smooth model spatially. remove “abundance” option response, use “count” dsm.var.prop dsm_varprop better diagnostics checking variance model hasn’t done something weird dsm.var.movblk discards replicates model didn’t converge fix potential issue negative binomial response used variance propagation (due scale parameterisations). dsm_varprop now refits nb() models negbin() advise users look deeply. Thanks Eric Rexstad spotting . fix bug predict.dsm() predictions “density” response including offset. Thanks Len Thomas spotting .","code":""},{"path":"/news/index.html","id":"dsm-2214","dir":"Changelog","previous_headings":"","what":"dsm 2.2.14","title":"dsm 2.2.14","text":"CRAN release: 2017-01-26 nasty bug predict.dsm fixed: logging offset twice newdata provided, leading odd checking plots. also effected results predict() newdata= specified. Thanks Megan Ferguson alerting us . add support point transects(!) rqgam.check() now produced linear predictor vs. randomised quantile residuals plot avoid confusion interpreting plots REML now default method smoothing parameter selection","code":""},{"path":"/news/index.html","id":"dsm-2213","dir":"Changelog","previous_headings":"","what":"dsm 2.2.13","title":"dsm 2.2.13","text":"CRAN release: 2016-10-03 dsm.var.gam now example dsm.var.prop now checks detection function model covariates","code":""},{"path":"/news/index.html","id":"dsm-2212","dir":"Changelog","previous_headings":"","what":"dsm 2.2.12","title":"dsm 2.2.12","text":"CRAN release: 2016-08-10 alpha argument now works analytical variance reported using dsm.var.prop dsm.var.gam print.summary summary methods. Thanks Jason Roberts Eric Rexstad spotting issue. dsm now uses numDeriv::grad calculate derivatives variance estimation – much faster. Thanks Jason Roberts fix. dsm now requires mrds version 2.1.15 higher, due internal changes fixed issue Effort column required even segment.area specified. Thanks Greg Lollback spotting issue. ensured ordering segments matched ordering observations aggregation observations correct. Thanks Dave Fifield reporting bug suggesting fix. check.cols longer checks see column called “distance” strip transects used","code":""},{"path":"/news/index.html","id":"dsm-2211","dir":"Changelog","previous_headings":"","what":"dsm 2.2.11","title":"dsm 2.2.11","text":"dsm.var.prop uses update() rather messing around eval() print.summary.dsm.var didn’t work GAM variance detection function specified, now fixed (thanks Natalia Dellabianca spotting ) Removed non-working plot() code variance examples – see Mexico pantropical spotted dolphins proper treatment plotting using projections (http://distancesampling.org/R/vignettes/mexico-analysis.html) plot.var.gam longer crashes default distance data doesn’t columns “x” “y”","code":""},{"path":"/news/index.html","id":"dsm-2210","dir":"Changelog","previous_headings":"","what":"dsm 2.2.10","title":"dsm 2.2.10","text":"corrected offsets examples dsm, dsm.var.prop, dsm.var.movblk updated mexdolphins data lat/long reversal prediction data non-list storage stopped use rqgam.check used non-negbin/Tweedie responses (thanks Phil Bouchet highlighting issue).","code":""},{"path":"/news/index.html","id":"dsm-229","dir":"Changelog","previous_headings":"","what":"dsm 2.2.9","title":"dsm 2.2.9","text":"CRAN release: 2015-07-29 Moving block bootstrap now works detection function (suggested Laura Williamson) Mexico dolphin data areas prediction grids segments re-projected. Thanks Phil Bouchet pointing error. Context: https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ","code":""},{"path":"/news/index.html","id":"dsm-228","dir":"Changelog","previous_headings":"","what":"dsm 2.2.8","title":"dsm 2.2.8","text":"Speed reduced memory usage variance calculations, thanks Filipe Dias asking . Better error observations matched detection function. Thanks Ricardo Lima highlighting . Check missing columns incorrect logic, now corrected tests. Thanks Julia Migné spotting .","code":""},{"path":"/news/index.html","id":"dsm-227","dir":"Changelog","previous_headings":"","what":"dsm 2.2.7","title":"dsm 2.2.7","text":"Plotting check plots using random quantile residuals (rqgam.check) didn’t work Tweedie tw family models, now fixed. Thanks Laura Mannocci Jason Roberts pointing . Plotting CV used matrix() coerce data data.frame – thus converting elements simplest type. Errors ensued. Thanks Adrian Schiavini finding bug!","code":""},{"path":"/news/index.html","id":"dsm-226","dir":"Changelog","previous_headings":"","what":"dsm 2.2.6","title":"dsm 2.2.6","text":"dsm.var.prop dsm.var.gam now allow supply single number offset, copy many times need. Thanks Filipe Dias suggesting .","code":""},{"path":"/news/index.html","id":"dsm-225","dir":"Changelog","previous_headings":"","what":"dsm 2.2.5","title":"dsm 2.2.5","text":"CRAN release: 2014-09-16 dsm.var.prop now throw error detection function dsm ’s trying calculate variance (thanks Adrián Schiavini finding ) variance/CV plotting threw error detection function supplied observation=TRUE. Now error sidestepped (, thanks Adrián Schiavini finding ).","code":""},{"path":"/news/index.html","id":"dsm-224","dir":"Changelog","previous_headings":"","what":"dsm 2.2.4","title":"dsm 2.2.4","text":"CRAN release: 2014-06-17 changed stop() warning() data detection function included observation data frame, automatically excluded later, bug found Eric Rexstad Anna Cucknell","code":""},{"path":"/news/index.html","id":"dsm-223","dir":"Changelog","previous_headings":"","what":"dsm 2.2.3","title":"dsm 2.2.3","text":"CRAN release: 2014-04-23 moving block boostrap using observed abundances residuals therefore gave wrong answers (Eric Rexstad found bug)","code":""},{"path":"/news/index.html","id":"dsm-222","dir":"Changelog","previous_headings":"","what":"dsm 2.2.2","title":"dsm 2.2.2","text":"version number reporting now works (Len Thomas found bug) documentation default density presence models now weighted area segment","code":""},{"path":"/news/index.html","id":"dsm-221","dir":"Changelog","previous_headings":"","what":"dsm 2.2.1","title":"dsm 2.2.1","text":"switch new testthat spec added new response variables (Dhat, density.est, count, n), suggestion Len Thomas","code":""},{"path":"/news/index.html","id":"dsm-214","dir":"Changelog","previous_headings":"","what":"dsm 2.1.4","title":"dsm 2.1.4","text":"check observed distances within width now throw error","code":""},{"path":"/news/index.html","id":"dsm-213","dir":"Changelog","previous_headings":"","what":"dsm 2.1.3","title":"dsm 2.1.3","text":"CRAN release: 2013-08-20 CRAN-compatibility fixes","code":""},{"path":"/news/index.html","id":"dsm-212","dir":"Changelog","previous_headings":"","what":"dsm 2.1.2","title":"dsm 2.1.2","text":"updates dsm.cor unified plotting variance estimation tweaks non-dsm models","code":""},{"path":"/news/index.html","id":"dsm-206","dir":"Changelog","previous_headings":"","what":"dsm 2.0.6","title":"dsm 2.0.6","text":"removed warning () comparison length make.soapgrid need pass bnd list() dsm.cor now handles gamm objects dsm.cor allows one check residual autocorrelation models presence/absence data now longer needs dummy detection function object","code":""},{"path":"/news/index.html","id":"dsm-205","dir":"Changelog","previous_headings":"","what":"dsm 2.0.5","title":"dsm 2.0.5","text":"started NEWS file fixed bug dsm.var.gam() didn’t return predictions plotting result fail silly typo docs check ’re using mgcv version >= 1.7-24 GAMM fitting warn ","code":""}]
+[{"path":"/articles/data_format/dsm-data-formatting.html","id":"introduction","dir":"Articles > Data_format","previous_headings":"","what":"Introduction","title":"`dsm` data formatting example","text":"dsm package expects data specific format. example ’ll show get data format give explanation requirements exist. recommend getting grips fitting models dsm reading tutorial know end point look like trying grapple data. example Gulf Mexico pantropical spotted dolphins available .","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"prerequisites","dir":"Articles > Data_format","previous_headings":"","what":"Prerequisites","title":"`dsm` data formatting example","text":"run code , need ggplot2 sf libraries installed. can following code: Rhode Island data required downloaded : loon observations coastline dbf coastline projection coastline shapefile coastline shx transects dbf transects projection transects shapefile transects shx placed folder called data code work.","code":"install.packages(c(\"ggplot2\", \"sf\"))"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"overview","dir":"Articles > Data_format","previous_headings":"","what":"Overview","title":"`dsm` data formatting example","text":"collect distance sampling data, need record (1) distance observation (along detectability covariates like weather), (2) transect currently , (3) much effort expended transect (length number visits). want fit DSM information needs expanded include transect , can build spatial model. point transects, simple always point, line transects need know position detection (equivalently, far along transect ). abstractly, need information detections effort. also need link two. dsm use three objects hold information: fitted detection function (argument ddf.obj) holds detections ignores spatial information “segments” (argument segment.data) holds chunks effort (parts transect point visits) locations. might also include environmental covariate information. table linking two (argument observation.data), making sure detection corresponding segment. image gives overview different data.frames interact, along corresponds real life situation collecting data transect. rest example goes table turn, explaining construction.","code":""},{"path":[]},{"path":"/articles/data_format/dsm-data-formatting.html","id":"detection-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Detection data","title":"`dsm` data formatting example","text":"data requirements similar packages mrds Distance. cases, detection corresponding row. analysis using dsm additional requirements data (can auto-generated Distance recommend ensure know records link correctly). Distance need: distance : distance detection object : unique detection identifier (link observation table) size : number individuals detection (1 objects occur alone) mrds requires following extra columns (allow double observer surveys): detected : 1 detected observer 0 missed (always 1 single observer) observer : observer number (1 2) (always 1 single observer) covariates affect detectability recorded (e.g., sex weather) can included data analysis mrds Distance. model fitted, deal fitted model object dsm analysis.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segment-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Segment data","title":"`dsm` data formatting example","text":"use term “segment” refer points point transects small chunks transect line transect case. ’ll assume ’ve already segmented lines first describing data format, go example segment line transect data. data.frame required segments needs following columns: Effort : effort expended segment (either length segment lines, number visits points) Sample.Label : unique identifier segment (engaged complicated survey, might take format like “YEAR-AREA-LINE-SEGMENT”, labels like “2020-North--15”, can useful keep track data came later). addition, environmental covariates like location relevant covariates (sea surface temperature, foliage type, altitude, bathymetry, etc) can included data.frame used spatial model.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segmenting","dir":"Articles > Data_format","previous_headings":"Table construction > Segment data","what":"Segmenting","title":"`dsm` data formatting example","text":"data lines want chop segments, section give sample R code . things work extremely dependent input data, hopefully gives template want least. jump : fluent ArcGIS QGIS recommend task software familiar first. simple guide task QGIS ArcGIS recommend MGET toolbox. example, look aerial survey seabirds coast Rhode Island, USA. Data surveys collected University Rhode Island analysed K. Winiarski, Miller, Paton, & McWilliams (2013), K. J. Winiarski, Burt, et al. (2014) K. J. Winiarski, Miller, Paton, & McWilliams (2014). transects saved shapefile “chop-” segments. start let’s plot data coastline:  code loaded sf package. package use spatial work segmenting data. tools expected GIS, though package still developed functionality always increasing. Investigating transects data loaded, can see consists data.frame extra bits (spatial information, including locations (geometry) projection (CRS)): one row per transect (total 24 transects), consists line joining start end points make transects. want can plot individual rows via plot(transects[1,]); can useful check row single line, processing needed (can tricky covered tutorial see “information” ). data looks good shape, can use st_segmentize function take transect make segments . make sure everything works correctly, need project data first. ’m using appropriate projected coordinate system (EPSG code 6348), Rhode Island State Plane. know truncation used detection function 1000m (distances collected bins bin), since ’re trying make segments approximately square, set length twice (since truncation applies either side transect), 2000m. Looking : See now row many coordinates attached , just looking first row (transect 1) comparing coordinates transects segs transects just two coordinates (start end points line). Whereas: segs 5, corresponding segment cut points. now need break rows segs multiple rows, one per segment. bit fiddly. use function provided dieghernan site . first load function (don’t worry understanding code!): can use separate segments look results: now 590 segments 1km 2km long. can check lengths using st_length function plot histogram segment lengths:  Note setting dfMaxLength argument st_segmentize giving rough guide segment length algorithm inside st_segmentize tries get segments equal length possible (note \\(x\\) axis histogram). Note also Transect column segs now duplicate entries transect multiple segments . can now create required columns dsm. First, Effort column can generated using st_length: can generate Sample.Labels. ’ll use complicated naming scheme show ’s done, one simply write segs$Sample.Label <- 1:nrow(segs) sufficient. Instead, like Sample.Label “YEAR-AREA-LINE-SEGMENT” scheme suggested . fancier ways , clarity, let’s use loop: Now can check looks right: required columns taken care , can also calculate centroids segments get locations segment use spatial model. Handily can use st_centroid one step keep data time. get warning centroids might calculated correctly latitude/longitude used. need project data first (Rhode Island State Plane) step using st_transform. Notice geometry type now POINT. can plot centroids previous map:  present, dsm package doesn’t know talk sf objects, final step need simplify segs regular data.frame, dsm can interpret. can extract coordinates centroids using st_coordinates append onto data.frame version object geometry dropped, like : can double check looks right: done. See next section link segments detections. want include spatial covariate information segment table, use (example) rerrdap obtain remotely-sensed data. situ data (.e., weather conditions recorded effort), can complicated require use summarize, see thread information deal situation.","code":"library(ggplot2) library(sf) ## Warning: package 'sf' was built under R version 4.4.2 # coast data, just for reference coastline <- read_sf(\"data/ri_coast.shp\")  # our transect lines transects <- read_sf(\"data/ri_transects.shp\")  # now make the plot p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects, aes(colour=Transect)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal()  print(p) transects ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86581 41.25891) ##  2        2  (-71.82872 41.30738, -71.82145 41.2258) ##  3        3 (-71.78805 41.31562, -71.75676 40.94846) ##  4        4 (-71.74674 41.31728, -71.71394 40.92971) ##  5        5  (-71.70589 41.3241, -71.67156 40.91612) ##  6        6 (-71.66578 41.33943, -71.62329 40.82944) ##  7        7   (-71.5435 41.36397, -71.5064 40.91516) ##  8        8  (-71.5015 41.35994, -71.45266 40.82978) ##  9        9 (-71.46131 41.37378, -71.42458 40.92496) ## 10       10   (-71.421 41.38723, -71.37562 40.83641) ## # ℹ 16 more rows # project transects transects <- st_transform(transects, 6348)  # do the segmenting segs <- st_segmentize(transects, dfMaxLength=units::set_units(2000, \"metres\"))  # transform back to lat/long segs <- st_transform(segs, 4326) transects <- st_transform(transects, 4326) segs ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                                             geometry ##  *    <dbl>                                                     <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86826 41.2861, -71.86703 41.2725, -71.8658… ##  2        2 (-71.82872 41.30738, -71.82727 41.29106, -71.82581 41.27475, -71.82… ##  3        3 (-71.78805 41.31562, -71.78654 41.29813, -71.78505 41.28065, -71.78… ##  4        4 (-71.74674 41.31728, -71.74524 41.29967, -71.74373 41.28205, -71.74… ##  5        5 (-71.70589 41.3241, -71.70438 41.30636, -71.70288 41.28863, -71.701… ##  6        6 (-71.66578 41.33943, -71.6643 41.32185, -71.66283 41.30426, -71.661… ##  7        7 (-71.5435 41.36397, -71.542 41.34602, -71.54051 41.32807, -71.53901… ##  8        8 (-71.5015 41.35994, -71.49986 41.34227, -71.49821 41.3246, -71.4965… ##  9        9 (-71.46131 41.37378, -71.45983 41.35583, -71.45835 41.33788, -71.45… ## 10       10 (-71.421 41.38723, -71.41952 41.36946, -71.41804 41.3517, -71.41656… ## # ℹ 16 more rows st_coordinates(transects[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86581 41.25891  1 st_coordinates(segs[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86826 41.28610  1 ## [3,] -71.86703 41.27250  1 ## [4,] -71.86581 41.25891  1 stdh_cast_substring <- function(x, to = \"MULTILINESTRING\") {   ggg <- st_geometry(x)    if (!unique(st_geometry_type(ggg)) %in% c(\"POLYGON\", \"LINESTRING\")) {     stop(\"Input should be  LINESTRING or POLYGON\")   }   for (k in 1:length(st_geometry(ggg))) {     sub <- ggg[k]     geom <- lapply(       1:(length(st_coordinates(sub)[, 1]) - 1),       function(i)         rbind(           as.numeric(st_coordinates(sub)[i, 1:2]),           as.numeric(st_coordinates(sub)[i + 1, 1:2])         )     ) %>%       st_multilinestring() %>%       st_sfc()      if (k == 1) {       endgeom <- geom     }     else {       endgeom <- rbind(endgeom, geom)     }   }   endgeom <- endgeom %>% st_sfc(crs = st_crs(x))   if (class(x)[1] == \"sf\") {     endgeom <- st_set_geometry(x, endgeom)   }   if (to == \"LINESTRING\") {     endgeom <- endgeom %>% st_cast(\"LINESTRING\")   }   return(endgeom) } segs <- stdh_cast_substring(segs, to=\"LINESTRING\") ## Warning in st_cast.sf(., \"LINESTRING\"): repeating attributes for all ## sub-geometries for which they may not be constant segs ## Simple feature collection with 590 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1  (-71.86948 41.29969, -71.86826 41.2861) ##  2        1   (-71.86826 41.2861, -71.86703 41.2725) ##  3        1  (-71.86703 41.2725, -71.86581 41.25891) ##  4        2 (-71.82872 41.30738, -71.82727 41.29106) ##  5        2 (-71.82727 41.29106, -71.82581 41.27475) ##  6        2 (-71.82581 41.27475, -71.82436 41.25843) ##  7        2  (-71.82436 41.25843, -71.8229 41.24212) ##  8        2   (-71.8229 41.24212, -71.82145 41.2258) ##  9        3 (-71.78805 41.31562, -71.78654 41.29813) ## 10        3 (-71.78654 41.29813, -71.78505 41.28065) ## # ℹ 580 more rows hist(st_length(segs), xlab=\"Segment lengths (m)\", main=\"\") segs$Effort <- st_length(segs) # create a dummy column that we can fill in as we go segs$Sample.Label <- NA  # set the year and area once for this data year <- 2010 area <- \"RI\"  # loop over the transect IDs for(this_transect in unique(segs$Transect)){   # how many segments in this transect?   n_segs <- nrow(subset(segs, Transect==this_transect))   # generate the n_segs labels that we need   segs$Sample.Label[segs$Transect==this_transect] <- paste(year, area, this_transect,                                                            1:n_segs, sep=\"-\") } segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect                                 geometry Effort Sample.Label ##  *    <dbl>                         <LINESTRING [°]>    [m] <chr>        ##  1        1  (-71.86948 41.29969, -71.86826 41.2861)  1515. 2010-RI-1-1  ##  2        1   (-71.86826 41.2861, -71.86703 41.2725)  1515. 2010-RI-1-2  ##  3        1  (-71.86703 41.2725, -71.86581 41.25891)  1515. 2010-RI-1-3  ##  4        2 (-71.82872 41.30738, -71.82727 41.29106)  1818. 2010-RI-2-1  ##  5        2 (-71.82727 41.29106, -71.82581 41.27475)  1818. 2010-RI-2-2  ##  6        2 (-71.82581 41.27475, -71.82436 41.25843)  1818. 2010-RI-2-3  ##  7        2  (-71.82436 41.25843, -71.8229 41.24212)  1818. 2010-RI-2-4  ##  8        2   (-71.8229 41.24212, -71.82145 41.2258)  1818. 2010-RI-2-5  ##  9        3 (-71.78805 41.31562, -71.78654 41.29813)  1948. 2010-RI-3-1  ## 10        3 (-71.78654 41.29813, -71.78505 41.28065)  1948. 2010-RI-3-2  ## # ℹ 580 more rows # save the line version of segs for plotting later segs_lines <- segs  # project segs segs <- st_transform(segs, 6348)  # find centroids segs <- st_centroid(segs) ## Warning: st_centroid assumes attributes are constant over geometries # project back to lat/long segs <- st_transform(segs, 4326)  # how does that look? segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86887 ymin: 40.83823 xmax: -70.89506 ymax: 41.45646 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect             geometry Effort Sample.Label ##  *    <dbl>          <POINT [°]>    [m] <chr>        ##  1        1 (-71.86887 41.29289)  1515. 2010-RI-1-1  ##  2        1  (-71.86764 41.2793)  1515. 2010-RI-1-2  ##  3        1  (-71.86642 41.2657)  1515. 2010-RI-1-3  ##  4        2 (-71.82799 41.29922)  1818. 2010-RI-2-1  ##  5        2  (-71.82654 41.2829)  1818. 2010-RI-2-2  ##  6        2 (-71.82508 41.26659)  1818. 2010-RI-2-3  ##  7        2 (-71.82363 41.25028)  1818. 2010-RI-2-4  ##  8        2 (-71.82218 41.23396)  1818. 2010-RI-2-5  ##  9        3 (-71.78729 41.30687)  1948. 2010-RI-3-1  ## 10        3  (-71.7858 41.28939)  1948. 2010-RI-3-2  ## # ℹ 580 more rows p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=segs) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) segs_df <- cbind(as.data.frame(st_drop_geometry(segs)),               st_coordinates(segs)) head(segs_df) ##   Transect       Effort Sample.Label         X        Y ## 1        1 1515.147 [m]  2010-RI-1-1 -71.86887 41.29289 ## 2        1 1515.151 [m]  2010-RI-1-2 -71.86764 41.27930 ## 3        1 1515.155 [m]  2010-RI-1-3 -71.86642 41.26570 ## 4        2 1818.179 [m]  2010-RI-2-1 -71.82799 41.29922 ## 5        2 1818.184 [m]  2010-RI-2-2 -71.82654 41.28290 ## 6        2 1818.190 [m]  2010-RI-2-3 -71.82508 41.26659"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"linking-observations-and-segments","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Linking observations and segments","title":"`dsm` data formatting example","text":"link together data detection function spatial data giving effort, use observation data.frame. really just cross-reference two tables, detection lives one segment. observation data.frame must (least) following columns: object : unique object identifier (corresponding identifiers detection function) Sample.Label : identifier segment observation occurred size : size observed group (e.g., 1 animals occurred individually) distance : distance observation observation data many rows detection function.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"relating-observations-and-segments-in-practice","dir":"Articles > Data_format","previous_headings":"Table construction > Linking observations and segments","what":"Relating observations and segments in practice","title":"`dsm` data formatting example","text":"methods building observation table. ’ll illustrate one assigning detections segments based distance (.e., detections associated closest segment centroid). appropriate don’t see forwards far, ’s unlikely detections miss assigned. true, instance, aerial survey observers look windows located sides plane might inappropriate shipboard survey observers flying bridge can see way ahead ship. First loading detection data survey (use fit detection function, post-processing): can see columns latitude longitude (Lat Long). can make data.frame sf object telling sf columns contain spatial coordinates (sf lingo, “geometry”): can overlay previous map transects:  Going back segs object , can use st_join function, combined st_nearest_feature function control tables linked. , need project data avoid issues. Now obs acquired additional columns segs, including Sample.Label (want) Effort (don’t). check worked okay, can plot obs segs_lines (line version segments saved earlier) colour coding Sample.Label. randomized colour order easier tell observations misallocated.  segs can remove unnecessary columns geometry get required columns : (Note since binned data, just keep Bin column need process later.) different way approach timestamps waypoints GPS, related segments. One look whether observation made start end times segment.","code":"obs <- read.csv(\"data/loons.csv\") head(obs) ##         Date     Time Bin      Lat      Long Observer object size ## 1 2011-04-19 18991230   B 40.85296 -71.45511       JV     46    1 ## 2 2011-02-17 18991230   A 40.87022 -71.54842       JV    106    1 ## 3 2011-05-02 18991230   A 40.87595 -71.30304       KW    121    1 ## 4 2011-12-04 18991230   C 40.91753 -71.38273       KW    286    1 ## 5 2011-02-23 18991230   A 40.94153 -71.38517       KW    446    1 ## 6 2011-02-07 18991230   A 40.94225 -71.46438       KW    457    1 obs <- st_as_sf(obs, coords=c(\"Long\", \"Lat\")) # set the coordinate system st_crs(obs) <- 4326 obs ## Simple feature collection with 941 features and 6 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size                   geometry ## 1  2011-04-19 18991230   B       JV     46    1 POINT (-71.45511 40.85296) ## 2  2011-02-17 18991230   A       JV    106    1 POINT (-71.54842 40.87022) ## 3  2011-05-02 18991230   A       KW    121    1 POINT (-71.30304 40.87595) ## 4  2011-12-04 18991230   C       KW    286    1 POINT (-71.38273 40.91753) ## 5  2011-02-23 18991230   A       KW    446    1 POINT (-71.38517 40.94153) ## 6  2011-02-07 18991230   A       KW    457    1 POINT (-71.46438 40.94225) ## 7  2011-04-19 18991230   A       JV    500    2 POINT (-71.59495 40.94761) ## 8  2011-05-02 18991230   A       KW    506    1   POINT (-71.552 40.94795) ## 9  2011-02-23 18991230   B       JV    814    1 POINT (-71.51172 40.97912) ## 10 2011-04-19 18991230   A       KW    911    1 POINT (-71.59762 40.98571) p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=obs, size=0.5) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) # project segs segs <- st_transform(segs, 6348) obs <- st_transform(obs, 6348)  # do the join obs <- st_join(obs, segs, join=st_nearest_feature)   # project back to lat/long segs <- st_transform(segs, 4326) obs <- st_transform(obs, 4326)  # how does that look? obs ## Simple feature collection with 941 features and 9 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size Transect       Effort ## 1  2011-04-19 18991230   B       JV     46    1        8 1969.873 [m] ## 2  2011-02-17 18991230   A       JV    106    1       26 1894.100 [m] ## 3  2011-05-02 18991230   A       KW    121    1       12 1988.809 [m] ## 4  2011-12-04 18991230   C       KW    286    1       10 1979.630 [m] ## 5  2011-02-23 18991230   A       KW    446    1       10 1979.624 [m] ## 6  2011-02-07 18991230   A       KW    457    1        8 1969.839 [m] ## 7  2011-04-19 18991230   A       JV    500    2       24 1923.218 [m] ## 8  2011-05-02 18991230   A       KW    506    1       26 1894.075 [m] ## 9  2011-02-23 18991230   B       JV    814    1        7 2000.139 [m] ## 10 2011-04-19 18991230   A       KW    911    1       24 1923.199 [m] ##     Sample.Label                   geometry ## 1   2010-RI-8-29 POINT (-71.45511 40.85296) ## 2  2010-RI-26-16 POINT (-71.54842 40.87022) ## 3  2010-RI-12-31 POINT (-71.30304 40.87595) ## 4  2010-RI-10-27 POINT (-71.38273 40.91753) ## 5  2010-RI-10-26 POINT (-71.38517 40.94153) ## 6   2010-RI-8-24 POINT (-71.46438 40.94225) ## 7  2010-RI-24-12 POINT (-71.59495 40.94761) ## 8  2010-RI-26-12   POINT (-71.552 40.94795) ## 9   2010-RI-7-22 POINT (-71.51172 40.97912) ## 10  2010-RI-24-9 POINT (-71.59762 40.98571) # make a random colour palette to avoid similar colours being near each other pal <- rainbow(nrow(segs), s=.6, v=.9)[sample(1:nrow(segs),nrow(segs))] p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=segs_lines, aes(colour=Sample.Label), pch=21) +   geom_sf(data=obs, size=0.5, aes(colour=Sample.Label)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   scale_colour_manual(values=pal) +   # make the plot look simpler   theme_minimal() +   theme(legend.position = \"none\") print(p) # get rid of \"spatialness\" obs <- st_drop_geometry(obs) # select the columns we need obs <- obs[, c(\"object\", \"Sample.Label\", \"size\", \"Bin\")] head(obs) ##   object  Sample.Label size Bin ## 1     46  2010-RI-8-29    1   B ## 2    106 2010-RI-26-16    1   A ## 3    121 2010-RI-12-31    1   A ## 4    286 2010-RI-10-27    1   C ## 5    446 2010-RI-10-26    1   A ## 6    457  2010-RI-8-24    1   A"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"more-information","dir":"Articles > Data_format","previous_headings":"","what":"More information","title":"`dsm` data formatting example","text":"provided information segmenting wiki page, part US Navy-funded project DenMod. information summarized ?\"dsm-data\" dsm package. may also useful look data example dataset mexdolphins can loaded data(mexdolphins). vignette analysis data available . hard give general information segment lines, extent depends data originally formatted (going way back make model GPS unit used). information dealing spatial data R using sf package available R spatial website (see “Articles” drop header). Another source help distance sampling mailing list. sure search archives prior posting several threads segmenting previously might helpful.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"acknowledgements","dir":"Articles > Data_format","previous_headings":"","what":"Acknowledgements","title":"`dsm` data formatting example","text":"Thanks Phil Bouchet providing helpful comments early version document Iúri Correia finding important bug.","code":""},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"introduction","dir":"Articles > Lines_gomex","previous_headings":"","what":"Introduction","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"analysis based dataset observations pantropical dolphins Gulf Mexico (shipped Distance 6.0 later). convenience data bundled R-friendly format, although code necessary creating data Distance project files available github. OBIS-SEAMAP page data may found SEFSC GoMex Oceanic 1996 survey page. intention highlight features dsm package, rather perform full analysis data. reason, important steps fully explored. familiarity density surface modelling (Miller, Burt, Rexstad, & Thomas, 2013) (Hedley & Buckland, 2004) assumed.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"preamble","dir":"Articles > Lines_gomex","previous_headings":"","what":"Preamble","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"start, load dsm package (dependencies) set options: order run vignette, ’ll need install R packages. can done via following call install.packages:","code":"library(dsm) library(ggplot2)  # plotting options gg.opts <- theme(panel.grid.major=element_blank(),                  panel.grid.minor=element_blank(),                  panel.background=element_blank()) install.packages(c(\"dsm\", \"Distance\", \"knitr\", \"distill\", \"ggplot2\", \"rgdal\",                    \"maptools\", \"plyr\", \"tweedie\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"the-data","dir":"Articles > Lines_gomex","previous_headings":"","what":"The data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data need included dsm package, two additional objects needed plotting required can downloaded put directory file. data can loaded R using following code: add objects survey.area pred.polys environment.","code":"load(\"mexdolphins-extra.rda\")"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"observation-and-segment-data","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Observation and segment data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data analysis nicely pre-formatted shipped dsm. Loading data, can see four data.frames, first lines shown: segdata holds segment data: transects already “chopped” segments. distdata holds distance sampling data used fit detection function. obsdata links distance data segments. preddata holds prediction grid (includes necessary covariates). Typically (.e. datasets) necessary divide transects segments, allocate observations correct segments using GIS similar package1, starting analysis using dsm.","code":"data(mexdolphins) head(segdata) ##   longitude latitude        x        y Effort Transect.Label Sample.Label depth ## 1 -86.92712 29.94378 836105.9 -1011416  13800       19960417   19960417-1 135.0 ## 2 -86.83176 29.84030 846012.9 -1021407  14000       19960417   19960417-2 147.7 ## 3 -86.74445 29.75279 855022.6 -1029785  14000       19960417   19960417-3 152.1 ## 4 -86.65230 29.65522 864610.3 -1039168  13900       19960417   19960417-4 163.8 ## 5 -86.56648 29.56088 873598.1 -1048266  13800       19960417   19960417-5 179.7 ## 6 -86.49290 29.49000 881203.7 -1055004  13800       19960417   19960417-6 188.5 head(distdata) ##     object size  distance Effort detected beaufort latitude longitude ## 45      45   21 3296.6363  36300        1        4 27.72872 -86.00159 ## 61      61  150  929.1937  17800        1        4 25.99896 -87.62712 ## 63      63  125 6051.0009  21000        1        2 26.00693 -87.94881 ## 85      85   75 5499.6971  21800        1        1 27.50344 -90.44891 ## 114    114   50 7258.9837  13400        1        3 27.40568 -94.99483 ## 120    120   45 1454.7962  20900        1        5 26.01765 -95.97449 ##              x        y ## 45  948000.065 -1236192 ## 61  812161.653 -1436899 ## 63  780969.520 -1438985 ## 85  528656.807 -1297833 ## 114  95910.149 -1324562 ## 120   2477.665 -1473909 head(obsdata) ##     object Sample.Label size  distance Effort ## 45      45   19960421-9   21 3296.6363  36300 ## 61      61   19960423-7  150  929.1937  17800 ## 63      63   19960423-9  125 6051.0009  21000 ## 85      85   19960427-1   75 5499.6971  21800 ## 114    114   19960430-8   50 7258.9837  13400 ## 120    120   19960501-5   45 1454.7962  20900 head(preddata) ##   latitude longitude        x          y depth      area ## 1 30.08333 -87.58333 774402.9 -1002759.1    35 271236913 ## 2 30.08333 -87.41667 789688.6 -1001264.5    30 271236913 ## 3 30.08333 -87.25000 804971.3  -999740.6    27 271236913 ## 4 30.08333 -87.08333 820251.1  -998187.5    22 271236913 ## 5 30.08333 -86.91667 835528.0  -996605.2    46 271236913 ## 6 29.91667 -87.75000 760783.1 -1021810.3    14 271236913"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"shapefiles-and-converting-units","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Shapefiles and converting units","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often data spatial analysis comes many different sources. important ensure measurements used analysis compatible units, otherwise resulting estimates incorrect hard interpret. measurements SI units outset removes need conversion later, making life much easier. data already appropriate units (Northings Eastings: kilometres centroid, projected using North American Lambert Conformal Conic projection). extensive literature particular projections latitude longitude appropriate highly recommend reader review particular study area; (Bivand, Pebesma, & Gómez-Rubio, 2008) good starting point. data frames already measurements appropriately converted. convention directions named x y. Using latitude longitude performing spatial smoothing can problematic certain smoother bases used. particular bivariate isotropic bases used non-isotropic nature latitude longitude inconsistent (moving one degree one direction moving one degree ). give example projecting polygon defines survey area (simply read R using readShapeSpatial shapefile produced GIS). Next bit code works study.area outline, massaging use soap film smoother. using soap film smoothers, code extraneous. code generates plot, shows survey area transect lines overlaid (using data segdata).  Also note since projected prediction grid, “squares” look quite like squares. plotting use polygons saved, polygons (stored pred.polys) read shapefile created GIS, object class SpatialPolygons sp package. needs project proper plotting using functions sf package. predsf <- st_as_sf(pred.polys)  Tips plotting polygons available ggplot2 pkg_down.","code":"library(sf) ## Warning: package 'sf' was built under R version 4.4.2 library(plyr)  # tell R that the survey.area object is currently in lat/long sp::proj4string(survey.area) <- sp::CRS(\"+proj=longlat +datum=WGS84\")  predsf <- st_as_sf(pred.polys)  area.sf <- st_as_sf(survey.area) st_crs(area.sf) <- \"WGS84\" area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))  # Convert preddata to a spatial object preddata_sf <- st_as_sf(preddata, coords=c(\"x\", \"y\")) st_crs(preddata_sf) <- st_crs(area.sf.proj) # Perform the intersection preddata_sf <- st_intersection(preddata_sf, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries coords_preddata <- data.frame(st_coordinates(preddata_sf))  preddata_sf$x <- coords_preddata$X preddata_sf$y <- coords_preddata$Y # proj 4 string # using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/ lcc_proj4 <- sp::CRS(\"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs \")  # project using LCC survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)  # simplify the object survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords) names(survey.area) <- c(\"x\", \"y\") segdata_sf <- st_as_sf(segdata, coords = c(\"x\",\"y\")) st_crs(segdata_sf) <- st_crs(area.sf.proj) # study area outline and segment centres plot_segments <- ggplot() +   geom_sf(data = area.sf.proj, fill=\"lightblue\", color = \"blue\", linewidth=.1) +   geom_sf(data=segdata_sf, fill=NA, color=\"black\", linewidth=.3) +   labs(title=\"1996 SE Fisheries Science Center Gulf of Mexico cruise\",        subtitle = \"Points are segment centres\") +   scale_fill_viridis_c(option = \"magma\", guide = \"none\") plot_segments predsf <- st_as_sf(pred.polys) # plot as projected plot(st_geometry(predsf), axes=TRUE)"},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"distance-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Distance data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top panels EDA plots show histograms observed distances cluster size, bottom panels show relationship observed distance observed cluster size, relationship observed distance Beaufort sea state. plots show relationship cluster size observed distance (fewer smaller clusters seem seen larger distances). following code generates EDA plots:  Top row, left right: histograms distance cluster size; bottom row: plot distance cluster size plot distances Beaufort sea state.","code":"par(mfrow=c(2,2))  # histograms hist(distdata$distance,main=\"\",xlab=\"Distance (m)\") hist(distdata$size,main=\"\",xlab=\"Cluster size\")  # plots of distance vs. cluster size plot(distdata$distance, distdata$size, main=\"\", xlab=\"Distance (m)\",      ylab=\"Group size\", pch=19, cex=0.5, col=gray(0.7))  # lm fit l.dat <- data.frame(distance=seq(0,8000,len=1000)) lo <- lm(size~distance, data=distdata) lines(l.dat$distance, as.vector(predict(lo,l.dat)))  plot(distdata$distance,distdata$beaufort, main=\"\", xlab=\"Distance (m)\",      ylab=\"Beaufort sea state\", pch=19, cex=0.5, col=gray(0.7))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Spatial data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Looking separately spatial data without thinking distances, can plot observed group sizes space (point size proportional group size observation). Circle size indicates size group observation. rather large areas observations, might cause variance estimates abundance rather large. also see depth data use depth later explanatory covariate spatial model.","code":"prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid) cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries depth <- ggplot() +          geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +          labs(title = \"Spotted dolphins, Gulf of Mexico\",               subtitle = \"Depth in meters, size of detected dolphin groups\") +          xlab(\"Longitude\") + ylab(\"Latitude\") +          geom_point(aes(x, y, size=size), data=distdata, colour=\"red\",alpha=I(0.5)) +          scale_fill_viridis_c(option = \"viridis\", direction = 1) depth"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"estimating-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"","what":"Estimating the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"use ds function package Distance fit detection function. (Distance package intended make standard distance sampling R relatively straightforward. flexible complex alternative, see function ddf mrds library.) First, loading Distance library: can fit detection function hazard-rate key adjustment terms: Calling summary gives us information parameter estimates, probability detection, AIC, etc: following code generates plot fitted detection function (left) quantile-quantile plot (right):  quantile-quantile plot show relatively good goodness fit hazard-rate detection function.","code":"library(Distance) detfc.hr.null <- ds(distdata, max(distdata$distance), key=\"hr\", adjustment=NULL) ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). ## Warning in ds(distdata, max(distdata$distance), key = \"hr\", adjustment = NULL): ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). summary(detfc.hr.null) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  841.2528  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##             estimate        se ## (Intercept)  7.98244 0.9533779 ##  ## Shape coefficient(s):   ##             estimate        se ## (Intercept)        0 0.7833406 ##  ##                       Estimate         SE        CV ## Average p            0.5912252  0.2224714 0.3762887 ## N in covered region 79.4959326 30.8184416 0.3876732 plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2) gof_ds(detfc.hr.null) ##  ## Goodness of fit results for ddf object ##  ## Distance sampling Cramer-von Mises test (unweighted) ## Test statistic = 0.0972004 p-value = 0.598774"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-covariates-to-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Estimating the detection function","what":"Adding covariates to the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"common include covariates detection function (-called Multiple Covariate Distance Sampling MCDS). dataset two covariates collected individual: Beaufort sea state size. brevity fit hazard-rate detection functions sea state included factor covariate follows: looking summary, detection function covariates give lower AIC model without covariates (843.71 vs. 841.25 hazard-rate model without covariates). Looking back bottom-right panel EDA plots, can see discernible pattern plot Beaufort vs distance. brevity, detection function model selection omitted . practise fit many different forms detection function (select model based goodness fit testing AIC).","code":"detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),                   key=\"hr\", adjustment=NULL) summary(detfc.hr.beau) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  843.7115  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##                         estimate        se ## (Intercept)           7.66291936  1.078972 ## as.factor(beaufort)2  2.24809809 14.754677 ## as.factor(beaufort)3  0.28581820  1.020809 ## as.factor(beaufort)4  0.07133776  1.225360 ## as.factor(beaufort)5 -0.36421115  1.540324 ##  ## Shape coefficient(s):   ##              estimate        se ## (Intercept) 0.2994644 0.5184204 ##  ##                       Estimate         SE        CV ## Average p            0.5420677  0.1753555 0.3234937 ## N in covered region 86.7050383 29.4646397 0.3398262"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"fitting-a-dsm","dir":"Articles > Lines_gomex","previous_headings":"","what":"Fitting a DSM","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"fitting dsm model, data must segmented; consists chopping transects attributing counts segments. mentioned , data already segmented.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"a-simple-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"A simple model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"begin simple model. assume number individuals segment quasi-Poisson distributed smooth function spatial coordinates (note formula exactly one specify gam mgcv). setting group=TRUE, abundance clusters/groups rather individuals can estimated (though ignore ). Note set method=\"REML\" ensure smooth terms estimated reliably. Running model: can obtain summary fitted model: exact interpretation model summary results can found (S. N. Wood, 2017); can see various information smooth components fitted general model statistics. can use deviance explained compare models2. can also get rough idea smooth space looks like using vis.gam (white/yellow indicates high values, red low indicates low values):  type=\"response\" argument ensures plot scale abundance values relative (offsets set median values). means plot useful get idea general shape smooth interpreted directly.","code":"dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)   -18.20       0.53  -34.34   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##         edf Ref.df     F  p-value     ## s(x,y) 24.8  27.49 2.354 0.000733 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.121   Deviance explained = 43.4% ## -REML = 936.04  Scale est. = 94.367    n = 387 vis.gam(dsm.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-another-environmental-covariate-to-the-spatial-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Adding another environmental covariate to the spatial model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data set also contains depth covariate (plotted ). can include model simply: see drop deviance explained, perhaps model useful first. discuss setting k parameter Model checking, . Setting select=TRUE (argument gam) impose extra shrinkage terms smooth model (allowing smooth terms removed model fitting; see ?gam information). particularly useful , include . However many environmental predictors model can good way (along looking \\(p\\)-values) perform term selection. Simply calling plot model object allows us look relationship depth linear predictor:  Omitting argument select call plot plot smooth terms, one time.","code":"dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy.depth) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, k = 10) + s(depth, k = 20) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.740      1.236  -15.16   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F p-value   ## s(x,y)   6.062  7.371 0.923  0.5038   ## s(depth) 9.443 11.466 1.585  0.0824 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0909   Deviance explained = 34.3% ## -REML = 939.52  Scale est. = 124.9     n = 387 plot(dsm.xy.depth, select=2)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-models-when-there-are-covariates-in-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Spatial models when there are covariates in the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"code fit DSM covariates detection function similar models, . However since detection function observation-level covariates, must estimate abundance per segment using Horvitz-Thompson-like estimator modelling, change response abundance.est: can see, summary results rather similar: resulting spatial smooth (though resulting surface somewhat “amplified”):","code":"dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method=\"REML\") summary(dsm.est.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## abundance.est ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -18.0233     0.4739  -38.03   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value     ## s(x,y) 24.64  27.44 2.349 0.00029 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.129   Deviance explained = 42.2% ## -REML = 1043.6  Scale est. = 174.1     n = 387 vis.gam(dsm.est.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", zlim=c(0, 300), contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-response-distributions","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other response distributions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often quasi-Poisson distribution doesn’t give adequate flexibility capture overdispersion response data (see Model checking Model selection ), illustrate two additional distributions can used count data. models section, ’ll move back count response, though estimated abundance also work.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"tweedie","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Tweedie","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Response distributions quasi-Poisson can used, example Tweedie distribution. Tweedie distribution available dsm setting family=tw().","code":"dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method=\"REML\") summary(dsm.xy.tweedie) ##  ## Family: Tweedie(p=1.347)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -17.2640     0.2363  -73.07   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value   ## s(x,y) 12.82  17.13 1.628  0.0562 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0684   Deviance explained = 26.8% ## -REML = 351.49  Scale est. = 60.26     n = 387"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"negative-binomial","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Negative binomial","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Though used , similarly, two options negative binomial distribution: negbin nb. former requires user specification single parameter theta range values parameter (specified vector), latter estimates value theta model fitting process (generally faster). latter recommended users.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-spatial-modelling-options","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other spatial modelling options","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"large literature spatial modelling using GAMs, much can harnessed DSM context. highlights.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"soap-film-smoothing","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other spatial modelling options","what":"Soap film smoothing","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"account complex region (e.g., region includes peninsulae) can use soap film smoother (Simon N. Wood, Bravington, & Hedley, 2008). use soap film smoother spatial part model must create set knots smoother use. easily done using make.soapgrid() function dsm: second argument specifies number points (direction) grid used create knots (knots grid outside survey.area removed). saw exploratory analysis, transect lines outside survey area. cause soap film smoother fail, remove : Note soap_checker script available can useful ensuring boundary, data knots correct format use soap film smoother. can run model depth covariate along spatial (soap film) smooth. Note k argument now refers complexity boundary smooth soap film, complexity film controlled knots given xt argument.","code":"soap.knots <- make.soapgrid(survey.area,c(15,10)) x <- segdata$x; y<-segdata$y onoff <- inSide(x=x,y=y, bnd=as.list(survey.area)) rm(x,y) segdata.soap <- segdata[onoff,] dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs=\"so\", k=15, xt=list(bnd=list(survey.area))) +             s(depth),           family=tw(), method=\"REML\",           detfc.hr.null, segdata.soap, obsdata, knots=soap.knots) summary(dsm.xy.tweedie.soap) ##  ## Family: Tweedie(p=1.356)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, bs = \"so\", k = 15, xt = list(bnd = list(survey.area))) +  ##     s(depth) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.018      0.405  -44.49   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F  p-value     ## s(x,y)   3.050 72.000 0.064 0.114944     ## s(depth) 5.135  6.196 3.895 0.000793 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0653   Deviance explained = 33.1% ## -REML = 339.26  Scale est. = 54.022    n = 365"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-checking","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model checking","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Fitting models well good, ’d like confirm models reasonable; dsm provides functions model checking. can use gam.check generate diagnostic plots:  show deviation Q-Q plot. “line” points plot residuals vs. linear predictor plot corresponds zeros data. Note well plots, gam.check also produces information model fitting. particular interest us last lines tell us basis size. k parameter provided s (te) terms dsm controls complexity smooths model. setting k parameter specify largest complexity smooth term model; long high enough, can sure enough flexibility. output gam.check , can see “p-value” calculated size basis, can good guide whether basis size needs increased. ?choose.k manual page mgcv gives guidance technical details matter. can look model form Tweedie distribution specified response:  Q-Q plot now seems much better (closer \\(y=x\\) line). plots histogram residuals rather hard interpret due large number zeros data. guidance interpreting gam.check output can found (S. N. Wood, 2017).","code":"gam.check(dsm.xy) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 6 iterations. ## Gradient range [-7.105956e-08,-9.826305e-09] ## (score 936.0363 & scale 94.3674). ## Hessian positive definite, eigenvalue range [5.800328,192.5304]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 24.8    1.01    0.99 gam.check(dsm.xy.tweedie) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 8 iterations. ## Gradient range [-1.513175e-06,3.981813e-06] ## (score 351.486 & scale 60.26034). ## Hessian positive definite, eigenvalue range [0.6230615,103.5094]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 12.8    0.73    0.34"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"randomised-quantile-residuals","dir":"Articles > Lines_gomex","previous_headings":"Model checking","what":"Randomised quantile residuals","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top right panel gam.check plots residuals vs. linear predictor plot includes odd line predictions. artifact link function, showing exact zeros data. can misleading distracting, making difficult see whether residuals show heteroskedasticity. Randomised quantile residuals (Dunn & Smyth, 1996) avoid issue transforming residuals exactly normally distributed. makes residuals vs. linear predictor plot much easier interpret therefore doesn’t include artifacts generated link function. plots can produced using rqgam.check dsm:  can see issue heteroskedasticity (increase spread residuals vs. linear predictor plot increasing values linear predictor). One can also plot residuals covariate values check pattern residuals. Note general, plots “Resids vs. linear pred.” interpreted caution output rqgam.check residuals generated normal construction (example Q-Q plot histogram residuals always look fine).","code":"rqgam_check(dsm.xy.tweedie)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-selection","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model selection","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Assuming models “passed” checks gam.check, rqgam.check sufficiently flexible, may left choice model “best”. several methods choosing best model – AIC, REML/GCV scores, deviance explained, full cross-validation test data . Though document intend full analysis pantropical dolphin data, can create results table compare various models fitted far terms abundance estimates associated uncertainties. can use resulting data.frame build table results using kable function:","code":"# make a data.frame to print out mod_results <- data.frame(\"Model name\" = c(\"`dsm.xy`\", \"`dsm.xy.depth`\", \"`dsm.xy.tweedie`\", \"`dsm.xy.tweedie.soap`\",                                            \"`dsm.est.xy`\"),                           \"Description\" = c(\"Bivariate smooth of location, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie\",                                             \"Soap film smooth of location, smooth of depth, Tweedie\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function\"),                           \"Deviance explained\" = c(unlist(lapply(list(dsm.xy,                                                                       dsm.xy.depth,                                                                       dsm.xy.tweedie,                                                                       dsm.xy.tweedie.soap),                  function(x){paste0(round(summary(x)$dev.expl*100,2),\"%\")})),NA)) kable(mod_results, col.names=c(\"Model name\", \"Description\", \"Deviance explained\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"abundance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Abundance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"model checked selected, can make predictions grid calculate abundance. offset stored area column3. Given predicted abundance estimates cell prediction grid, connected prediction grid object mapping:  can calculate abundance survey area simply summing predictions: can compare plot predictions dsm.xy.depth:  can see inclusion depth model noticeable effect distribution (note difference legend scale two plots). can also look total abundance: see much change abundance, terms abundance alone isn’t much two models. Next ’ll go look variance next can see bigger differences models.","code":"dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area) prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid)  preddata_sf$Prediction_xy <- dsm.xy.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.pred) ## [1] 28462.22 dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)  preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, smooth of depth, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.depth.pred) ## [1] 27084.54"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"variance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Variance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Obviously point estimates abundance important, also calculate uncertainty around abundance estimates. Fortunately dsm provides functions perform calculations display resulting uncertainty estimates. two approaches estimating uncertainty abundance estimate dsm. dsm_var_gam assumes spatial model detection function parts model independent . case squared coefficients variation model component added. dsm_var_prop takes account fact detection probability may correlated spatial part model. uses methods described (Bravington, Miller, & Hedley, 2021). dsm_var_prop can applied covariate detection function varies level segments, recorded segment (example Beaufort). don’t situation , opt dsm_var_gam. methods estimate variance abundance element list provided pred.data. case wish obtain abundance prediction cells, use split chop data set list elements give dsm_var_gam (dsm_var_prop). Calling summary give information uncertainty estimation: can also make plot CVs (transect lines observations overlaid) using following code.  Note increase CV away transect lines. can revisit model included depth location smooths observe coefficient variation model larger model location smooth.","code":"preddata.var <- split(preddata, 1:nrow(preddata)) dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,                           off.set=preddata$area) summary(dsm.xy.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 16091.72 28430.76 50231.32  ## (Using log-Normal approximation) ##  ## Point estimate                 : 28430.76  ## CV of detection function       : 0.2471842  ## CV from GAM                    : 0.164  ## Total standard error           : 8433.356  ## Total coefficient of variation : 0.2966 preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries CV <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, uncertainty (CV)\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) CV dsm.xy.depth.var <- dsm_var_gam(dsm.xy.depth, pred.data=preddata.var,                                 off.set=preddata$area) summary(dsm.xy.depth.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 14377.57 27054.60 50909.23  ## (Using log-Normal approximation) ##  ## Point estimate                 : 27054.6  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.1741  ## Total standard error           : 8958.444  ## Total coefficient of variation : 0.3311"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"conclusions","dir":"Articles > Lines_gomex","previous_headings":"","what":"Conclusions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"document outlined analysis spatially-explicit distance sampling data using dsm package. Note many possible models can fitted using dsm aim show just options. Results models can rather different, care must taken performing model selection, discrimination criticism.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"software","dir":"Articles > Lines_gomex","previous_headings":"","what":"Software","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Distance available http://github.com/DistanceDevelopment/Distance well CRAN. dsm available http://github.com/DistanceDevelopment/dsm, well CRAN.","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"David L. Miller. Author. Eric Rexstad. Contributor. Louise Burt. Contributor. Mark V. Bravington. Contributor. Sharon Hedley. Contributor. Megan Ferguson. Contributor. Natalie Kelly. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller DL (2024). dsm: Density Surface Modelling Distance Sampling Data. R package version 2.3.3.9001, https://github.com/DistanceDevelopment/dsm.","code":"@Manual{,   title = {dsm: Density Surface Modelling of Distance Sampling Data},   author = {David L. Miller},   year = {2024},   note = {R package version 2.3.3.9001},   url = {https://github.com/DistanceDevelopment/dsm}, }"},{"path":[]},{"path":"/index.html","id":"what-is-dsm","dir":"","previous_headings":"","what":"Density Surface Modelling of Distance Sampling Data","title":"Density Surface Modelling of Distance Sampling Data","text":"dsm provides Generalized Additive Model (GAM)-based approach calculate spatially-explicit estimates animal abundance distance sampling (also presence/absence strip transect) data. Several utility functions provided model checking, plotting variance estimation. Open Access paper describing methods: Miller, DL, ML Burt, EA Rexstad L Thomas (2013). Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution.","code":""},{"path":"/index.html","id":"using-dsm","dir":"","previous_headings":"","what":"Using dsm","title":"Density Surface Modelling of Distance Sampling Data","text":"Examples use dsm distance sampling R packages available examples.distancesampling.org.","code":""},{"path":"/reference/block.info.per.su.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the block information — block.info.per.su","title":"Find the block information — block.info.per.su","text":"Takes transect data works many blocks given size (segment terms) fit .","code":""},{"path":"/reference/block.info.per.su.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the block information — block.info.per.su","text":"","code":"block.info.per.su(block.size, data, name.su)"},{"path":"/reference/block.info.per.su.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the block information — block.info.per.su","text":"block.size number segments per block data data used build model name.su names sampling units (.e., transects)","code":""},{"path":"/reference/block.info.per.su.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the block information — block.info.per.su","text":"data.frame following columns name        sample unit name (e.g. transect label) num.seg     number segments transect num.block   number blocks available start.block block number first block end.block   block number last block num.req     number blocks needed unit","code":""},{"path":"/reference/check.cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names exist — check.cols","title":"Check column names exist — check.cols","text":"Internal function check supplied data.frames correct columns checks sample labels unique.","code":""},{"path":"/reference/check.cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names exist — check.cols","text":"","code":"check.cols(ddf.obj, segment.data, observation.data, segment.area)"},{"path":"/reference/check.cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names exist — check.cols","text":"ddf.obj ddf object mrds segment.data segment data defined dsm observation.data observation data defined dsm segment.area area segments","code":""},{"path":"/reference/check.cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names exist — check.cols","text":"nothing, throws error something went wrong","code":""},{"path":"/reference/check.cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check column names exist — check.cols","text":"David Lawrence Miller","code":""},{"path":"/reference/dsm-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Data format for DSM — dsm-data","title":"Data format for DSM — dsm-data","text":"Two data.frames must provided dsm. referred observation.data segment.data.","code":""},{"path":"/reference/dsm-data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data format for DSM — dsm-data","text":"segment.data table sample identifiers define segments, corresponding effort (line length) expended environmental covariates used model abundance/density. observation.data provides link table observations used detection function samples (segments), can aggregate observations segments (.e., observation.data \"look-table\" observations segments). observation.data - observation data.frame must (least) following columns: object unique object identifier Sample.Label identifier segment observation occurred size size observed group (e.g., 1 animals occurred individually) distance distance observation One can often also use observation.data fit detection function (additional columns detection function covariates allowed table). segment.data: segment data.frame must (least) following columns: Effort effort (terms length segment) Sample.Label identifier segment (unique!) ??? environmental covariates, example location (projected latitude longitude), relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).","code":""},{"path":"/reference/dsm-data.html","id":"multiple-detection-functions","dir":"Reference","previous_headings":"","what":"Multiple detection functions","title":"Data format for DSM — dsm-data","text":"multiple detection functions used, column named ddfobj must included observation.data segment.data. lets model know detection function observation . numeric ordered ddf.obj argument dsm, e.g., ddf.obj=list(ship_ddf, aerial_ddf) means ship detections ddfobj=1 aerial detections ddfobj=2 observation data.","code":""},{"path":"/reference/dsm-data.html","id":"mark-recapture-distance-sampling-models","dir":"Reference","previous_headings":"","what":"Mark-recapture distance sampling models","title":"Data format for DSM — dsm-data","text":"using mrds models include mark-recapture components (currently independent observer trial modes supported) format observation data needs checked ensure observations duplicated. observer column also required observation.data. Independent observer mode unique observations (unique object IDs) required. Trial mode observations made observer 1 required.","code":""},{"path":"/reference/dsm-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Density surface modelling — dsm-package","title":"Density surface modelling — dsm-package","text":"dsm implements spatial models distance sampling data. Models detectability can fitted using packages mrds Distance. dsm fits generalized additive models spatially-referenced data. See Miller et al (2013) introduction.","code":""},{"path":"/reference/dsm-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density surface modelling — dsm-package","text":"information distance sampling methods example code available http://distancesampling.org/R/. help distance sampling package, Google Group https://groups.google.com/forum/#!forum/distance-sampling. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density surface modelling — dsm-package","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm.cor","title":"Check for autocorrelation in residuals — dsm.cor","text":"function deprecated, use dsm_cor.","code":""},{"path":"/reference/dsm.cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm.cor","text":"","code":"dsm.cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm.cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm.cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Fits density surface model (DSM) detection adjusted counts spatially-referenced distance sampling analysis. dsm takes observations animals, allocates segments line (strip transects) optionally adjusts counts based detectability using supplied detection function model. generalized additive model, generalized mixed model generalized linear model used model adjusted counts based formula involving environmental covariates.","code":""},{"path":"/reference/dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"dsm(   formula,   ddf.obj,   segment.data,   observation.data,   engine = \"gam\",   convert.units = 1,   family = quasipoisson(link = \"log\"),   group = FALSE,   control = list(keepData = TRUE),   availability = 1,   segment.area = NULL,   weights = NULL,   method = \"REML\",   ... )"},{"path":"/reference/dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"formula formula surface. valid formula. See \"Details\", , define response. ddf.obj result call ddf ds. multiple detection functions required list can provided. strip/circle transects assumed objects observed, see dummy_ddf. Mark-recapture distance sampling (mrds) models type io (independent observers) trial allowed. segment.data segment data, see dsm-data. observation.data observation data, see dsm-data. engine fitting engine used DSM (\"glm\"/\"gam\"/\"gamm\"/\"bam\"). convert.units conversion factor multiply area segments . See 'Units' . family response distribution (popular choices include quasipoisson, Tweedie/tw negbin/nb). Defaults quasipoisson. group TRUE abundance groups calculated rather abundance individuals. Setting option TRUE equivalent setting size group 1. control usual control argument gam; keepData must TRUE variance estimation work (though option set GLMs GAMMs). availability estimate availability bias. count models used multiply effective strip width (must vector length 1 length number rows segment.data); estimated abundance/estimated density models used scale response (must vector length 1 length number rows observation.data). Uncertainty availability handled present. segment.area NULL (default) segment areas calculated multiplying Effort column segment.data (right minus left) truncation distance ddf.obj strip.width. Alternatively vector segment areas can provided (must length number rows segment.data) character string giving name column segment.data contains areas. segment.area specified takes precedent. weights weights observation used model fitting. default, weights=NULL, weights observation area (see Details). Setting scalar value (e.g., weights=1) observations equally weighted. method smoothing parameter estimation method. Default \"REML\", using Restricted Maximum Likelihood. See gam options. Ignored engine=\"glm\". ... anything else passed straight glm, gam, gamm bam.","code":""},{"path":"/reference/dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"glm, gam, gamm bam object, additional element, $ddf holds detection function object.","code":""},{"path":"/reference/dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"response (LHS formula) can one following (restrictions outlined ): count count segment abundance.est estimated abundance per segment, estimation via Horvitz-Thompson estimator density.est density per segment offset used model dependent response: count area segment multiplied average probability detection segment abundance.est area segment density zero count response can used detection function covariates vary segments/points (within). example, weather conditions (like visibility sea state) foliage cover usually acceptable change within segment, animal sex behaviour work. abundance.est response can used covariates detection function. density case, observations can weighted segment areas via weights= argument. default (weights=NULL), density estimated weights set segment areas (using segment.area calculated detection function object metadata Effort data). Alternatively weights=1 set weights equal. third alternative pass vector length equal number segments, containing appropriate weights. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"often case distances collected metres segment lengths recorded kilometres. dsm allows provide conversion factor (convert.units) multiply areas . example: distances metres segment lengths kilometres setting convert.units=1000 lead analysis metres. Setting convert.units=1/1000 lead analysis kilometres. conversion factor applied segment.area specified.","code":""},{"path":"/reference/dsm.html","id":"large-models","dir":"Reference","previous_headings":"","what":"Large models","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"large models, engine=\"bam\" method=\"fREML\" may useful. Models specified bam gam. Read bam using option; option considered EXPERIMENTAL moment. particular note default basis choice (thin plate regression splines) slow general fitting less stable using gam. negative binomial response, theta must specified using bam.","code":""},{"path":"/reference/dsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"David L. Miller","code":""},{"path":"/reference/dsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) summary(mod1)  # predict over a grid mod1.pred <- predict(mod1, preddata, preddata$area)  # calculate the predicted abundance over the grid sum(mod1.pred)  # plot the smooth plot(mod1) } # }"},{"path":"/reference/dsm.var.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm.var.gam","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"function deprecated, use dsm_var_gam.","code":""},{"path":"/reference/dsm.var.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"","code":"dsm.var.gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm.var.movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"function deprecated, use dsm_var_movblk.","code":""},{"path":"/reference/dsm.var.movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"","code":"dsm.var.movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm.var.movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL – file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm.var.prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm.var.prop","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"function deprecated, use dsm_var_prop.","code":""},{"path":"/reference/dsm.var.prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"","code":"dsm.var.prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm_cor","title":"Check for autocorrelation in residuals — dsm_cor","text":"DSM fitted data, function can used check autocorrelation residuals.","code":""},{"path":"/reference/dsm_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"dsm_cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm_cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for autocorrelation in residuals — dsm_cor","text":"plot vector fun applied lags.","code":""},{"path":"/reference/dsm_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for autocorrelation in residuals — dsm_cor","text":"Within Transect.Label, segments sorted according Segment.Labels. may require time get right particular data. one multiple surveys transects revisited, example, one may want make Transect.Label unique transect-survey identifier. Neither label need included model, must just present $data field model. usually means segment data passed dsm. current iteration function plot correlations nicely, things can get function return data (assigning result object). NA values residuals correlogram calculated. usually occurs due NA values covariates (smoother fitted values ). Code like (.na(dsm.obj$data)) might helpful.","code":""},{"path":"/reference/dsm_cor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for autocorrelation in residuals — dsm_cor","text":"David L. Miller","code":""},{"path":"/reference/dsm_cor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"# \\donttest{  library(Distance) #>  #> Attaching package: 'Distance' #> The following object is masked from 'package:dsm': #>  #>     dummy_ddf #> The following object is masked from 'package:mrds': #>  #>     create.bins  library(dsm)   # load the data, see ?mexdolphins  data(mexdolphins)   # fit a model  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.433 #> No survey area information supplied, only estimating detection function.  mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) #> Warning: Some observations are outside of detection function 1 truncation!   # look at lag 1 differences up to a maximum of lag 9, using deviance  # residuals  dsm_cor(mod1, resid.type=\"deviance\", max.lag=9,          Segment.Label=\"Sample.Label\")  # }"},{"path":"/reference/dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance propagation for density surface models — dsm_varprop","title":"Variance propagation for density surface models — dsm_varprop","text":"Calculate uncertainty predictions fitted DSM, including uncertainty detection function.","code":""},{"path":"/reference/dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance propagation for density surface models — dsm_varprop","text":"","code":"dsm_varprop(   model,   newdata = NULL,   trace = FALSE,   var.type = \"Vp\",   var_type = NULL )"},{"path":"/reference/dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance propagation for density surface models — dsm_varprop","text":"model fitted dsm. newdata prediction grid. Set NULL avoid making predictions just return model objects. trace debugging, see scale parameter estimation going. var.type variance-covariance matrix used (\"Vp\" variance-covariance conditional smoothing parameter(s), \"Vc\" unconditional). See gamObject details/explanation. doubt, stick default, \"Vp\". var_type deprecated, use var.type instead.","code":""},{"path":"/reference/dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance propagation for density surface models — dsm_varprop","text":"list elements: old_model fitted model supplied function model refit refitted model object, extra term pred point estimates predictions newdata var total variance calculated newdata ses standard error prediction cell newdata newdata=NULL last three entries NA.","code":""},{"path":"/reference/dsm_varprop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance propagation for density surface models — dsm_varprop","text":"make predictions spatial model, also want know uncertainty abundance estimate. Since density surface models 2 () stage models, need incorporate uncertainty earlier stages (.e. detection function) \"final\" uncertainty estimate. function refit spatial model include Hessian offset extra term. Variance estimates using new model can used calculate variance predicted abundance estimates incorporate detection function uncertainty. Importantly requires detection function covariates, vary within segment (, example covariates like sex used). information construct prediction grid data.frame, newdata, see predict.dsm. routine useful detection function covariates used DSM. Note can use var.type=\"Vc\" (see gamObject), variance-covariance matrix spatial model, corrected smoothing parameter uncertainty. See Wood, Pya & S\\\"afken (2016) information. Models fixed scale parameters (e.g., negative binomial) require extra round optimisation.","code":""},{"path":"/reference/dsm_varprop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Variance propagation for density surface models — dsm_varprop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_varprop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance propagation for density surface models — dsm_varprop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535. Wood, S.N., Pya, N. S\\\"afken, B. (2016) Smoothing parameter model selection general smooth models. Journal American Statistical Association, 1-45.","code":""},{"path":"/reference/dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance propagation for density surface models — dsm_varprop","text":"David L. Miller, based code Mark V. Bravington Sharon L. Hedley.","code":""},{"path":"/reference/dsm_var_gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm_var_gam","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"one willing assume detection function spatial model independent, function produce estimates variance predictions abundance, using result squared coefficients variation add.","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"dsm_var_gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"list elements model fitted model object pred.var variance regions given pred.data. bootstrap logical, always FALSE model fitted model extra term dsm.object original model (dsm.obj )","code":""},{"path":"/reference/dsm_var_gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"David L. Miller","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"if (FALSE) { # \\dontrun{  library(Distance)  library(dsm)   # load the Gulf of Mexico dolphin data (see ?mexdolphins)  data(mexdolphins)   # fit a detection function and look at the summary  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL)  summary(hr.model)   # fit a simple smooth of x and y  mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)   # Calculate the variance  # this will give a summary over the whole area in mexdolphins$preddata  mod1.var <- dsm_var_gam(mod1, preddata, off.set=preddata$area) } # }"},{"path":"/reference/dsm_var_movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Estimate variance abundance area using moving block bootstrap. Two procedures implemented, one incorporating detection function uncertainty, one .","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"dsm_var_movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm_var_movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL – file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm_var_movblk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Setting ds.uncertainty=TRUE incorporate detection function uncertainty directly bootstrap. done generating observations fitted detection function re-fitting new detection function (form), calculating new effective strip width. Rejection sampling used generate observations (except half-normal case) procedure can rather slow. Note currently supported covariates detection function. Setting ds.uncertainty=FALSE incorporate detection function uncertainty using delta method. assumes detection function spatial model INDEPENDENT. probably reasonable.","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata) summary(mod1)  # calculate the variance by 500 moving block bootstraps mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,    block.size = 3, samp.unit.name = \"Transect.Label\",    off.set = preddata$area,    bar = TRUE, bs.file = \"mexico-bs.csv\", ds.uncertainty = TRUE) } # }"},{"path":"/reference/dsm_var_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm_var_prop","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"ensure uncertainty detection function correctly propagated final variance estimate abundance, function uses method first detailed Williams et al (2011), explanation given Bravington et al. (2021).","code":""},{"path":"/reference/dsm_var_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"","code":"dsm_var_prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"list elements model fitted model object pred.var variance region given pred.data bootstrap logical, always FALSE pred.data .set model fitted model extra term dsm.object original model, model.check simple check subtracting coefficients two models see large difference deriv numerically calculated Hessian offset","code":""},{"path":"/reference/dsm_var_prop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"idea refit spatial model including extra random effect. random effect zero mean hence effect point estimates. variance Hessian detection function. Variance estimates incorporate detection function uncertainty. mathematical details given paper references . Many prediction grids can supplied supplying list data.frames function. Note routine simply calls dsm_varprop. require multiple prediction grids, routine probably faster. routine useful detection function covariates used DSM.","code":""},{"path":"/reference/dsm_var_prop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_var_prop.html","id":"limitations","dir":"Reference","previous_headings":"","what":"Limitations","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Note routine useful detection function used DSM. used abundance.est density.est responses used. Importantly requires detection function covariates, vary within segment (, example covariates like sex used).","code":""},{"path":"/reference/dsm_var_prop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535.","code":""},{"path":"/reference/dsm_var_prop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Mark V. Bravington, Sharon L. Hedley. Bugs added David L. Miller.","code":""},{"path":"/reference/dummy_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection function objects when detection is certain — dummy_ddf","title":"Detection function objects when detection is certain — dummy_ddf","text":"Create detection function object strip/plot surveys use density surface models.","code":""},{"path":"/reference/dummy_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection function objects when detection is certain — dummy_ddf","text":"","code":"dummy_ddf(object, size = 1, width, left = 0, transect = \"line\")"},{"path":"/reference/dummy_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection function objects when detection is certain — dummy_ddf","text":"object numeric vector object identifiers, relating object field observation data DSM. size group size observation (default groups size 1) width right truncation left left truncation (default 0, left truncation) transect \"line\" \"point\" transect","code":""},{"path":"/reference/dummy_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection function objects when detection is certain — dummy_ddf","text":"David L Miller","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data from a fitted detection function — generate.ds.uncertainty","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"using dsm.var.movblk ds.uncertainty=TRUE, procedure generates data fitted detection function (assuming correct).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"","code":"generate.ds.uncertainty(ds.object)"},{"path":"/reference/generate.ds.uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"ds.object fitted detection function object (returned call ddf.ds).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"function changes random number generator seed. avoid potential side-effects, use something like: seed <- get(\".Random.seed\",envir=.GlobalEnv) running code assign(\".Random.seed\",seed,envir=.GlobalEnv) .","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"David L. Miller","code":""},{"path":"/reference/generate.mb.sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Moving block bootstrap sampler — generate.mb.sample","title":"Moving block bootstrap sampler — generate.mb.sample","text":"usually used , called within dsm.var.movblk.","code":""},{"path":"/reference/generate.mb.sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Moving block bootstrap sampler — generate.mb.sample","text":"","code":"generate.mb.sample(   num.blocks.required,   block.size,   which.blocks,   dsm.data,   unit.info,   n.units )"},{"path":"/reference/generate.mb.sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Moving block bootstrap sampler — generate.mb.sample","text":"num.blocks.required number blocks need. block.size number segments per block. .blocks blocks sampled. dsm.data $data element result call dsm. unit.info result calling block.info.per.su. n.units number sampling units.","code":""},{"path":"/reference/generate.mb.sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Moving block bootstrap sampler — generate.mb.sample","text":"vector log-residuals","code":""},{"path":"/reference/latlong2km.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert latitude and longitude to Northings and Eastings — latlong2km","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Convert longitude latitude co-ordinates kilometres west-east south-north axes (lon0,lat0) using \"spherical law cosines\".","code":""},{"path":"/reference/latlong2km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"","code":"latlong2km(lon, lat, lon0 = sum(range(lon))/2, lat0 = sum(range(lat))/2)"},{"path":"/reference/latlong2km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"lon longitude lat latitude lon0 longitude reference point (defaults mean longitude) lat0 latitude reference point (defaults mean latitude)","code":""},{"path":"/reference/latlong2km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"list elements km.e km.n.","code":""},{"path":"/reference/latlong2km.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"WARNING: approximate procedure converting latitude/ longitude Northing/Easting. Consider using projection conversions available packages sp, sf rgdal better results.","code":""},{"path":"/reference/latlong2km.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Simon N. Wood","code":""},{"path":"/reference/make.soapgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"routine simply creates grid knots (correct format) used \"internal\" part soap film smoother","code":""},{"path":"/reference/make.soapgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"","code":"make.soapgrid(bnd, n.grid)"},{"path":"/reference/make.soapgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"bnd list elements x y give locations boundary vertices. first last elements . n.grid either one number giving number points along x y axes used create grid, vector giving number x direction, y direction.","code":""},{"path":"/reference/make.soapgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"list elements x y, containing knot locations.","code":""},{"path":"/reference/make.soapgrid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"David L Miller","code":""},{"path":"/reference/mexdolphins.html","id":null,"dir":"Reference","previous_headings":"","what":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Data combination several NOAA shipboard surveys conducted pan-tropical spotted dolphins Gulf Mexico. data consist 47 observations groups dolphins. group size recorded, well Beaufort sea state time observation. Coordinates observation bathymetry data also available covariates analysis. complete example analysis (description data) provided http://distancesampling.org/R/vignettes/mexico-analysis.html.","code":""},{"path":"/reference/mexdolphins.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Halpin, P.N., .J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, . Dimatteo, J. Cleary, C. Good, L.B. Crowder, K.D. Hyrenbach. 2009. OBIS-SEAMAP: world data center marine mammal, sea bird, sea turtle distributions. Oceanography 22(2):104-115 NOAA Southeast Fisheries Science Center. 1996. Report Cetacean Survey Oceanic Selected Continental Shelf Waters Northern Gulf Mexico aboard NOAA Ship Oregon II (Cruise 220)","code":""},{"path":"/reference/obs_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Observed versus expected diagnostics for fitted DSMs — obs_exp","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"Given covariate, calculate observed expected counts unique value covariate. can useful goodness fit check DSMs.","code":""},{"path":"/reference/obs_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"obs_exp(model, covar, cut = NULL)"},{"path":"/reference/obs_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"model fitted dsm model object covar covariate aggregate (character) cut vector cut points aggregate . supplied, unique values covar used.","code":""},{"path":"/reference/obs_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"data.frame values observed expected counts.","code":""},{"path":"/reference/obs_exp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"One strategy model checking calculate observed expected counts different aggregations variable. match well model fit good.","code":""},{"path":"/reference/obs_exp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"David L Miller, suggestion Mark Bravington.","code":""},{"path":"/reference/obs_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # example with the Gulf of Mexico dolphin data data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) } # }"},{"path":"/reference/plot.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a density surface model. — plot.dsm","title":"Plot a density surface model. — plot.dsm","text":"See plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a density surface model. — plot.dsm","text":"","code":"# S3 method for class 'dsm' plot(x, ...)"},{"path":"/reference/plot.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a density surface model. — plot.dsm","text":"x model fitted dsm ... arguments passed plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a density surface model. — plot.dsm","text":"plot!","code":""},{"path":[]},{"path":"/reference/plot.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a density surface model. — plot.dsm","text":"David L. Miller","code":""},{"path":"/reference/plot.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Create plots of abundance uncertainty — plot.dsm.var","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"Note prediction data set must x y columns even used model.","code":""},{"path":"/reference/plot.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"","code":"# S3 method for class 'dsm.var' plot(   x,   poly = NULL,   limits = NULL,   breaks = NULL,   legend.breaks = NULL,   xlab = \"x\",   ylab = \"y\",   observations = TRUE,   plot = TRUE,   boxplot.coef = 1.5,   x.name = \"x\",   y.name = \"y\",   gg.grad = NULL,   ... )"},{"path":"/reference/plot.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"x dsm.var object poly list data.frame columns x y, gives coordinates polygon draw. may also optionally column group, many polygons. limits limits fill colours breaks breaks colour fill legend.breaks breaks displayed xlab label x axis ylab label y axis observations observations plotted? plot actually plot map, just return ggplot2 object? boxplot.coef control trimming (summary.dsm.var), effect bootstrap file saved. x.name name variable plot x axis. y.name name variable plot y axis. gg.grad optional ggplot gradient object. ... arguments","code":""},{"path":"/reference/plot.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"plot","code":""},{"path":"/reference/plot.dsm.var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"order get plotting work dsm_var_prop dsm_var_gam, one must first format data correctly since functions designed compute general summaries. One summary calculated element list pred supplied dsm_var_prop dsm_var_gam. plot uncertainty prediction grid, pred (data.frame), say, can create correct format simply using pred.new <- split(pred,1:nrow(pred)).","code":""},{"path":[]},{"path":"/reference/plot.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/plot_pred_by_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially plot predictions per model term — plot_pred_by_term","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"Plot effect smooth model spatially. term model, plot effect space. Plots made scale, relative influence smooth can seen.","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"plot_pred_by_term(dsm.obj, data, location.cov = c(\"x\", \"y\"))"},{"path":"/reference/plot_pred_by_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"dsm.obj fitted dsm object data data use plot (often prediction grid), data also include width height columns plotting location.cov deprecated, use location.cov","code":""},{"path":"/reference/plot_pred_by_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"ggplot2 plot","code":""},{"path":"/reference/plot_pred_by_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"David L Miller (idea taken inlabru)","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data and fit a model data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y) + s(depth), hr.model, segdata, obsdata)  preddata$width <- preddata$height <- sqrt(preddata$area)  # make the plot plot_pred_by_term(mod1, preddata, c(\"x\",\"y\"))  # better plot would be # library(viridis) # plot_pred_by_term(mod1, preddata, c(\"x\",\"y\")) + scale_fill_viridis() } # }"},{"path":"/reference/predict.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from a fitted density surface model — predict.dsm","title":"Predict from a fitted density surface model — predict.dsm","text":"Make predictions density abundance outside (inside) covered area.","code":""},{"path":"/reference/predict.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from a fitted density surface model — predict.dsm","text":"","code":"# S3 method for class 'dsm' predict(object, newdata = NULL, off.set = NULL, type = \"response\", ...)"},{"path":"/reference/predict.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from a fitted density surface model — predict.dsm","text":"object fitted dsm object newdata spatially referenced covariates e.g. altitude, depth, distance shore, etc. Covariates data.frame must names identical variable names used fitting model .set area cells prediction grid. units segments/distances given dsm. Replaces column newdata called .set supplied. Ignored newdata supplied type scale results . default \"response\", see predict.gam explanation options (usually necessary) ... arguments passed predict.gam","code":""},{"path":"/reference/predict.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from a fitted density surface model — predict.dsm","text":"predicted values response scale default (unless type specified, case see predict.gam).","code":""},{"path":"/reference/predict.dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict from a fitted density surface model — predict.dsm","text":"newdata supplied, predictions made data built model. Note order results necessarily segdata (segment data) data.frame supplied dsm. area .set used argument supplied, otherwise look areas column named .set newdata. Either way link function (usually log) applied offsets, need log passing function.","code":""},{"path":[]},{"path":"/reference/predict.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict from a fitted density surface model — predict.dsm","text":"David L. Miller","code":""},{"path":"/reference/predict.fake_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for fake detection functions — predict.fake_ddf","title":"Prediction for fake detection functions — predict.fake_ddf","text":"Prediction function dummy detection functions. function returns many 1s rows newdata. esw=TRUE strip width returned.","code":""},{"path":"/reference/predict.fake_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for fake detection functions — predict.fake_ddf","text":"","code":"# S3 method for class 'fake_ddf' predict(   object,   newdata = NULL,   compute = FALSE,   int.range = NULL,   esw = FALSE,   ... )"},{"path":"/reference/predict.fake_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for fake detection functions — predict.fake_ddf","text":"object model object newdata many 1s return? compute unused, compatibility mrds::predict int.range unused, compatibility mrds::predict esw strip width returned? ... S3 consistency","code":""},{"path":"/reference/predict.fake_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction for fake detection functions — predict.fake_ddf","text":"David L Miller","code":""},{"path":"/reference/print.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model object — print.dsm","title":"Print a description of a density surface model object — print.dsm","text":"method just gives short description fitted model. Use summary.dsm method information.","code":""},{"path":"/reference/print.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model object — print.dsm","text":"","code":"# S3 method for class 'dsm' print(x, ...)"},{"path":"/reference/print.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model object — print.dsm","text":"x model fitted dsm ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model object — print.dsm","text":"NULL","code":""},{"path":"/reference/print.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model object — print.dsm","text":"David L. Miller","code":""},{"path":"/reference/print.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm.var","title":"Print a description of a density surface model variance object — print.dsm.var","text":"method provides short summary, use summary.dsm.var method information.","code":""},{"path":"/reference/print.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm.var","text":"","code":"# S3 method for class 'dsm.var' print(x, ...)"},{"path":"/reference/print.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm.var","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model variance object — print.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm_varprop","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"method provides short summary, see summary.dsm_varprop.","code":""},{"path":"/reference/print.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"","code":"# S3 method for class 'dsm_varprop' print(x, ...)"},{"path":"/reference/print.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":[]},{"path":"/reference/print.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm.var","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"See summary.dsm.var information.","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"","code":"# S3 method for class 'summary.dsm.var' print(x, ...)"},{"path":"/reference/print.summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm_varprop","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"See summary.dsm_varprop information.","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"","code":"# S3 method for class 'summary.dsm_varprop' print(x, ...)"},{"path":"/reference/print.summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/rqgam.check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"function deprecated, use rqgam_check.","code":""},{"path":"/reference/rqgam.check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"","code":"rqgam.check(gam.obj, ...)"},{"path":"/reference/rqgam.check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Reproduces \"Resids vs. linear pred\" plot gam.check using randomised quantile residuals, la Dunn Smyth (1996). Checks heteroskedasticity usual, looking \"funnel\"-type structures points, much easier randomised quantile residuals deviance residuals, model uses count distribution response.","code":""},{"path":"/reference/rqgam_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"rqgam_check(gam.obj, ...)"},{"path":"/reference/rqgam_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"just plots!","code":""},{"path":"/reference/rqgam_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Note function works negative binomial Tweedie response distributions. Earlier versions function produced full gam.check output, confusing one plots really useful.  Checks k computed, need done using gam.check.","code":""},{"path":"/reference/rqgam_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Based code Natalie Kelly, bugs added Dave Miller","code":""},{"path":"/reference/rqgam_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"# \\donttest{ library(Distance) library(dsm) library(tweedie)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.433 #> No survey area information supplied, only estimating detection function.  # fit a simple smooth of x and y with a Tweedie response with estimated #  p parameter mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw()) #> Warning: Some observations are outside of detection function 1 truncation! rqgam_check(mod1)  # }"},{"path":"/reference/summary.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted density surface model — summary.dsm","title":"Summarize a fitted density surface model — summary.dsm","text":"Gives brief summary fitted dsm object.","code":""},{"path":"/reference/summary.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted density surface model — summary.dsm","text":"","code":"# S3 method for class 'dsm' summary(object, ...)"},{"path":"/reference/summary.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted density surface model — summary.dsm","text":"object model fitted dsm ... arguments passed summary.gam","code":""},{"path":"/reference/summary.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted density surface model — summary.dsm","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize a fitted density surface model — summary.dsm","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm.var","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"Gives brief summary fitted dsm variance object.","code":""},{"path":"/reference/summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"","code":"# S3 method for class 'dsm.var' summary(   object,   alpha = 0.05,   boxplot.coef = 1.5,   bootstrap.subregions = NULL,   ... )"},{"path":"/reference/summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95\\ confidence intervals) boxplot.coef value coef used calculate outliers see boxplot. bootstrap.subregions list vectors logicals indices subregions variances need calculated (bootstraps (see dsm.var.prop use subregions variance propagation). ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm_varprop","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"Gives brief summary fitted dsm_varprop variance object.","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"","code":"# S3 method for class 'dsm_varprop' summary(object, alpha = 0.05, ...)"},{"path":"/reference/summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95% confidence internal) ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/trim.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimmed variance — trim.var","title":"Trimmed variance — trim.var","text":"Trim variance estimates bootstrap. defined percentage defined amount necessary bring median trimmed mean within 8% defined 'outliers'.","code":""},{"path":"/reference/trim.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimmed variance — trim.var","text":"","code":"trim.var(untrimmed.bootstraps, boxplot.coef = 1.5)"},{"path":"/reference/trim.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimmed variance — trim.var","text":"untrimmed.bootstraps (usually $study.area.total element returned dsm.var.movblk bootstrap object. boxplot.coef value coef used calculate outliers see boxplot.","code":""},{"path":"/reference/trim.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimmed variance — trim.var","text":"trimmed variance","code":""},{"path":"/reference/trim.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trimmed variance — trim.var","text":"Louise Burt","code":""},{"path":"/reference/vis.concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis.concurvity","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"function deprecated, use vis_concurvity.","code":""},{"path":"/reference/vis.concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"","code":"vis.concurvity(model, type = \"estimate\")"},{"path":"/reference/vis.concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis_concurvity","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"Plot measures much one term model explained another. values high, one consider re-running variable selection one offending variables removed check stability term selection.","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"vis_concurvity(model, type = \"estimate\")"},{"path":"/reference/vis_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"methods considered somewhat experimental time. Consult concurvity information concurvity measures calculated.","code":""},{"path":"/reference/vis_concurvity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"David L Miller","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata)  # visualise concurvity using the \"estimate\" metric vis_concurvity(mod1) } # }"},{"path":"/news/index.html","id":"dsm-234","dir":"Changelog","previous_headings":"","what":"dsm 2.3.4","title":"dsm 2.3.4","text":"fix bug predictions NA (e.g., predictions outside soap boundary), variances NA due propagation (#4)","code":""},{"path":"/news/index.html","id":"dsm-233","dir":"Changelog","previous_headings":"","what":"dsm 2.3.3","title":"dsm 2.3.3","text":"CRAN release: 2022-08-20 dsm.var.prop -> dsm_var_prop dsm.var.gam -> dsm_var_gam dsm.var.movblk -> dsm_var_movblk dsm.cor -> dsm_cor rqgam.check -> rqgam_check vis.concurvity -> vis_concurvity var_type -> var.type","code":""},{"path":"/news/index.html","id":"dsm-232","dir":"Changelog","previous_headings":"","what":"dsm 2.3.2","title":"dsm 2.3.2","text":"CRAN release: 2022-03-17 fixed bug offset calculation dsm_varprop se estimation offsets logged twice. Thanks Megan Ferguson spotting . documentation now rmarkdown format dsm 2.3.1 transect type (line point) now determined detection function rather using transect= argument multiple detection functions now possible, use list() fitted detection functions supplied ddf.obj= argument dsm(). dsm.var.gam, dsm.var.prop dsm_varprop compatible. Vignette coming soon. Thanks Dave Fifield Ewan Wakefield extensive testing suggestions. See ?“dsm-data” information set data. strip transects now handled using dummy_ddf() function. presence~ response type removed Response options “D”, “density”, “Dhat”, “N”, “Nhat” “n” deprecated. Now “count”, “abundance.est” “density.est” allowed. Make predict.dsm work tibbles .set column newdata (error thrown previous code’s check ). Thanks Dave Fifield reporting issue. Fix bug inconsistent predictions density.est used response. Code predict.dsm simplified documentation updated. Thanks Dave Fifield reporting issue. mrds independent observer (io) trial models allowed detection function.Variance can estimated via dsm_varprop (usual limitation covariates may vary segment level) dsm.var.gam. feature currently experimental. Thanks Doug Sigourney Megan Ferguson help suggestions. See ?“dsm-data” information set data. example multiddf mrds analysis dsm https://github.com/densitymodelling/nefsc_fin_mrds_dsm availability= argument now works differently count models. availability must now length number segments (nrow(segment.data)) multiply offset (effective area) rather number animals observed coherent detectability handled case. dsm_varprop longer requies newdata supplied. newdata isn’t present just refitted original models returned, entries NA.","code":""},{"path":"/news/index.html","id":"dsm-230","dir":"Changelog","previous_headings":"","what":"dsm 2.3.0","title":"dsm 2.3.0","text":"CRAN release: 2020-04-22 fixed dsm_varprop (gam.fixed.priors) response distributions fixed scale parameters. Negative binomial results now may incorrect!","code":""},{"path":"/news/index.html","id":"dsm-2218","dir":"Changelog","previous_headings":"","what":"dsm 2.2.18","title":"dsm 2.2.18","text":"Clarification predict.dsm return documentation. Thanks Dave Fifield suggestion. .set=NULL lead predictions 0 density used. Now just returns density. Thanks Dave Fifield catch.","code":""},{"path":"/news/index.html","id":"dsm-2217","dir":"Changelog","previous_headings":"","what":"dsm 2.2.17","title":"dsm 2.2.17","text":"CRAN release: 2019-01-19 Now Suggests package “sp” (needed parts mexdolphin example data)","code":""},{"path":"/news/index.html","id":"dsm-2216","dir":"Changelog","previous_headings":"","what":"dsm 2.2.16","title":"dsm 2.2.16","text":"CRAN release: 2018-06-27 check Sample.Labels match , none throw error. Thanks Madhura Davate bringing issue attention. gamma option removed, since now ignored gam(). using method=“GCV.Cp” gamma=1.4 still used (generally advise using REML rather GCV smoothing parameter selection) dsm now requires mgcv version 1.8-23 higher. dsm.var.prop (dsm_varprop) now throw error covariates detection function can’t anything useful new function obs_exp diagnostic function compare observed expected counts different covariate aggregations","code":""},{"path":"/news/index.html","id":"dsm-2215","dir":"Changelog","previous_headings":"","what":"dsm 2.2.15","title":"dsm 2.2.15","text":"CRAN release: 2017-07-03 mexdolphins loaded using data(), data longer need attach()ed dsm.var.prop throws error count N used response. dsm.var.prop now calls dsm_varprop, keeps scale parameter constant refitting models variance propagation. dsm_varprop provides lower-level interface variance propagation probably faster flexible. add vis.concurvity visually assess concurvity (colinearity extended smooth terms) model new model diagnostic function: plot_pred_by_term – plots effect smooth model spatially. remove “abundance” option response, use “count” dsm.var.prop dsm_varprop better diagnostics checking variance model hasn’t done something weird dsm.var.movblk discards replicates model didn’t converge fix potential issue negative binomial response used variance propagation (due scale parameterisations). dsm_varprop now refits nb() models negbin() advise users look deeply. Thanks Eric Rexstad spotting . fix bug predict.dsm() predictions “density” response including offset. Thanks Len Thomas spotting .","code":""},{"path":"/news/index.html","id":"dsm-2214","dir":"Changelog","previous_headings":"","what":"dsm 2.2.14","title":"dsm 2.2.14","text":"CRAN release: 2017-01-26 nasty bug predict.dsm fixed: logging offset twice newdata provided, leading odd checking plots. also effected results predict() newdata= specified. Thanks Megan Ferguson alerting us . add support point transects(!) rqgam.check() now produced linear predictor vs. randomised quantile residuals plot avoid confusion interpreting plots REML now default method smoothing parameter selection","code":""},{"path":"/news/index.html","id":"dsm-2213","dir":"Changelog","previous_headings":"","what":"dsm 2.2.13","title":"dsm 2.2.13","text":"CRAN release: 2016-10-03 dsm.var.gam now example dsm.var.prop now checks detection function model covariates","code":""},{"path":"/news/index.html","id":"dsm-2212","dir":"Changelog","previous_headings":"","what":"dsm 2.2.12","title":"dsm 2.2.12","text":"CRAN release: 2016-08-10 alpha argument now works analytical variance reported using dsm.var.prop dsm.var.gam print.summary summary methods. Thanks Jason Roberts Eric Rexstad spotting issue. dsm now uses numDeriv::grad calculate derivatives variance estimation – much faster. Thanks Jason Roberts fix. dsm now requires mrds version 2.1.15 higher, due internal changes fixed issue Effort column required even segment.area specified. Thanks Greg Lollback spotting issue. ensured ordering segments matched ordering observations aggregation observations correct. Thanks Dave Fifield reporting bug suggesting fix. check.cols longer checks see column called “distance” strip transects used","code":""},{"path":"/news/index.html","id":"dsm-2211","dir":"Changelog","previous_headings":"","what":"dsm 2.2.11","title":"dsm 2.2.11","text":"dsm.var.prop uses update() rather messing around eval() print.summary.dsm.var didn’t work GAM variance detection function specified, now fixed (thanks Natalia Dellabianca spotting ) Removed non-working plot() code variance examples – see Mexico pantropical spotted dolphins proper treatment plotting using projections (http://distancesampling.org/R/vignettes/mexico-analysis.html) plot.var.gam longer crashes default distance data doesn’t columns “x” “y”","code":""},{"path":"/news/index.html","id":"dsm-2210","dir":"Changelog","previous_headings":"","what":"dsm 2.2.10","title":"dsm 2.2.10","text":"corrected offsets examples dsm, dsm.var.prop, dsm.var.movblk updated mexdolphins data lat/long reversal prediction data non-list storage stopped use rqgam.check used non-negbin/Tweedie responses (thanks Phil Bouchet highlighting issue).","code":""},{"path":"/news/index.html","id":"dsm-229","dir":"Changelog","previous_headings":"","what":"dsm 2.2.9","title":"dsm 2.2.9","text":"CRAN release: 2015-07-29 Moving block bootstrap now works detection function (suggested Laura Williamson) Mexico dolphin data areas prediction grids segments re-projected. Thanks Phil Bouchet pointing error. Context: https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ","code":""},{"path":"/news/index.html","id":"dsm-228","dir":"Changelog","previous_headings":"","what":"dsm 2.2.8","title":"dsm 2.2.8","text":"Speed reduced memory usage variance calculations, thanks Filipe Dias asking . Better error observations matched detection function. Thanks Ricardo Lima highlighting . Check missing columns incorrect logic, now corrected tests. Thanks Julia Migné spotting .","code":""},{"path":"/news/index.html","id":"dsm-227","dir":"Changelog","previous_headings":"","what":"dsm 2.2.7","title":"dsm 2.2.7","text":"Plotting check plots using random quantile residuals (rqgam.check) didn’t work Tweedie tw family models, now fixed. Thanks Laura Mannocci Jason Roberts pointing . Plotting CV used matrix() coerce data data.frame – thus converting elements simplest type. Errors ensued. Thanks Adrian Schiavini finding bug!","code":""},{"path":"/news/index.html","id":"dsm-226","dir":"Changelog","previous_headings":"","what":"dsm 2.2.6","title":"dsm 2.2.6","text":"dsm.var.prop dsm.var.gam now allow supply single number offset, copy many times need. Thanks Filipe Dias suggesting .","code":""},{"path":"/news/index.html","id":"dsm-225","dir":"Changelog","previous_headings":"","what":"dsm 2.2.5","title":"dsm 2.2.5","text":"CRAN release: 2014-09-16 dsm.var.prop now throw error detection function dsm ’s trying calculate variance (thanks Adrián Schiavini finding ) variance/CV plotting threw error detection function supplied observation=TRUE. Now error sidestepped (, thanks Adrián Schiavini finding ).","code":""},{"path":"/news/index.html","id":"dsm-224","dir":"Changelog","previous_headings":"","what":"dsm 2.2.4","title":"dsm 2.2.4","text":"CRAN release: 2014-06-17 changed stop() warning() data detection function included observation data frame, automatically excluded later, bug found Eric Rexstad Anna Cucknell","code":""},{"path":"/news/index.html","id":"dsm-223","dir":"Changelog","previous_headings":"","what":"dsm 2.2.3","title":"dsm 2.2.3","text":"CRAN release: 2014-04-23 moving block boostrap using observed abundances residuals therefore gave wrong answers (Eric Rexstad found bug)","code":""},{"path":"/news/index.html","id":"dsm-222","dir":"Changelog","previous_headings":"","what":"dsm 2.2.2","title":"dsm 2.2.2","text":"version number reporting now works (Len Thomas found bug) documentation default density presence models now weighted area segment","code":""},{"path":"/news/index.html","id":"dsm-221","dir":"Changelog","previous_headings":"","what":"dsm 2.2.1","title":"dsm 2.2.1","text":"switch new testthat spec added new response variables (Dhat, density.est, count, n), suggestion Len Thomas","code":""},{"path":"/news/index.html","id":"dsm-214","dir":"Changelog","previous_headings":"","what":"dsm 2.1.4","title":"dsm 2.1.4","text":"check observed distances within width now throw error","code":""},{"path":"/news/index.html","id":"dsm-213","dir":"Changelog","previous_headings":"","what":"dsm 2.1.3","title":"dsm 2.1.3","text":"CRAN release: 2013-08-20 CRAN-compatibility fixes","code":""},{"path":"/news/index.html","id":"dsm-212","dir":"Changelog","previous_headings":"","what":"dsm 2.1.2","title":"dsm 2.1.2","text":"updates dsm.cor unified plotting variance estimation tweaks non-dsm models","code":""},{"path":"/news/index.html","id":"dsm-206","dir":"Changelog","previous_headings":"","what":"dsm 2.0.6","title":"dsm 2.0.6","text":"removed warning () comparison length make.soapgrid need pass bnd list() dsm.cor now handles gamm objects dsm.cor allows one check residual autocorrelation models presence/absence data now longer needs dummy detection function object","code":""},{"path":"/news/index.html","id":"dsm-205","dir":"Changelog","previous_headings":"","what":"dsm 2.0.5","title":"dsm 2.0.5","text":"started NEWS file fixed bug dsm.var.gam() didn’t return predictions plotting result fail silly typo docs check ’re using mgcv version >= 1.7-24 GAMM fitting warn ","code":""}]
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-    <loc>/reference/dsm.var.movblk.html</loc>
-  </url>
-  <url>
-    <loc>/reference/dsm.var.prop.html</loc>
-  </url>
-  <url>
-    <loc>/reference/dsm_cor.html</loc>
-  </url>
-  <url>
-    <loc>/reference/dsm_varprop.html</loc>
-  </url>
-  <url>
-    <loc>/reference/dsm_var_gam.html</loc>
-  </url>
-  <url>
-    <loc>/reference/dsm_var_movblk.html</loc>
-  </url>
-  <url>
-    <loc>/reference/dsm_var_prop.html</loc>
-  </url>
-  <url>
-    <loc>/reference/dummy_ddf.html</loc>
-  </url>
-  <url>
-    <loc>/reference/generate.ds.uncertainty.html</loc>
-  </url>
-  <url>
-    <loc>/reference/generate.mb.sample.html</loc>
-  </url>
-  <url>
-    <loc>/reference/index.html</loc>
-  </url>
-  <url>
-    <loc>/reference/latlong2km.html</loc>
-  </url>
-  <url>
-    <loc>/reference/make.soapgrid.html</loc>
-  </url>
-  <url>
-    <loc>/reference/mexdolphins.html</loc>
-  </url>
-  <url>
-    <loc>/reference/obs_exp.html</loc>
-  </url>
-  <url>
-    <loc>/reference/plot.dsm.html</loc>
-  </url>
-  <url>
-    <loc>/reference/plot.dsm.var.html</loc>
-  </url>
-  <url>
-    <loc>/reference/plot_pred_by_term.html</loc>
-  </url>
-  <url>
-    <loc>/reference/predict.dsm.html</loc>
-  </url>
-  <url>
-    <loc>/reference/predict.fake_ddf.html</loc>
-  </url>
-  <url>
-    <loc>/reference/print.dsm.html</loc>
-  </url>
-  <url>
-    <loc>/reference/print.dsm.var.html</loc>
-  </url>
-  <url>
-    <loc>/reference/print.dsm_varprop.html</loc>
-  </url>
-  <url>
-    <loc>/reference/print.summary.dsm.var.html</loc>
-  </url>
-  <url>
-    <loc>/reference/print.summary.dsm_varprop.html</loc>
-  </url>
-  <url>
-    <loc>/reference/rqgam.check.html</loc>
-  </url>
-  <url>
-    <loc>/reference/rqgam_check.html</loc>
-  </url>
-  <url>
-    <loc>/reference/summary.dsm.html</loc>
-  </url>
-  <url>
-    <loc>/reference/summary.dsm.var.html</loc>
-  </url>
-  <url>
-    <loc>/reference/summary.dsm_varprop.html</loc>
-  </url>
-  <url>
-    <loc>/reference/trim.var.html</loc>
-  </url>
-  <url>
-    <loc>/reference/vis.concurvity.html</loc>
-  </url>
-  <url>
-    <loc>/reference/vis_concurvity.html</loc>
-  </url>
+<urlset xmlns = 'http://www.sitemaps.org/schemas/sitemap/0.9'>
+<url><loc>/404.html</loc></url>
+<url><loc>/articles/data_format/dsm-data-formatting.html</loc></url>
+<url><loc>/articles/index.html</loc></url>
+<url><loc>/articles/lines_gomex/mexico-analysis.html</loc></url>
+<url><loc>/authors.html</loc></url>
+<url><loc>/index.html</loc></url>
+<url><loc>/LICENSE-text.html</loc></url>
+<url><loc>/news/index.html</loc></url>
+<url><loc>/reference/block.info.per.su.html</loc></url>
+<url><loc>/reference/check.cols.html</loc></url>
+<url><loc>/reference/dsm-data.html</loc></url>
+<url><loc>/reference/dsm-package.html</loc></url>
+<url><loc>/reference/dsm.cor.html</loc></url>
+<url><loc>/reference/dsm.html</loc></url>
+<url><loc>/reference/dsm.var.gam.html</loc></url>
+<url><loc>/reference/dsm.var.movblk.html</loc></url>
+<url><loc>/reference/dsm.var.prop.html</loc></url>
+<url><loc>/reference/dsm_cor.html</loc></url>
+<url><loc>/reference/dsm_varprop.html</loc></url>
+<url><loc>/reference/dsm_var_gam.html</loc></url>
+<url><loc>/reference/dsm_var_movblk.html</loc></url>
+<url><loc>/reference/dsm_var_prop.html</loc></url>
+<url><loc>/reference/dummy_ddf.html</loc></url>
+<url><loc>/reference/generate.ds.uncertainty.html</loc></url>
+<url><loc>/reference/generate.mb.sample.html</loc></url>
+<url><loc>/reference/index.html</loc></url>
+<url><loc>/reference/latlong2km.html</loc></url>
+<url><loc>/reference/make.soapgrid.html</loc></url>
+<url><loc>/reference/mexdolphins.html</loc></url>
+<url><loc>/reference/obs_exp.html</loc></url>
+<url><loc>/reference/plot.dsm.html</loc></url>
+<url><loc>/reference/plot.dsm.var.html</loc></url>
+<url><loc>/reference/plot_pred_by_term.html</loc></url>
+<url><loc>/reference/predict.dsm.html</loc></url>
+<url><loc>/reference/predict.fake_ddf.html</loc></url>
+<url><loc>/reference/print.dsm.html</loc></url>
+<url><loc>/reference/print.dsm.var.html</loc></url>
+<url><loc>/reference/print.dsm_varprop.html</loc></url>
+<url><loc>/reference/print.summary.dsm.var.html</loc></url>
+<url><loc>/reference/print.summary.dsm_varprop.html</loc></url>
+<url><loc>/reference/rqgam.check.html</loc></url>
+<url><loc>/reference/rqgam_check.html</loc></url>
+<url><loc>/reference/summary.dsm.html</loc></url>
+<url><loc>/reference/summary.dsm.var.html</loc></url>
+<url><loc>/reference/summary.dsm_varprop.html</loc></url>
+<url><loc>/reference/trim.var.html</loc></url>
+<url><loc>/reference/vis.concurvity.html</loc></url>
+<url><loc>/reference/vis_concurvity.html</loc></url>
 </urlset>
+

From b4c62400009b5d848802e85d89cfc4e8de4f0f5e Mon Sep 17 00:00:00 2001
From: erex <ericincrail@gmail.com>
Date: Mon, 18 Nov 2024 17:03:34 +0000
Subject: [PATCH 6/6] embedded clarity in _pkgdown.yml

---
 .Rhistory                                     |    4 +-
 README.md                                     |    9 +-
 _pkgdown.yml                                  |    5 +-
 docs/404.html                                 |    5 +-
 docs/LICENSE-text.html                        |    2 +-
 .../data_format/dsm-data-formatting.html      |   58 +-
 docs/articles/index.html                      |    2 +-
 .../articles/lines_gomex/mexico-analysis.html |   95 +-
 docs/authors.html                             |    2 +-
 docs/deps/data-deps.txt                       |    4 +-
 docs/deps/font-awesome-6.5.2/css/all.css      | 8028 +++++++++++++++++
 docs/deps/font-awesome-6.5.2/css/all.min.css  |    9 +
 docs/deps/font-awesome-6.5.2/css/v4-shims.css | 2194 +++++
 .../font-awesome-6.5.2/css/v4-shims.min.css   |    6 +
 .../webfonts/fa-brands-400.ttf                |  Bin 0 -> 209128 bytes
 .../webfonts/fa-brands-400.woff2              |  Bin 0 -> 117852 bytes
 .../webfonts/fa-regular-400.ttf               |  Bin 0 -> 67860 bytes
 .../webfonts/fa-regular-400.woff2             |  Bin 0 -> 25392 bytes
 .../webfonts/fa-solid-900.ttf                 |  Bin 0 -> 420332 bytes
 .../webfonts/fa-solid-900.woff2               |  Bin 0 -> 156400 bytes
 .../webfonts/fa-v4compatibility.ttf           |  Bin 0 -> 10832 bytes
 .../webfonts/fa-v4compatibility.woff2         |  Bin 0 -> 4792 bytes
 docs/index.html                               |   17 +-
 docs/news/index.html                          |    2 +-
 docs/pkgdown.yml                              |    4 +-
 docs/reference/block.info.per.su.html         |    4 +-
 docs/reference/check.cols.html                |    4 +-
 docs/reference/dsm-data.html                  |    4 +-
 docs/reference/dsm-package.html               |    4 +-
 docs/reference/dsm.cor.html                   |    2 +-
 docs/reference/dsm.html                       |    4 +-
 docs/reference/dsm.var.gam.html               |    2 +-
 docs/reference/dsm.var.movblk.html            |    2 +-
 docs/reference/dsm.var.prop.html              |    2 +-
 docs/reference/dsm_cor.html                   |    4 +-
 docs/reference/dsm_var_gam.html               |    4 +-
 docs/reference/dsm_var_movblk.html            |    4 +-
 docs/reference/dsm_var_prop.html              |    4 +-
 docs/reference/dsm_varprop.html               |    4 +-
 docs/reference/dummy_ddf.html                 |    4 +-
 docs/reference/generate.ds.uncertainty.html   |    4 +-
 docs/reference/generate.mb.sample.html        |    4 +-
 docs/reference/index.html                     |    2 +-
 docs/reference/latlong2km.html                |    4 +-
 docs/reference/make.soapgrid.html             |    4 +-
 docs/reference/mexdolphins.html               |    4 +-
 docs/reference/obs_exp.html                   |    4 +-
 docs/reference/plot.dsm.html                  |    2 +-
 docs/reference/plot.dsm.var.html              |    4 +-
 docs/reference/plot_pred_by_term.html         |    4 +-
 docs/reference/predict.dsm.html               |    4 +-
 docs/reference/predict.fake_ddf.html          |    4 +-
 docs/reference/print.dsm.html                 |    4 +-
 docs/reference/print.dsm.var.html             |    4 +-
 docs/reference/print.dsm_varprop.html         |    4 +-
 docs/reference/print.summary.dsm.var.html     |    2 +-
 docs/reference/print.summary.dsm_varprop.html |    2 +-
 docs/reference/rqgam.check.html               |    2 +-
 docs/reference/rqgam_check.html               |    4 +-
 docs/reference/summary.dsm.html               |    2 +-
 docs/reference/summary.dsm.var.html           |    2 +-
 docs/reference/summary.dsm_varprop.html       |    4 +-
 docs/reference/trim.var.html                  |    4 +-
 docs/reference/vis.concurvity.html            |    2 +-
 docs/reference/vis_concurvity.html            |    4 +-
 docs/search.json                              |    2 +-
 66 files changed, 10418 insertions(+), 166 deletions(-)
 create mode 100644 docs/deps/font-awesome-6.5.2/css/all.css
 create mode 100644 docs/deps/font-awesome-6.5.2/css/all.min.css
 create mode 100644 docs/deps/font-awesome-6.5.2/css/v4-shims.css
 create mode 100644 docs/deps/font-awesome-6.5.2/css/v4-shims.min.css
 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.ttf
 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.woff2
 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.ttf
 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.woff2
 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.ttf
 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.woff2
 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.ttf
 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.woff2

diff --git a/.Rhistory b/.Rhistory
index 0c1aa13..48cb9a0 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,5 +1,3 @@
-subtitle = "Points are segment centres") +
-scale_fill_viridis_c(option = "magma", guide = "none")
 plot_segments
 # p <- qplot(data=survey.area, x=x, y=y, geom="polygon",fill=I("lightblue"),
 # ylab="y", xlab="x", alpha=I(0.7))
@@ -510,3 +508,5 @@ gof_ds(detfc.hr.null)
 dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method="REML")
 summary(dsm.xy)
 unique(detfc.hr.null$ddf$fitted)
+library(pkgdown)
+build_site()
diff --git a/README.md b/README.md
index a491393..712cc26 100644
--- a/README.md
+++ b/README.md
@@ -9,9 +9,14 @@
 
 `dsm` provides a Generalized Additive Model (GAM)-based approach to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.
 
-Open Access paper describing the methods: [Miller, DL, ML Burt, EA Rexstad and L Thomas (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution.]( https://doi.org/10.1111/2041-210X.12105)
+Open Access paper describing the methods: 
+
+- Miller, DL, ML Burt, EA Rexstad and L Thomas (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution. [https://doi.org/10.1111/2041-210X.12105](https://doi.org/10.1111/2041-210X.12105)
 
 # Using `dsm`
 
-Examples of how to use `dsm` and other distance sampling R packages are available at [examples.distancesampling.org](http://examples.distancesampling.org).
+Examples of how to use `dsm` see 
+
+- https://distancesampling.org/dsm/articles/data_format/dsm-data-formatting.html
+- https://distancesampling.org/dsm/articles/lines_gomex/mexico-analysis.html
 
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 606efaa..d72b120 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -5,8 +5,9 @@ template:
     bg: "#fcfaf2"
     fg: "#000000"
     primary: "#0542a3"
-  include-in-header:
-    file: Clarity.txt
+  includes:
+    in_header: |
+      <script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script>
 
 navbar:
   bg: primary
diff --git a/docs/404.html b/docs/404.html
index 79b224a..d93e97a 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -8,9 +8,10 @@
 <title>Page not found (404) • dsm</title>
 <script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
-<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
+<script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script>
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 6b7eae5..86f4d6d 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • dsm</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • dsm</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/articles/data_format/dsm-data-formatting.html b/docs/articles/data_format/dsm-data-formatting.html
index 10f1ac6..8c7ec99 100644
--- a/docs/articles/data_format/dsm-data-formatting.html
+++ b/docs/articles/data_format/dsm-data-formatting.html
@@ -8,13 +8,14 @@
 <title>`dsm` data formatting example • dsm</title>
 <script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="../../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
-<link href="../../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="`dsm` data formatting example">
 <meta name="description" content="'Making sure your data is in the correct format for `dsm`.'
 ">
 <meta property="og:description" content="'Making sure your data is in the correct format for `dsm`.'
 ">
+<script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script>
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
@@ -158,10 +159,9 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <p>As an example, we will look at an aerial survey of seabirds off the coast of Rhode Island, USA. Data from these surveys were collected by the University of Rhode Island and analysed in <span class="citation">K. Winiarski, Miller, Paton, &amp; McWilliams (2013)</span>, <span class="citation">K. J. Winiarski, Burt, et al. (2014)</span> and <span class="citation">K. J. Winiarski, Miller, Paton, &amp; McWilliams (2014)</span>. I have the transects saved as a shapefile that I will then “chop-up” into segments. To start with let’s plot the data with the coastline:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning: package 'sf' was built under R version 4.4.2</span></span></code></pre>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># coast data, just for reference</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># coast data, just for reference</span></span>
 <span><span class="va">coastline</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_read.html" class="external-link">read_sf</a></span><span class="op">(</span><span class="st">"data/ri_coast.shp"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># our transect lines</span></span>
@@ -181,7 +181,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-4-1.png"><!-- --></p>
 <p>In the above code we have loaded the <a href="https://r-spatial.github.io/sf/" class="external-link"><code>sf</code> package</a>. This is the package we will use to do most of our spatial work in segmenting the data. It has most of the tools expected from a GIS, though the package is still being developed so its functionality is always increasing.</p>
 <p>Investigating the <code>transects</code> data we loaded, we can see it consists of a <code>data.frame</code> with some extra bits (the spatial information, including locations (<code>geometry</code>) and projection (<code>CRS</code>)):</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">transects</span></span></code></pre></div>
 <pre><code><span><span class="co">## Simple feature collection with 26 features and 1 field</span></span>
 <span><span class="co">## Geometry type: LINESTRING</span></span>
@@ -204,7 +204,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #949494;"># ℹ 16 more rows</span></span></span></code></pre>
 <p>We have one row per transect (for a total of 24 transects), each of which consists of a line joining the start and end points that make up the transects. If we want we can plot individual rows via <code>plot(transects[1,])</code>; this can be useful to check that each row is a single line, or if further processing is needed (this can be tricky and is not covered in this tutorial but see “More information” below).</p>
 <p>This data looks in good shape, so we can use the <code>st_segmentize</code> function to take each transect and make segments from them. To make sure everything works correctly, we need to project the data first. Here I’m using an appropriate projected coordinate system (EPSG code 6348), which is the Rhode Island State Plane.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># project transects</span></span>
 <span><span class="va">transects</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">transects</span>, <span class="fl">6348</span><span class="op">)</span></span>
 <span></span>
@@ -216,7 +216,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="va">transects</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">transects</span>, <span class="fl">4326</span><span class="op">)</span></span></code></pre></div>
 <p>Here we know the truncation used for the detection function was 1000m (the distances were collected in bins and this was the further bin), and since we’re trying to make our segments approximately square, we set the length to be twice that (since the truncation applies to either side of the transect), so 2000m.</p>
 <p>Looking at what that did:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
 <pre><code><span><span class="co">## Simple feature collection with 26 features and 1 field</span></span>
 <span><span class="co">## Geometry type: LINESTRING</span></span>
@@ -238,13 +238,13 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #BCBCBC;">10</span>       10 (-71.421 41.38723, -71.41952 41.36946, -71.41804 41.3517, -71.41656…</span></span>
 <span><span class="co">## <span style="color: #949494;"># ℹ 16 more rows</span></span></span></code></pre>
 <p>See now that each row has many coordinates attached to it, just looking at the first row (transect 1) and comparing the coordinates between <code>transects</code> and <code>segs</code></p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">transects</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##              X        Y L1</span></span>
 <span><span class="co">## [1,] -71.86948 41.29969  1</span></span>
 <span><span class="co">## [2,] -71.86581 41.25891  1</span></span></code></pre>
 <p><code>transects</code> has just two coordinates in it (the start and end points of the line). Whereas:</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">segs</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##              X        Y L1</span></span>
 <span><span class="co">## [1,] -71.86948 41.29969  1</span></span>
@@ -253,7 +253,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## [4,] -71.86581 41.25891  1</span></span></code></pre>
 <p><code>segs</code> has 5, corresponding to our segment cut points.</p>
 <p>We now need to break up the rows of <code>segs</code> into multiple rows, one per segment. This is a bit fiddly. We use a function provided by <a href="https://dieghernan.github.io" class="external-link"><code>dieghernan</code></a> on <a href="https://dieghernan.github.io/201905_Cast-to-subsegments/" class="external-link">their site</a> to do this. We first load the function (don’t worry about understanding the code!):</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">stdh_cast_substring</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">to</span> <span class="op">=</span> <span class="st">"MULTILINESTRING"</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  <span class="va">ggg</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_geometry</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
 <span></span>
@@ -290,11 +290,11 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">endgeom</span><span class="op">)</span></span>
 <span><span class="op">}</span></span></code></pre></div>
 <p>We can then use it to separate the segments and look at the results:</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu">stdh_cast_substring</span><span class="op">(</span><span class="va">segs</span>, to<span class="op">=</span><span class="st">"LINESTRING"</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in st_cast.sf(., "LINESTRING"): repeating attributes for all</span></span>
 <span><span class="co">## sub-geometries for which they may not be constant</span></span></code></pre>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
 <pre><code><span><span class="co">## Simple feature collection with 590 features and 1 field</span></span>
 <span><span class="co">## Geometry type: LINESTRING</span></span>
@@ -316,17 +316,17 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #BCBCBC;">10</span>        3 (-71.78654 41.29813, -71.78505 41.28065)</span></span>
 <span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
 <p>We now have 590 segments between 1km and 2km long. We can check the lengths using the <code>st_length</code> function and plot a histogram of the segment lengths:</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_measures.html" class="external-link">st_length</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>, xlab<span class="op">=</span><span class="st">"Segment lengths (m)"</span>, main<span class="op">=</span><span class="st">""</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dsm-data-formatting_files/figure-html/length-segs-1.png"><!-- --></p>
 <p>Note that in setting the <code>dfMaxLength</code> argument to <code>st_segmentize</code> we are giving a rough guide to the segment length and the algorithm inside <code>st_segmentize</code> tries to get segments to be as equal in length as possible (note the <span class="math inline">\(x\)</span> axis in the histogram).</p>
 <p>Note also that the <code>Transect</code> column in <code>segs</code> now has duplicate entries as each transect has multiple segments in it. We can now create our required columns for <code>dsm</code>.</p>
 <p>First, the <code>Effort</code> column can be generated using <code>st_length</code>:</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span><span class="op">$</span><span class="va">Effort</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_measures.html" class="external-link">st_length</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span></span></code></pre></div>
 <p>Then we can generate the <code>Sample.Labels</code>. Here I’ll use a more complicated naming scheme to show how that’s done, but one could simply write <code>segs$Sample.Label &lt;- 1:nrow(segs)</code> and that would be sufficient. Instead, I would like to have my <code>Sample.Label</code> be the “<code>YEAR</code>-<code>AREA</code>-<code>LINE</code>-<code>SEGMENT</code>” scheme I suggested above.</p>
 <p>There are fancier ways to do this, but for clarity, let’s use a <code>for</code> loop:</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># create a dummy column that we can fill in as we go</span></span>
 <span><span class="va">segs</span><span class="op">$</span><span class="va">Sample.Label</span> <span class="op">&lt;-</span> <span class="cn">NA</span></span>
 <span></span>
@@ -343,7 +343,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span>                                                           <span class="fl">1</span><span class="op">:</span><span class="va">n_segs</span>, sep<span class="op">=</span><span class="st">"-"</span><span class="op">)</span></span>
 <span><span class="op">}</span></span></code></pre></div>
 <p>Now we can check that looks right:</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs</span></span></code></pre></div>
 <pre><code><span><span class="co">## Simple feature collection with 590 features and 3 fields</span></span>
 <span><span class="co">## Geometry type: LINESTRING</span></span>
@@ -366,7 +366,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
 <p>With the required columns taken care of, we can also calculate the centroids of our segments to get the locations of each segment to use in the spatial model. Handily we can use <code>st_centroid</code> to do this in one step and keep all our data at the same time.</p>
 <p>We get a warning about how centroids might not be calculated correctly when latitude/longitude are used. So again we need to project the data first (to Rhode Island State Plane) for this step using <code>st_transform</code>.</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># save the line version of segs for plotting later</span></span>
 <span><span class="va">segs_lines</span> <span class="op">&lt;-</span> <span class="va">segs</span></span>
 <span></span>
@@ -376,7 +376,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co"># find centroids</span></span>
 <span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_unary.html" class="external-link">st_centroid</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: st_centroid assumes attributes are constant over geometries</span></span></code></pre>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># project back to lat/long</span></span>
 <span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">4326</span><span class="op">)</span></span>
 <span></span>
@@ -402,7 +402,7 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="co">## <span style="color: #BCBCBC;">10</span>        3  (-71.7858 41.28939)  <span style="text-decoration: underline;">1</span>948. 2010-RI-3-2 </span></span>
 <span><span class="co">## <span style="color: #949494;"># ℹ 580 more rows</span></span></span></code></pre>
 <p>Notice that our <code>geometry type</code> is now <code>POINT</code>. We can plot these centroids on our previous map:</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="co"># geom_sf knows what to do with spatial data</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">coastline</span><span class="op">)</span> <span class="op">+</span></span>
@@ -415,11 +415,11 @@ <h4 id="segmenting">Segmenting<a class="anchor" aria-label="anchor" href="#segme
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-6-1.png"><!-- --></p>
 <p>At present, the <code>dsm</code> package doesn’t know how to talk to <code>sf</code> objects, so as a final step we need to simplify <code>segs</code> to be a regular <code>data.frame</code>, which <code>dsm</code> can interpret. We can extract the coordinates of the centroids using <code>st_coordinates</code> and then append them onto a <code>data.frame</code> version of the object with the geometry dropped, like so:</p>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segs_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_drop_geometry</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span>,</span>
 <span>              <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>We can double check that looks right:</p>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">segs_df</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##   Transect       Effort Sample.Label         X        Y</span></span>
 <span><span class="co">## 1        1 1515.147 [m]  2010-RI-1-1 -71.86887 41.29289</span></span>
@@ -453,7 +453,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 </h4>
 <p>There are a few methods for building the observation table. Here I’ll illustrate one assigning detections to segments based on their distance (i.e., detections are associated with their closest segment centroid). This is appropriate when you don’t see forwards too far, so it’s unlikely that detections will be miss assigned. This is true, for instance, in an aerial survey where observers look out windows located on the sides of the plane but might be inappropriate for a shipboard survey where observers on the flying bridge can see way ahead of the ship.</p>
 <p>First loading the detection data for this survey (this would be what we use to fit the detection function, post-processing):</p>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html" class="external-link">read.csv</a></span><span class="op">(</span><span class="st">"data/loons.csv"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##         Date     Time Bin      Lat      Long Observer object size</span></span>
@@ -464,7 +464,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="co">## 5 2011-02-23 18991230   A 40.94153 -71.38517       KW    446    1</span></span>
 <span><span class="co">## 6 2011-02-07 18991230   A 40.94225 -71.46438       KW    457    1</span></span></code></pre>
 <p>We can see there are columns for latitude and longitude (<code>Lat</code> and <code>Long</code>). We can make this <code>data.frame</code> be an <code>sf</code> object by telling <code>sf</code> that these columns contain spatial coordinates (in <code>sf</code> lingo, “geometry”):</p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">obs</span>, coords<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Long"</span>, <span class="st">"Lat"</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co"># set the coordinate system</span></span>
 <span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fl">4326</span></span>
@@ -487,7 +487,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="co">## 9  2011-02-23 18991230   B       JV    814    1 POINT (-71.51172 40.97912)</span></span>
 <span><span class="co">## 10 2011-04-19 18991230   A       KW    911    1 POINT (-71.59762 40.98571)</span></span></code></pre>
 <p>We can overlay this on our previous map of transects:</p>
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="co"># geom_sf knows what to do with spatial data</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">coastline</span><span class="op">)</span> <span class="op">+</span></span>
@@ -500,7 +500,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dsm-data-formatting_files/figure-html/unnamed-chunk-7-1.png"><!-- --></p>
 <p>Going back to our <code>segs</code> object above, we can use the <code>st_join</code> function, combined with the <code>st_nearest_feature</code> function to control how the tables are linked. Again, we need to project the data to avoid issues.</p>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># project segs</span></span>
 <span><span class="va">segs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">segs</span>, <span class="fl">6348</span><span class="op">)</span></span>
 <span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_transform.html" class="external-link">st_transform</a></span><span class="op">(</span><span class="va">obs</span>, <span class="fl">6348</span><span class="op">)</span></span>
@@ -544,7 +544,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="co">## 9   2010-RI-7-22 POINT (-71.51172 40.97912)</span></span>
 <span><span class="co">## 10  2010-RI-24-9 POINT (-71.59762 40.98571)</span></span></code></pre>
 <p>Now <code>obs</code> has acquired additional columns from <code>segs</code>, including the <code>Sample.Label</code> (which we want) and <code>Effort</code> (which we don’t). To check this worked okay, we can plot the <code>obs</code> and <code>segs_lines</code> (the line version of the segments which we saved earlier) with colour coding for the <code>Sample.Label</code>. I randomized the colour order so it would be easier to tell if observations were misallocated.</p>
-<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># make a random colour palette to avoid similar colours being near each other</span></span>
 <span><span class="va">pal</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/grDevices/palettes.html" class="external-link">rainbow</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>, s<span class="op">=</span><span class="fl">.6</span>, v<span class="op">=</span><span class="fl">.9</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span>,<span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">segs</span><span class="op">)</span><span class="op">)</span><span class="op">]</span></span>
 <span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
@@ -561,7 +561,7 @@ <h4 id="relating-observations-and-segments-in-practice">Relating observations an
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dsm-data-formatting_files/figure-html/plot-check-1.png"><!-- --></p>
 <p>As with <code>segs</code> we can remove unnecessary columns and geometry to get the required columns only:</p>
-<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># get rid of "spatialness"</span></span>
 <span><span class="va">obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_drop_geometry</a></span><span class="op">(</span><span class="va">obs</span><span class="op">)</span></span>
 <span><span class="co"># select the columns we need</span></span>
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 493755a..ff543ea 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
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diff --git a/docs/articles/lines_gomex/mexico-analysis.html b/docs/articles/lines_gomex/mexico-analysis.html
index 8db67fb..e6ddbd5 100644
--- a/docs/articles/lines_gomex/mexico-analysis.html
+++ b/docs/articles/lines_gomex/mexico-analysis.html
@@ -8,13 +8,14 @@
 <title>Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico • dsm</title>
 <script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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-<link href="../../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico">
 <meta name="description" content="Example spatial analysis of pantropical spotted dolphins.
 ">
 <meta property="og:description" content="Example spatial analysis of pantropical spotted dolphins.
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@@ -162,10 +163,8 @@ <h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class
 <p>Using latitude and longitude when performing spatial smoothing can be problematic when certain smoother bases are used. In particular when bivariate isotropic bases are used the non-isotropic nature of latitude and longitude is inconsistent (moving one degree in one direction is not the same as moving one degree in the other).</p>
 <p>We give an example of projecting the polygon that defines the survey area (which as simply been read into R using <code>readShapeSpatial</code> from a shapefile produced by GIS).</p>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning: package 'sf' was built under R version 4.4.2</span></span></code></pre>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://had.co.nz/plyr" class="external-link">plyr</a></span><span class="op">)</span></span>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://had.co.nz/plyr" class="external-link">plyr</a></span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># tell R that the survey.area object is currently in lat/long</span></span>
 <span><span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/is.projected.html" class="external-link">proj4string</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/CRS-class.html" class="external-link">CRS</a></span><span class="op">(</span><span class="st">"+proj=longlat +datum=WGS84"</span><span class="op">)</span></span>
@@ -183,13 +182,13 @@ <h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class
 <span><span class="va">preddata_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">preddata_sf</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">coords_preddata</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_coordinates.html" class="external-link">st_coordinates</a></span><span class="op">(</span><span class="va">preddata_sf</span><span class="op">)</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">coords_preddata</span><span class="op">$</span><span class="va">X</span></span>
 <span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">y</span> <span class="op">&lt;-</span> <span class="va">coords_preddata</span><span class="op">$</span><span class="va">Y</span> </span></code></pre></div>
 <p>Next bit of code works with the <code>study.area</code> outline, massaging it for use with soap film smoother. If you will not be using soap film smoothers, the code is extraneous.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># proj 4 string</span></span>
 <span><span class="co"># using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/</span></span>
 <span><span class="va">lcc_proj4</span> <span class="op">&lt;-</span> <span class="fu">sp</span><span class="fu">::</span><span class="fu"><a href="https://edzer.github.io/sp/reference/CRS-class.html" class="external-link">CRS</a></span><span class="op">(</span><span class="st">"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs "</span><span class="op">)</span></span>
@@ -201,7 +200,7 @@ <h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class
 <span><span class="va">survey.area</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">@</span><span class="va">polygons</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">@</span><span class="va">Polygons</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">@</span><span class="va">coords</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>, <span class="st">"y"</span><span class="op">)</span></span></code></pre></div>
 <p>The below code generates this plot, which shows the survey area with the transect lines overlaid (using data from <code>segdata</code>).</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">segdata_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">segdata</span>, coords <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">segdata_sf</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_crs.html" class="external-link">st_crs</a></span><span class="op">(</span><span class="va">area.sf.proj</span><span class="op">)</span></span>
 <span><span class="co"># study area outline and segment centres</span></span>
@@ -214,7 +213,7 @@ <h3 id="shapefiles-and-converting-units">Shapefiles and converting units<a class
 <span><span class="va">plot_segments</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/areawithtransects-1.png"><!-- --></p>
 <p>Also note that since we have projected our prediction grid, the “squares” do not look quite like squares. So for plotting we will use the polygons that we have saved, these polygons (stored in <code>pred.polys</code>) are read from a shapefile created in GIS, the object itself is of class <code>SpatialPolygons</code> from the <code>sp</code> package. It needs project for proper plotting using functions in the <code>sf</code> package. predsf &lt;- st_as_sf(pred.polys)</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">predsf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_as_sf.html" class="external-link">st_as_sf</a></span><span class="op">(</span><span class="va">pred.polys</span><span class="op">)</span></span>
 <span><span class="co"># plot as projected</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_geometry.html" class="external-link">st_geometry</a></span><span class="op">(</span><span class="va">predsf</span><span class="op">)</span>, axes<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
@@ -230,7 +229,7 @@ <h3 id="distance-data">Distance data<a class="anchor" aria-label="anchor" href="
 </h3>
 <p>The top panels of the EDA plots below show histograms of observed distances and cluster size, while the bottom panels show the relationship between observed distance and observed cluster size, and the relationship between observed distance and Beaufort sea state. The plots show that there is some relationship between cluster size and observed distance (fewer smaller clusters seem to be seen at larger distances).</p>
 <p>The following code generates the EDA plots:</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># histograms</span></span>
@@ -255,14 +254,14 @@ <h3 id="distance-data">Distance data<a class="anchor" aria-label="anchor" href="
 <h3 id="spatial-data">Spatial data<a class="anchor" aria-label="anchor" href="#spatial-data"></a>
 </h3>
 <p>Looking separately at the spatial data without thinking about the distances, we can plot the observed group sizes in space (point size is proportional to the group size for each observation). Circle size indicates the size of the group in the observation. There are rather large areas with no observations, which might cause our variance estimates for abundance to be rather large. We also see the depth data which we will use depth later as an explanatory covariate in our spatial model.</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">prediction_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_make_grid.html" class="external-link">st_make_grid</a></span><span class="op">(</span><span class="va">area.sf.proj</span>, cellsize <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">9000</span>,<span class="fl">9000</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">prediction_grid_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/sf.html" class="external-link">st_sf</a></span><span class="op">(</span>geometry <span class="op">=</span> <span class="va">prediction_grid</span><span class="op">)</span></span>
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">prediction_grid_sf</span>, <span class="va">preddata_sf</span>, join <span class="op">=</span> <span class="va">st_nearest_feature</span><span class="op">)</span></span>
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">depth</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill<span class="op">=</span><span class="va">depth</span><span class="op">)</span>, color<span class="op">=</span><span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
 <span>         <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Spotted dolphins, Gulf of Mexico"</span>,</span>
@@ -279,10 +278,10 @@ <h2 id="estimating-the-detection-function">Estimating the detection function<a c
 </h2>
 <p>We use the <code>ds</code> function in the package <code>Distance</code> to fit the detection function. (The <code>Distance</code> package is intended to make standard distance sampling in <code>R</code> relatively straightforward. For a more flexible but more complex alternative, see the function <code>ddf</code> in the <code>mrds</code> library.)</p>
 <p>First, loading the <code>Distance</code> library:</p>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/DistanceDevelopment/Distance/" class="external-link">Distance</a></span><span class="op">)</span></span></code></pre></div>
 <p>We can then fit a detection function with hazard-rate key with no adjustment terms:</p>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">detfc.hr.null</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span><span class="op">)</span>, key<span class="op">=</span><span class="st">"hr"</span>, adjustment<span class="op">=</span><span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, :</span></span>
 <span><span class="co">## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible</span></span>
@@ -294,7 +293,7 @@ <h2 id="estimating-the-detection-function">Estimating the detection function<a c
 <span><span class="co">## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible</span></span>
 <span><span class="co">## problem in data (e.g., spike near zero distance).</span></span></code></pre>
 <p>Calling <code>summary</code> gives us information about parameter estimates, probability of detection, AIC, etc:</p>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">detfc.hr.null</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Summary for distance analysis </span></span>
@@ -318,7 +317,7 @@ <h2 id="estimating-the-detection-function">Estimating the detection function<a c
 <span><span class="co">## Average p            0.5912252  0.2224714 0.3762887</span></span>
 <span><span class="co">## N in covered region 79.4959326 30.8184416 0.3876732</span></span></code></pre>
 <p>The following code generates a plot of the fitted detection function (left) and quantile-quantile plot (right):</p>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">detfc.hr.null</span>, showpoints<span class="op">=</span><span class="cn">FALSE</span>, pl.den<span class="op">=</span><span class="fl">0</span>, lwd<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/Distance/man/gof_ds.html" class="external-link">gof_ds</a></span><span class="op">(</span><span class="va">detfc.hr.null</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
@@ -332,11 +331,11 @@ <h2 id="estimating-the-detection-function">Estimating the detection function<a c
 <h3 id="adding-covariates-to-the-detection-function">Adding covariates to the detection function<a class="anchor" aria-label="anchor" href="#adding-covariates-to-the-detection-function"></a>
 </h3>
 <p>It is common to include covariates in the detection function (so-called Multiple Covariate Distance Sampling or MCDS). In this dataset there are two covariates that were collected on each individual: Beaufort sea state and size. For brevity we fit only a hazard-rate detection functions with the sea state included as a factor covariate as follows:</p>
-<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">detfc.hr.beau</span><span class="op">&lt;-</span><span class="fu"><a href="https://rdrr.io/pkg/Distance/man/ds.html" class="external-link">ds</a></span><span class="op">(</span><span class="va">distdata</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">distdata</span><span class="op">$</span><span class="va">distance</span><span class="op">)</span>, formula<span class="op">=</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">beaufort</span><span class="op">)</span>,</span>
 <span>                  key<span class="op">=</span><span class="st">"hr"</span>, adjustment<span class="op">=</span><span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
 <p>Again looking at the <code>summary</code>,</p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">detfc.hr.beau</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Summary for distance analysis </span></span>
@@ -376,10 +375,10 @@ <h3 id="a-simple-model">A simple model<a class="anchor" aria-label="anchor" href
 </h3>
 <p>We begin with a very simple model. We assume that the number of individuals in each segment are quasi-Poisson distributed and that they are a smooth function of their spatial coordinates (note that the formula is exactly as one would specify to <code>gam</code> in <code>mgcv</code>). By setting <code>group=TRUE</code>, the abundance of clusters/groups rather than individuals can be estimated (though we ignore this here). Note we set <code>method="REML"</code> to ensure that smooth terms are estimated reliably.</p>
 <p>Running the model:</p>
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span></code></pre></div>
 <p>We can then obtain a summary of the fitted model:</p>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Family: quasipoisson </span></span>
@@ -405,7 +404,7 @@ <h3 id="a-simple-model">A simple model<a class="anchor" aria-label="anchor" href
 <p>The exact interpretation of the model summary results can be found in <span class="citation">(S. N. Wood, 2017)</span>; here we can see various information about the smooth components fitted and general model statistics.</p>
 <p>We can use the deviance explained to compare between models<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content='&lt;p&gt;Note though that the adjusted &lt;span class="math inline"&gt;\(R^2\)&lt;/span&gt; for the model is defined as the proportion of variance explained but the “original” variance used for comparison doesn’t include the offset (the area (or effective of the segments). It is therefore not recommended for one to directly interpret the &lt;span class="math inline"&gt;\(R^2\)&lt;/span&gt; value (see the &lt;code&gt;summary.gam&lt;/code&gt; manual page for further details).&lt;/p&gt;'><sup>2</sup></a>.</p>
 <p>We can also get a rough idea of what the smooth of space looks like using <code>vis.gam</code> (white/yellow indicates high values, red low indicates low values):</p>
-<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/vis.gam.html" class="external-link">vis.gam</a></span><span class="op">(</span><span class="va">dsm.xy</span>, plot.type<span class="op">=</span><span class="st">"contour"</span>, view<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span>, asp<span class="op">=</span><span class="fl">1</span>, type<span class="op">=</span><span class="st">"response"</span>, contour.col<span class="op">=</span><span class="st">"black"</span>, n.grid<span class="op">=</span><span class="fl">100</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/visgam1-1.png"><!-- --></p>
 <p>The <code>type="response"</code> argument ensures that the plot is on the scale of abundance but the values are relative (as the offsets are set to be their median values). This means that the plot is useful to get an idea of the general shape of the smooth but cannot be interpreted directly.</p>
@@ -414,7 +413,7 @@ <h3 id="a-simple-model">A simple model<a class="anchor" aria-label="anchor" href
 <h3 id="adding-another-environmental-covariate-to-the-spatial-model">Adding another environmental covariate to the spatial model<a class="anchor" aria-label="anchor" href="#adding-another-environmental-covariate-to-the-spatial-model"></a>
 </h3>
 <p>The data set also contains a <code>depth</code> covariate (which we plotted above). We can include in the model very simply:</p>
-<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.depth</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span>,k<span class="op">=</span><span class="fl">10</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">depth</span>,k<span class="op">=</span><span class="fl">20</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
@@ -442,7 +441,7 @@ <h3 id="adding-another-environmental-covariate-to-the-spatial-model">Adding anot
 <p>Here we see a drop in deviance explained, so perhaps this model is not as useful as the first. We discuss setting the <code>k</code> parameter in <a href="#model-checking">Model checking</a>, below.</p>
 <p>Setting <code>select=TRUE</code> here (as an argument to <code>gam</code>) would impose extra shrinkage terms on each smooth in the model (allowing smooth terms to be removed from the model during fitting; see <code><a href="https://rdrr.io/pkg/mgcv/man/gam.html" class="external-link">?gam</a></code> for more information). This is not particularly useful here, so we do not include it. However when there are many environmental predictors is in the model this can be a good way (along with looking at <span class="math inline">\(p\)</span>-values) to perform term selection.</p>
 <p>Simply calling <code>plot</code> on the model object allows us to look at the relationship between depth and the linear predictor:</p>
-<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, select<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm-xy-depth-depth-1.png"><!-- --></p>
 <p>Omitting the argument <code>select</code> in the call to <code>plot</code> will plot each of the smooth terms, one at a time.</p>
@@ -451,10 +450,10 @@ <h3 id="adding-another-environmental-covariate-to-the-spatial-model">Adding anot
 <h3 id="spatial-models-when-there-are-covariates-in-the-detection-function">Spatial models when there are covariates in the detection function<a class="anchor" aria-label="anchor" href="#spatial-models-when-there-are-covariates-in-the-detection-function"></a>
 </h3>
 <p>The code to fit the DSM when there are covariates in the detection function is similar to the other models, above. However since the detection function has observation-level covariates, we must estimate the abundance per segment using a Horvitz-Thompson-like estimator before modelling, so we change the response to be <code>abundance.est</code>:</p>
-<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.est.xy</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">abundance.est</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.beau</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span></code></pre></div>
 <p>As we can see, the <code>summary</code> results are rather similar:</p>
-<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.est.xy</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
 <span><span class="co">## Family: quasipoisson </span></span>
@@ -478,7 +477,7 @@ <h3 id="spatial-models-when-there-are-covariates-in-the-detection-function">Spat
 <span><span class="co">## R-sq.(adj) =  0.129   Deviance explained = 42.2%</span></span>
 <span><span class="co">## -REML = 1043.6  Scale est. = 174.1     n = 387</span></span></code></pre>
 <p>As is the resulting spatial smooth (though the resulting surface is somewhat “amplified”):</p>
-<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/vis.gam.html" class="external-link">vis.gam</a></span><span class="op">(</span><span class="va">dsm.est.xy</span>, plot.type<span class="op">=</span><span class="st">"contour"</span>, view<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x"</span>,<span class="st">"y"</span><span class="op">)</span>, asp<span class="op">=</span><span class="fl">1</span>, type<span class="op">=</span><span class="st">"response"</span>, zlim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">300</span><span class="op">)</span>, contour.col<span class="op">=</span><span class="st">"black"</span>, n.grid<span class="op">=</span><span class="fl">100</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/visgam5-1.png"><!-- --></p>
 </div>
@@ -491,7 +490,7 @@ <h3 id="other-response-distributions">Other response distributions<a class="anch
 <h4 id="tweedie">Tweedie<a class="anchor" aria-label="anchor" href="#tweedie"></a>
 </h4>
 <p>Response distributions other than the quasi-Poisson can be used, for example the Tweedie distribution. The Tweedie distribution is available in <code>dsm</code> by setting <code>family=tw()</code>.</p>
-<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.tweedie</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span>, <span class="va">detfc.hr.null</span>, <span class="va">segdata</span>, <span class="va">obsdata</span>, family<span class="op">=</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></span><span class="op">(</span><span class="op">)</span>, method<span class="op">=</span><span class="st">"REML"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## </span></span>
@@ -531,18 +530,18 @@ <h4 id="soap-film-smoothing">Soap film smoothing<a class="anchor" aria-label="an
 </h4>
 <p>To account for a complex region (e.g., a region that includes peninsulae) we can use the soap film smoother <span class="citation">(Simon N. Wood, Bravington, &amp; Hedley, 2008)</span>.</p>
 <p>To use a soap film smoother for the spatial part of the model we must create a set of knots for the smoother to use. This is easily done using the <code><a href="../../reference/make.soapgrid.html">make.soapgrid()</a></code> function in <code>dsm</code>:</p>
-<div class="sourceCode" id="cb49"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">soap.knots</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/make.soapgrid.html">make.soapgrid</a></span><span class="op">(</span><span class="va">survey.area</span>,<span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">15</span>,<span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>where the second argument specifies the number of points (in each direction) in the grid that will be used to create the knots (knots in the grid outside of <code>survey.area</code> are removed).</p>
 <p>As we saw in the exploratory analysis, some of the transect lines are outside of the survey area. These will cause the soap film smoother to fail, so we remove them:</p>
-<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb48"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">segdata</span><span class="op">$</span><span class="va">x</span>; <span class="va">y</span><span class="op">&lt;-</span><span class="va">segdata</span><span class="op">$</span><span class="va">y</span></span>
 <span><span class="va">onoff</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/inSide.html" class="external-link">inSide</a></span><span class="op">(</span>x<span class="op">=</span><span class="va">x</span>,y<span class="op">=</span><span class="va">y</span>, bnd<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">as.list</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/rm.html" class="external-link">rm</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span></span>
 <span><span class="va">segdata.soap</span> <span class="op">&lt;-</span> <span class="va">segdata</span><span class="op">[</span><span class="va">onoff</span>,<span class="op">]</span></span></code></pre></div>
 <p>Note that the <a href="https://github.com/dill/soap_checker" class="external-link"><code>soap_checker</code> script available here</a> can be useful in ensuring that the boundary, data and knots are in the correct format to use with the soap film smoother.</p>
 <p>We can run a model with both the <code>depth</code> covariate along with a spatial (soap film) smooth. Note that the <code>k</code> argument now refers to the complexity of the boundary smooth in the soap film, and the complexity of the film is controlled by the knots given in the <code>xt</code> argument.</p>
-<div class="sourceCode" id="cb51"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb49"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.tweedie.soap</span><span class="op">&lt;-</span><span class="fu"><a href="../../reference/dsm.html">dsm</a></span><span class="op">(</span><span class="va">count</span><span class="op">~</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, bs<span class="op">=</span><span class="st">"so"</span>, k<span class="op">=</span><span class="fl">15</span>, xt<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>bnd<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">survey.area</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>            <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">depth</span><span class="op">)</span>,</span>
 <span>          family<span class="op">=</span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/Tweedie.html" class="external-link">tw</a></span><span class="op">(</span><span class="op">)</span>, method<span class="op">=</span><span class="st">"REML"</span>,</span>
@@ -579,7 +578,7 @@ <h2 id="model-checking">Model checking<a class="anchor" aria-label="anchor" href
 </h2>
 <p>Fitting models is all well and good, but we’d like to confirm that the models we have are reasonable; <code>dsm</code> provides some functions for model checking.</p>
 <p>We can use <code>gam.check</code> to generate diagnostic plots:</p>
-<div class="sourceCode" id="cb53"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb51"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></span><span class="op">(</span><span class="va">dsm.xy</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy-check-1.png"><!-- --></p>
 <pre><code><span><span class="co">## </span></span>
@@ -601,7 +600,7 @@ <h2 id="model-checking">Model checking<a class="anchor" aria-label="anchor" href
 <p>By setting the <code>k</code> parameter we specify the largest complexity for that smooth term in the model; as long as this is high enough, we can be sure that there is enough flexibility. In the output from <code>gam.check</code> above, we can see that there is a “p-value” calculated for the size of the basis, this can be a good guide as to whether the basis size needs to be increased.</p>
 <p>The <code><a href="https://rdrr.io/pkg/mgcv/man/choose.k.html" class="external-link">?choose.k</a></code> manual page from <code>mgcv</code> gives further guidance and technical details on this matter.</p>
 <p>We can look at the same model form but with a Tweedie distribution specified as the response:</p>
-<div class="sourceCode" id="cb55"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb53"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/gam.check.html" class="external-link">gam.check</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy.tweedie-check-1.png"><!-- --></p>
 <pre><code><span><span class="co">## </span></span>
@@ -624,7 +623,7 @@ <h4 id="randomised-quantile-residuals">Randomised quantile residuals<a class="an
 </h4>
 <p>In the top right panel of the above <code>gam.check</code> plots the residuals vs. linear predictor plot includes a odd line of predictions. These are an artifact of the link function, showing the exact zeros in the data. These can be misleading and distracting, making it difficult to see whether residuals show heteroskedasticity.</p>
 <p>Randomised quantile residuals <span class="citation">(Dunn &amp; Smyth, 1996)</span> avoid this issue by transforming the residuals to be exactly normally distributed. This makes the residuals vs. linear predictor plot much easier to interpret as it therefore doesn’t include the artifacts generated by the link function. These plots can be produced using <code>rqgam.check</code> in <code>dsm</code>:</p>
-<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb55"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../../reference/rqgam_check.html">rqgam_check</a></span><span class="op">(</span><span class="va">dsm.xy.tweedie</span><span class="op">)</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy.tweedie-rqcheck-1.png"><!-- --></p>
 <p>Here we can see that there is no issue with heteroskedasticity (no increase in spread in the residuals vs. linear predictor plot with increasing values of the linear predictor). One can also plot these residuals against covariate values to check for pattern in the residuals.</p>
@@ -636,7 +635,7 @@ <h2 id="model-selection">Model selection<a class="anchor" aria-label="anchor" hr
 </h2>
 <p>Assuming that models have “passed” the checks in <code>gam.check</code>, <code>rqgam.check</code> and are sufficiently flexible, we may be left with a choice of which model is “best”. There are several methods for choosing the best model – AIC, REML/GCV scores, deviance explained, full cross-validation with test data and so on.</p>
 <p>Though this document does not intend to be a full analysis of the pantropical dolphin data, we can create a results table to compare the various models that have been fitted so far in terms of their abundance estimates and associated uncertainties.</p>
-<div class="sourceCode" id="cb58"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># make a data.frame to print out</span></span>
 <span><span class="va">mod_results</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="st">"Model name"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"`dsm.xy`"</span>, <span class="st">"`dsm.xy.depth`"</span>, <span class="st">"`dsm.xy.tweedie`"</span>, <span class="st">"`dsm.xy.tweedie.soap`"</span>,</span>
 <span>                                           <span class="st">"`dsm.est.xy`"</span><span class="op">)</span>,</span>
@@ -651,7 +650,7 @@ <h2 id="model-selection">Model selection<a class="anchor" aria-label="anchor" hr
 <span>                                                                      <span class="va">dsm.xy.tweedie.soap</span><span class="op">)</span>,</span>
 <span>                 <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">{</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">$</span><span class="va">dev.expl</span><span class="op">*</span><span class="fl">100</span>,<span class="fl">2</span><span class="op">)</span>,<span class="st">"%"</span><span class="op">)</span><span class="op">}</span><span class="op">)</span><span class="op">)</span>,<span class="cn">NA</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>We can then use the resulting <code>data.frame</code> to build a table of results using the <code>kable</code> function:</p>
-<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">kable</span><span class="op">(</span><span class="va">mod_results</span>, col.names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Model name"</span>, <span class="st">"Description"</span>, <span class="st">"Deviance explained"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
@@ -697,10 +696,10 @@ <h2 id="model-selection">Model selection<a class="anchor" aria-label="anchor" hr
 <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="anchor" href="#abundance-estimation"></a>
 </h2>
 <p>Once a model has been checked and selected, we can make predictions over the grid and calculate abundance. The offset is stored in the <code>area</code> column<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content='&lt;p&gt;An earlier version of this vignette incorrectly stated that the areas of the prediction cells were 444km&lt;span class="math inline"&gt;\(^2\)&lt;/span&gt;. This has been corrected. Thanks to Phil Bouchet for pointing this out.&lt;/p&gt;'><sup>3</sup></a>.</p>
-<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb58"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">dsm.xy</span>, <span class="va">preddata</span>, <span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></code></pre></div>
 <p>Given predicted abundance estimates for each cell in the prediction grid, these are connected to the prediction grid object for mapping:</p>
-<div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">prediction_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_make_grid.html" class="external-link">st_make_grid</a></span><span class="op">(</span><span class="va">area.sf.proj</span>, cellsize <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">9000</span>,<span class="fl">9000</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">prediction_grid_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/sf.html" class="external-link">st_sf</a></span><span class="op">(</span>geometry <span class="op">=</span> <span class="va">prediction_grid</span><span class="op">)</span></span>
 <span></span>
@@ -710,7 +709,7 @@ <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">Prediction_xy</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
@@ -720,11 +719,11 @@ <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="
 <span><span class="va">pred</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy-preds-1.png"><!-- --></p>
 <p>We can calculate abundance over the survey area by simply summing these predictions:</p>
-<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb62"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">dsm.xy.pred</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## [1] 28462.22</span></span></code></pre>
 <p>We can compare this with a plot of the predictions from this <code>dsm.xy.depth</code>:</p>
-<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.depth.pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, <span class="va">preddata</span>, <span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">Prediction_xy_depth</span> <span class="op">&lt;-</span> <span class="va">dsm.xy.depth.pred</span></span>
@@ -733,7 +732,7 @@ <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb68"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">Prediction_xy_depth</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
@@ -743,7 +742,7 @@ <h2 id="abundance-estimation">Abundance estimation<a class="anchor" aria-label="
 <span><span class="va">pred</span></span></code></pre></div>
 <p><img src="mexico-analysis_files/figure-html/dsm.xy.depth-preds-1.png"><!-- --></p>
 <p>We can see the inclusion of depth into the model has had a noticeable effect on the distribution (note the difference in legend scale between the two plots). We can again also look at the total abundance:</p>
-<div class="sourceCode" id="cb69"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb67"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">dsm.xy.depth.pred</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## [1] 27084.54</span></span></code></pre>
 <p>Here we see that there is not much of a change in the abundance, so in terms of abundance alone there isn’t much between the two models. Next we’ll go on to look at variance next where we can see bigger differences between the models.</p>
@@ -761,12 +760,12 @@ <h2 id="variance-estimation">Variance estimation<a class="anchor" aria-label="an
 </ul>
 <p><code>dsm_var_prop</code> can only be applied when there is a covariate in the detection function that varies at the level of the segments, and is recorded at each segment (for example Beaufort). We don’t have that situation here, so we opt for <code>dsm_var_gam</code>.</p>
 <p>Both methods estimate the variance of the abundance for each element in the list provided in <code>pred.data</code>. In our case we wish to obtain an abundance for each of the prediction cells, so we use <code>split</code> to chop our data set into list elements to give to <code>dsm_var_gam</code> (or <code>dsm_var_prop</code>).</p>
-<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb69"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">preddata.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/split.html" class="external-link">split</a></span><span class="op">(</span><span class="va">preddata</span>, <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">preddata</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">dsm.xy.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm_var_gam.html">dsm_var_gam</a></span><span class="op">(</span><span class="va">dsm.xy</span>, pred.data<span class="op">=</span><span class="va">preddata.var</span>,</span>
 <span>                          off.set<span class="op">=</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span></code></pre></div>
 <p>Calling <code>summary</code> will give some information about uncertainty estimation:</p>
-<div class="sourceCode" id="cb72"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb70"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.var</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Summary of uncertainty in a density surface model calculated</span></span>
 <span><span class="co">##  analytically for GAM, with delta method</span></span>
@@ -782,14 +781,14 @@ <h2 id="variance-estimation">Variance estimation<a class="anchor" aria-label="an
 <span><span class="co">## Total standard error           : 8433.356 </span></span>
 <span><span class="co">## Total coefficient of variation : 0.2966</span></span></code></pre>
 <p>We can also make a plot of the CVs (with transect lines and observations overlaid) using the following code.</p>
-<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb72"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">CV</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">dsm.xy.var</span><span class="op">$</span><span class="va">pred.var</span><span class="op">)</span><span class="op">/</span><span class="va">preddata_sf</span><span class="op">$</span><span class="va">Prediction_xy</span></span>
 <span></span>
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_join.html" class="external-link">st_join</a></span><span class="op">(</span><span class="va">prediction_grid_sf</span>, <span class="va">preddata_sf</span>, join <span class="op">=</span> <span class="va">st_nearest_feature</span><span class="op">)</span></span>
 <span><span class="va">cropped_grid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://r-spatial.github.io/sf/reference/geos_binary_ops.html" class="external-link">st_intersection</a></span><span class="op">(</span><span class="va">cropped_grid</span>, <span class="va">area.sf.proj</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning: attribute variables are assumed to be spatially constant throughout</span></span>
 <span><span class="co">## all geometries</span></span></code></pre>
-<div class="sourceCode" id="cb76"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">CV</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">cropped_grid</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">CV</span><span class="op">)</span>, color <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data<span class="op">=</span><span class="va">segdata_sf</span>, fill<span class="op">=</span><span class="cn">NA</span>, color<span class="op">=</span><span class="st">"white"</span>, linewidth<span class="op">=</span><span class="fl">.001</span><span class="op">)</span> <span class="op">+</span></span>
@@ -800,7 +799,7 @@ <h2 id="variance-estimation">Variance estimation<a class="anchor" aria-label="an
 <p><img src="mexico-analysis_files/figure-html/dsm.xyvarplot-1.png"><!-- --></p>
 <p>Note the increase in CV away from the transect lines.</p>
 <p>We can revisit the model that included both depth and location smooths and observe that the coefficient of variation for that model is larger than that of the model with only the location smooth.</p>
-<div class="sourceCode" id="cb77"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb75"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dsm.xy.depth.var</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dsm_var_gam.html">dsm_var_gam</a></span><span class="op">(</span><span class="va">dsm.xy.depth</span>, pred.data<span class="op">=</span><span class="va">preddata.var</span>,</span>
 <span>                                off.set<span class="op">=</span><span class="va">preddata</span><span class="op">$</span><span class="va">area</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dsm.xy.depth.var</span><span class="op">)</span></span></code></pre></div>
diff --git a/docs/authors.html b/docs/authors.html
index 02e04a0..c191345 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • dsm</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"></head><body>
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diff --git a/docs/deps/data-deps.txt b/docs/deps/data-deps.txt
index 8aa49e2..ca4dfd9 100644
--- a/docs/deps/data-deps.txt
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new file mode 100644
index 0000000..151dd57
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+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, 0));
+          transform: rotate(var(--fa-rotate-angle, 0)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
+.fa-paste::before {
+  content: "\f0ea"; }
+
+.fa-file-clipboard::before {
+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
+
+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
+.fa-blog::before {
+  content: "\f781"; }
+
+.fa-user-ninja::before {
+  content: "\f504"; }
+
+.fa-person-arrow-up-from-line::before {
+  content: "\e539"; }
+
+.fa-scroll-torah::before {
+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
+
+.fa-broom-ball::before {
+  content: "\f458"; }
+
+.fa-quidditch::before {
+  content: "\f458"; }
+
+.fa-quidditch-broom-ball::before {
+  content: "\f458"; }
+
+.fa-toggle-off::before {
+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
+.fa-arrow-down-9-1::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-desc::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-down-alt::before {
+  content: "\f886"; }
+
+.fa-face-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
+
+.fa-trowel-bricks::before {
+  content: "\e58a"; }
+
+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
+
+.fa-hospital-user::before {
+  content: "\f80d"; }
+
+.fa-tent-arrow-left-right::before {
+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
+.fa-square-caret-down::before {
+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
+
+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
+.fa-chevron-circle-down::before {
+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-table-cells-column-lock::before {
+  content: "\e678"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
+.fa-ad::before {
+  content: "\f641"; }
+
+.fa-circle-arrow-right::before {
+  content: "\f0a9"; }
+
+.fa-arrow-circle-right::before {
+  content: "\f0a9"; }
+
+.fa-filter-circle-xmark::before {
+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
+
+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
+
+.fa-list-numeric::before {
+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
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+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
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+  content: "\f15d"; }
+
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+  content: "\f15d"; }
+
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+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
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+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
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+  content: "\f0e2"; }
+
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+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
+.fa-level-up::before {
+  content: "\f148"; }
+
+.fa-u::before {
+  content: "\55"; }
+
+.fa-square-root-variable::before {
+  content: "\f698"; }
+
+.fa-square-root-alt::before {
+  content: "\f698"; }
+
+.fa-clock::before {
+  content: "\f017"; }
+
+.fa-clock-four::before {
+  content: "\f017"; }
+
+.fa-backward-step::before {
+  content: "\f048"; }
+
+.fa-step-backward::before {
+  content: "\f048"; }
+
+.fa-pallet::before {
+  content: "\f482"; }
+
+.fa-faucet::before {
+  content: "\e005"; }
+
+.fa-baseball-bat-ball::before {
+  content: "\f432"; }
+
+.fa-s::before {
+  content: "\53"; }
+
+.fa-timeline::before {
+  content: "\e29c"; }
+
+.fa-keyboard::before {
+  content: "\f11c"; }
+
+.fa-caret-down::before {
+  content: "\f0d7"; }
+
+.fa-house-chimney-medical::before {
+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
+.fa-mobile-android-alt::before {
+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
+
+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
+
+.fa-khanda::before {
+  content: "\f66d"; }
+
+.fa-sliders::before {
+  content: "\f1de"; }
+
+.fa-sliders-h::before {
+  content: "\f1de"; }
+
+.fa-folder-tree::before {
+  content: "\f802"; }
+
+.fa-network-wired::before {
+  content: "\f6ff"; }
+
+.fa-map-pin::before {
+  content: "\f276"; }
+
+.fa-hamsa::before {
+  content: "\f665"; }
+
+.fa-cent-sign::before {
+  content: "\e3f5"; }
+
+.fa-flask::before {
+  content: "\f0c3"; }
+
+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
+.fa-wand-sparkles::before {
+  content: "\f72b"; }
+
+.fa-ellipsis-vertical::before {
+  content: "\f142"; }
+
+.fa-ellipsis-v::before {
+  content: "\f142"; }
+
+.fa-ticket::before {
+  content: "\f145"; }
+
+.fa-power-off::before {
+  content: "\f011"; }
+
+.fa-right-long::before {
+  content: "\f30b"; }
+
+.fa-long-arrow-alt-right::before {
+  content: "\f30b"; }
+
+.fa-flag-usa::before {
+  content: "\f74d"; }
+
+.fa-laptop-file::before {
+  content: "\e51d"; }
+
+.fa-tty::before {
+  content: "\f1e4"; }
+
+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
+
+.fa-person-rifle::before {
+  content: "\e54e"; }
+
+.fa-house-medical-circle-exclamation::before {
+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
+
+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
+
+.fa-calculator::before {
+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
+
+.fa-n::before {
+  content: "\4e"; }
+
+.fa-cable-car::before {
+  content: "\f7da"; }
+
+.fa-tram::before {
+  content: "\f7da"; }
+
+.fa-cloud-rain::before {
+  content: "\f73d"; }
+
+.fa-building-circle-xmark::before {
+  content: "\e4d4"; }
+
+.fa-ship::before {
+  content: "\f21a"; }
+
+.fa-arrows-down-to-line::before {
+  content: "\e4b8"; }
+
+.fa-download::before {
+  content: "\f019"; }
+
+.fa-face-grin::before {
+  content: "\f580"; }
+
+.fa-grin::before {
+  content: "\f580"; }
+
+.fa-delete-left::before {
+  content: "\f55a"; }
+
+.fa-backspace::before {
+  content: "\f55a"; }
+
+.fa-eye-dropper::before {
+  content: "\f1fb"; }
+
+.fa-eye-dropper-empty::before {
+  content: "\f1fb"; }
+
+.fa-eyedropper::before {
+  content: "\f1fb"; }
+
+.fa-file-circle-check::before {
+  content: "\e5a0"; }
+
+.fa-forward::before {
+  content: "\f04e"; }
+
+.fa-mobile::before {
+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
+
+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
+.fa-align-center::before {
+  content: "\f037"; }
+
+.fa-book-skull::before {
+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
+.fa-id-card::before {
+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
+.fa-heart-circle-exclamation::before {
+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
+.fa-home::before {
+  content: "\f015"; }
+
+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
+
+.fa-file-medical::before {
+  content: "\f477"; }
+
+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
+
+.fa-arrow-right-arrow-left::before {
+  content: "\f0ec"; }
+
+.fa-exchange::before {
+  content: "\f0ec"; }
+
+.fa-rotate-right::before {
+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
+
+.fa-rotate-forward::before {
+  content: "\f2f9"; }
+
+.fa-utensils::before {
+  content: "\f2e7"; }
+
+.fa-cutlery::before {
+  content: "\f2e7"; }
+
+.fa-arrow-up-wide-short::before {
+  content: "\f161"; }
+
+.fa-sort-amount-up::before {
+  content: "\f161"; }
+
+.fa-mill-sign::before {
+  content: "\e1ed"; }
+
+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
+.fa-long-arrow-alt-up::before {
+  content: "\f30c"; }
+
+.fa-stop::before {
+  content: "\f04d"; }
+
+.fa-code-merge::before {
+  content: "\f387"; }
+
+.fa-upload::before {
+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
+
+.fa-mound::before {
+  content: "\e52d"; }
+
+.fa-toilet-portable::before {
+  content: "\e583"; }
+
+.fa-compact-disc::before {
+  content: "\f51f"; }
+
+.fa-file-arrow-down::before {
+  content: "\f56d"; }
+
+.fa-file-download::before {
+  content: "\f56d"; }
+
+.fa-caravan::before {
+  content: "\f8ff"; }
+
+.fa-shield-cat::before {
+  content: "\e572"; }
+
+.fa-bolt::before {
+  content: "\f0e7"; }
+
+.fa-zap::before {
+  content: "\f0e7"; }
+
+.fa-glass-water::before {
+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
+.fa-plane-circle-xmark::before {
+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
+
+.fa-yen::before {
+  content: "\f157"; }
+
+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
+.fa-sun::before {
+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
+.fa-face-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrow-alt-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrows-turn-to-dots::before {
+  content: "\e4c1"; }
+
+.fa-florin-sign::before {
+  content: "\e184"; }
+
+.fa-arrow-down-short-wide::before {
+  content: "\f884"; }
+
+.fa-sort-amount-desc::before {
+  content: "\f884"; }
+
+.fa-sort-amount-down-alt::before {
+  content: "\f884"; }
+
+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
+
+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
+
+.fa-user-lock::before {
+  content: "\f502"; }
+
+.fa-arrow-right-long::before {
+  content: "\f178"; }
+
+.fa-long-arrow-right::before {
+  content: "\f178"; }
+
+.fa-anchor-circle-xmark::before {
+  content: "\e4ac"; }
+
+.fa-ellipsis::before {
+  content: "\f141"; }
+
+.fa-ellipsis-h::before {
+  content: "\f141"; }
+
+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
+.fa-first-aid::before {
+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
+.fa-hands-holding-circle::before {
+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
+.fa-book-open-reader::before {
+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
+
+.fa-square-h::before {
+  content: "\f0fd"; }
+
+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
+.fa-map-marker-alt::before {
+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
+
+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
+.fa-droplet::before {
+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
+
+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
+
+.fa-stream::before {
+  content: "\f550"; }
+
+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
+
+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
+
+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-table-cells-row-lock::before {
+  content: "\e67a"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
+.fa-expand::before {
+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
+.fa-arrow-trend-up::before {
+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
+
+.fa-yelp:before {
+  content: "\f1e9"; }
+
+.fa-cc-visa:before {
+  content: "\f1f0"; }
+
+.fa-lastfm:before {
+  content: "\f202"; }
+
+.fa-shopware:before {
+  content: "\f5b5"; }
+
+.fa-creative-commons-nc:before {
+  content: "\f4e8"; }
+
+.fa-aws:before {
+  content: "\f375"; }
+
+.fa-redhat:before {
+  content: "\f7bc"; }
+
+.fa-yoast:before {
+  content: "\f2b1"; }
+
+.fa-cloudflare:before {
+  content: "\e07d"; }
+
+.fa-ups:before {
+  content: "\f7e0"; }
+
+.fa-pixiv:before {
+  content: "\e640"; }
+
+.fa-wpexplorer:before {
+  content: "\f2de"; }
+
+.fa-dyalog:before {
+  content: "\f399"; }
+
+.fa-bity:before {
+  content: "\f37a"; }
+
+.fa-stackpath:before {
+  content: "\f842"; }
+
+.fa-buysellads:before {
+  content: "\f20d"; }
+
+.fa-first-order:before {
+  content: "\f2b0"; }
+
+.fa-modx:before {
+  content: "\f285"; }
+
+.fa-guilded:before {
+  content: "\e07e"; }
+
+.fa-vnv:before {
+  content: "\f40b"; }
+
+.fa-square-js:before {
+  content: "\f3b9"; }
+
+.fa-js-square:before {
+  content: "\f3b9"; }
+
+.fa-microsoft:before {
+  content: "\f3ca"; }
+
+.fa-qq:before {
+  content: "\f1d6"; }
+
+.fa-orcid:before {
+  content: "\f8d2"; }
+
+.fa-java:before {
+  content: "\f4e4"; }
+
+.fa-invision:before {
+  content: "\f7b0"; }
+
+.fa-creative-commons-pd-alt:before {
+  content: "\f4ed"; }
+
+.fa-centercode:before {
+  content: "\f380"; }
+
+.fa-glide-g:before {
+  content: "\f2a6"; }
+
+.fa-drupal:before {
+  content: "\f1a9"; }
+
+.fa-jxl:before {
+  content: "\e67b"; }
+
+.fa-hire-a-helper:before {
+  content: "\f3b0"; }
+
+.fa-creative-commons-by:before {
+  content: "\f4e7"; }
+
+.fa-unity:before {
+  content: "\e049"; }
+
+.fa-whmcs:before {
+  content: "\f40d"; }
+
+.fa-rocketchat:before {
+  content: "\f3e8"; }
+
+.fa-vk:before {
+  content: "\f189"; }
+
+.fa-untappd:before {
+  content: "\f405"; }
+
+.fa-mailchimp:before {
+  content: "\f59e"; }
+
+.fa-css3-alt:before {
+  content: "\f38b"; }
+
+.fa-square-reddit:before {
+  content: "\f1a2"; }
+
+.fa-reddit-square:before {
+  content: "\f1a2"; }
+
+.fa-vimeo-v:before {
+  content: "\f27d"; }
+
+.fa-contao:before {
+  content: "\f26d"; }
+
+.fa-square-font-awesome:before {
+  content: "\e5ad"; }
+
+.fa-deskpro:before {
+  content: "\f38f"; }
+
+.fa-brave:before {
+  content: "\e63c"; }
+
+.fa-sistrix:before {
+  content: "\f3ee"; }
+
+.fa-square-instagram:before {
+  content: "\e055"; }
+
+.fa-instagram-square:before {
+  content: "\e055"; }
+
+.fa-battle-net:before {
+  content: "\f835"; }
+
+.fa-the-red-yeti:before {
+  content: "\f69d"; }
+
+.fa-square-hacker-news:before {
+  content: "\f3af"; }
+
+.fa-hacker-news-square:before {
+  content: "\f3af"; }
+
+.fa-edge:before {
+  content: "\f282"; }
+
+.fa-threads:before {
+  content: "\e618"; }
+
+.fa-napster:before {
+  content: "\f3d2"; }
+
+.fa-square-snapchat:before {
+  content: "\f2ad"; }
+
+.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa-google-plus-g:before {
+  content: "\f0d5"; }
+
+.fa-artstation:before {
+  content: "\f77a"; }
+
+.fa-markdown:before {
+  content: "\f60f"; }
+
+.fa-sourcetree:before {
+  content: "\f7d3"; }
+
+.fa-google-plus:before {
+  content: "\f2b3"; }
+
+.fa-diaspora:before {
+  content: "\f791"; }
+
+.fa-foursquare:before {
+  content: "\f180"; }
+
+.fa-stack-overflow:before {
+  content: "\f16c"; }
+
+.fa-github-alt:before {
+  content: "\f113"; }
+
+.fa-phoenix-squadron:before {
+  content: "\f511"; }
+
+.fa-pagelines:before {
+  content: "\f18c"; }
+
+.fa-algolia:before {
+  content: "\f36c"; }
+
+.fa-red-river:before {
+  content: "\f3e3"; }
+
+.fa-creative-commons-sa:before {
+  content: "\f4ef"; }
+
+.fa-safari:before {
+  content: "\f267"; }
+
+.fa-google:before {
+  content: "\f1a0"; }
+
+.fa-square-font-awesome-stroke:before {
+  content: "\f35c"; }
+
+.fa-font-awesome-alt:before {
+  content: "\f35c"; }
+
+.fa-atlassian:before {
+  content: "\f77b"; }
+
+.fa-linkedin-in:before {
+  content: "\f0e1"; }
+
+.fa-digital-ocean:before {
+  content: "\f391"; }
+
+.fa-nimblr:before {
+  content: "\f5a8"; }
+
+.fa-chromecast:before {
+  content: "\f838"; }
+
+.fa-evernote:before {
+  content: "\f839"; }
+
+.fa-hacker-news:before {
+  content: "\f1d4"; }
+
+.fa-creative-commons-sampling:before {
+  content: "\f4f0"; }
+
+.fa-adversal:before {
+  content: "\f36a"; }
+
+.fa-creative-commons:before {
+  content: "\f25e"; }
+
+.fa-watchman-monitoring:before {
+  content: "\e087"; }
+
+.fa-fonticons:before {
+  content: "\f280"; }
+
+.fa-weixin:before {
+  content: "\f1d7"; }
+
+.fa-shirtsinbulk:before {
+  content: "\f214"; }
+
+.fa-codepen:before {
+  content: "\f1cb"; }
+
+.fa-git-alt:before {
+  content: "\f841"; }
+
+.fa-lyft:before {
+  content: "\f3c3"; }
+
+.fa-rev:before {
+  content: "\f5b2"; }
+
+.fa-windows:before {
+  content: "\f17a"; }
+
+.fa-wizards-of-the-coast:before {
+  content: "\f730"; }
+
+.fa-square-viadeo:before {
+  content: "\f2aa"; }
+
+.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa-meetup:before {
+  content: "\f2e0"; }
+
+.fa-centos:before {
+  content: "\f789"; }
+
+.fa-adn:before {
+  content: "\f170"; }
+
+.fa-cloudsmith:before {
+  content: "\f384"; }
+
+.fa-opensuse:before {
+  content: "\e62b"; }
+
+.fa-pied-piper-alt:before {
+  content: "\f1a8"; }
+
+.fa-square-dribbble:before {
+  content: "\f397"; }
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+  content: "\f1d0"; }
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+.fa-css3:before {
+  content: "\f13c"; }
+
+.fa-staylinked:before {
+  content: "\f3f5"; }
+
+.fa-kaggle:before {
+  content: "\f5fa"; }
+
+.fa-space-awesome:before {
+  content: "\e5ac"; }
+
+.fa-deviantart:before {
+  content: "\f1bd"; }
+
+.fa-cpanel:before {
+  content: "\f388"; }
+
+.fa-goodreads-g:before {
+  content: "\f3a9"; }
+
+.fa-square-git:before {
+  content: "\f1d2"; }
+
+.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa-square-tumblr:before {
+  content: "\f174"; }
+
+.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa-trello:before {
+  content: "\f181"; }
+
+.fa-creative-commons-nc-jp:before {
+  content: "\f4ea"; }
+
+.fa-get-pocket:before {
+  content: "\f265"; }
+
+.fa-perbyte:before {
+  content: "\e083"; }
+
+.fa-grunt:before {
+  content: "\f3ad"; }
+
+.fa-weebly:before {
+  content: "\f5cc"; }
+
+.fa-connectdevelop:before {
+  content: "\f20e"; }
+
+.fa-leanpub:before {
+  content: "\f212"; }
+
+.fa-black-tie:before {
+  content: "\f27e"; }
+
+.fa-themeco:before {
+  content: "\f5c6"; }
+
+.fa-python:before {
+  content: "\f3e2"; }
+
+.fa-android:before {
+  content: "\f17b"; }
+
+.fa-bots:before {
+  content: "\e340"; }
+
+.fa-free-code-camp:before {
+  content: "\f2c5"; }
+
+.fa-hornbill:before {
+  content: "\f592"; }
+
+.fa-js:before {
+  content: "\f3b8"; }
+
+.fa-ideal:before {
+  content: "\e013"; }
+
+.fa-git:before {
+  content: "\f1d3"; }
+
+.fa-dev:before {
+  content: "\f6cc"; }
+
+.fa-sketch:before {
+  content: "\f7c6"; }
+
+.fa-yandex-international:before {
+  content: "\f414"; }
+
+.fa-cc-amex:before {
+  content: "\f1f3"; }
+
+.fa-uber:before {
+  content: "\f402"; }
+
+.fa-github:before {
+  content: "\f09b"; }
+
+.fa-php:before {
+  content: "\f457"; }
+
+.fa-alipay:before {
+  content: "\f642"; }
+
+.fa-youtube:before {
+  content: "\f167"; }
+
+.fa-skyatlas:before {
+  content: "\f216"; }
+
+.fa-firefox-browser:before {
+  content: "\e007"; }
+
+.fa-replyd:before {
+  content: "\f3e6"; }
+
+.fa-suse:before {
+  content: "\f7d6"; }
+
+.fa-jenkins:before {
+  content: "\f3b6"; }
+
+.fa-twitter:before {
+  content: "\f099"; }
+
+.fa-rockrms:before {
+  content: "\f3e9"; }
+
+.fa-pinterest:before {
+  content: "\f0d2"; }
+
+.fa-buffer:before {
+  content: "\f837"; }
+
+.fa-npm:before {
+  content: "\f3d4"; }
+
+.fa-yammer:before {
+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/docs/deps/font-awesome-6.5.2/css/all.min.css b/docs/deps/font-awesome-6.5.2/css/all.min.css
new file mode 100644
index 0000000..269bcee
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 Brands"}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em .15em)}.fa-pull-left{float:left;margin-right:var(--fa-pull-margin,.3em)}.fa-pull-right{float:right;margin-left:var(--fa-pull-margin,.3em)}.fa-beat{-webkit-animation-name:fa-beat;animation-name:fa-beat;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-bounce{-webkit-animation-name:fa-bounce;animation-name:fa-bounce;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1))}.fa-fade{-webkit-animation-name:fa-fade;animation-name:fa-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-beat-fade,.fa-fade{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s)}.fa-beat-fade{-webkit-animation-name:fa-beat-fade;animation-name:fa-beat-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-flip{-webkit-animation-name:fa-flip;animation-name:fa-flip;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-shake{-webkit-animation-name:fa-shake;animation-name:fa-shake;-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-shake,.fa-spin{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal)}.fa-spin{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-duration:var(--fa-animation-duration,2s);animation-duration:var(--fa-animation-duration,2s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-spin-reverse{--fa-animation-direction:reverse}.fa-pulse,.fa-spin-pulse{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,steps(8));animation-timing-function:var(--fa-animation-timing,steps(8))}@media (prefers-reduced-motion:reduce){.fa-beat,.fa-beat-fade,.fa-bounce,.fa-fade,.fa-flip,.fa-pulse,.fa-shake,.fa-spin,.fa-spin-pulse{-webkit-animation-delay:-1ms;animation-delay:-1ms;-webkit-animation-duration:1ms;animation-duration:1ms;-webkit-animation-iteration-count:1;animation-iteration-count:1;-webkit-transition-delay:0s;transition-delay:0s;-webkit-transition-duration:0s;transition-duration:0s}}@-webkit-keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@-webkit-keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) 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translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) 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diff --git a/docs/deps/font-awesome-6.5.2/css/v4-shims.css b/docs/deps/font-awesome-6.5.2/css/v4-shims.css
new file mode 100644
index 0000000..ea60ea4
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
+.fa.fa-envelope-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
+.fa.fa-close:before {
+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
+
+.fa.fa-file-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-o:before {
+  content: "\f15b"; }
+
+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
+
+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
+.fa.fa-refresh:before {
+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
+
+.fa.fa-video-camera:before {
+  content: "\f03d"; }
+
+.fa.fa-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-picture-o:before {
+  content: "\f03e"; }
+
+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-photo:before {
+  content: "\f03e"; }
+
+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-image:before {
+  content: "\f03e"; }
+
+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
+
+.fa.fa-pencil-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pencil-square-o:before {
+  content: "\f044"; }
+
+.fa.fa-edit {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-edit:before {
+  content: "\f044"; }
+
+.fa.fa-share-square-o:before {
+  content: "\f14d"; }
+
+.fa.fa-check-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
+
+.fa.fa-arrows:before {
+  content: "\f0b2"; }
+
+.fa.fa-times-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-circle-o:before {
+  content: "\f057"; }
+
+.fa.fa-check-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-check-circle-o:before {
+  content: "\f058"; }
+
+.fa.fa-mail-forward:before {
+  content: "\f064"; }
+
+.fa.fa-expand:before {
+  content: "\f424"; }
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+.fa.fa-compress:before {
+  content: "\f422"; }
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+.fa.fa-eye {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-eye-slash {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-warning:before {
+  content: "\f071"; }
+
+.fa.fa-calendar:before {
+  content: "\f073"; }
+
+.fa.fa-arrows-v:before {
+  content: "\f338"; }
+
+.fa.fa-arrows-h:before {
+  content: "\f337"; }
+
+.fa.fa-bar-chart:before {
+  content: "\e0e3"; }
+
+.fa.fa-bar-chart-o:before {
+  content: "\e0e3"; }
+
+.fa.fa-twitter-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-twitter-square:before {
+  content: "\f081"; }
+
+.fa.fa-facebook-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook-square:before {
+  content: "\f082"; }
+
+.fa.fa-gears:before {
+  content: "\f085"; }
+
+.fa.fa-thumbs-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-thumbs-o-up:before {
+  content: "\f164"; }
+
+.fa.fa-thumbs-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-thumbs-o-down:before {
+  content: "\f165"; }
+
+.fa.fa-heart-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-heart-o:before {
+  content: "\f004"; }
+
+.fa.fa-sign-out:before {
+  content: "\f2f5"; }
+
+.fa.fa-linkedin-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-linkedin-square:before {
+  content: "\f08c"; }
+
+.fa.fa-thumb-tack:before {
+  content: "\f08d"; }
+
+.fa.fa-external-link:before {
+  content: "\f35d"; }
+
+.fa.fa-sign-in:before {
+  content: "\f2f6"; }
+
+.fa.fa-github-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-github-square:before {
+  content: "\f092"; }
+
+.fa.fa-lemon-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-lemon-o:before {
+  content: "\f094"; }
+
+.fa.fa-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-square-o:before {
+  content: "\f0c8"; }
+
+.fa.fa-bookmark-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-bookmark-o:before {
+  content: "\f02e"; }
+
+.fa.fa-twitter {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook:before {
+  content: "\f39e"; }
+
+.fa.fa-facebook-f {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook-f:before {
+  content: "\f39e"; }
+
+.fa.fa-github {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-credit-card {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-feed:before {
+  content: "\f09e"; }
+
+.fa.fa-hdd-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hdd-o:before {
+  content: "\f0a0"; }
+
+.fa.fa-hand-o-right {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-o-right:before {
+  content: "\f0a4"; }
+
+.fa.fa-hand-o-left {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-o-left:before {
+  content: "\f0a5"; }
+
+.fa.fa-hand-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-o-up:before {
+  content: "\f0a6"; }
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+.fa.fa-hand-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-o-down:before {
+  content: "\f0a7"; }
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+.fa.fa-globe:before {
+  content: "\f57d"; }
+
+.fa.fa-tasks:before {
+  content: "\f828"; }
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+.fa.fa-arrows-alt:before {
+  content: "\f31e"; }
+
+.fa.fa-group:before {
+  content: "\f0c0"; }
+
+.fa.fa-chain:before {
+  content: "\f0c1"; }
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+.fa.fa-cut:before {
+  content: "\f0c4"; }
+
+.fa.fa-files-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-files-o:before {
+  content: "\f0c5"; }
+
+.fa.fa-floppy-o {
+  font-family: 'Font Awesome 6 Free';
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css b/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css
new file mode 100644
index 0000000..09baf5f
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
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-<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
-<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Density Surface Modelling of Distance Sampling Data">
 <meta name="description" content="Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.">
 <meta property="og:description" content="Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.">
+<script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script>
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
@@ -61,13 +62,21 @@ <h2 id="dsm---density-surface-modelling">
 <div class="page-header"><h1 id="what-is-dsm">What is <code>dsm</code>?<a class="anchor" aria-label="anchor" href="#what-is-dsm"></a>
 </h1></div>
 <p><code>dsm</code> provides a Generalized Additive Model (GAM)-based approach to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.</p>
-<p>Open Access paper describing the methods: <a href="https://doi.org/10.1111/2041-210X.12105" class="external-link">Miller, DL, ML Burt, EA Rexstad and L Thomas (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution.</a></p>
+<p>Open Access paper describing the methods:</p>
+<ul>
+<li>Miller, DL, ML Burt, EA Rexstad and L Thomas (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution. <a href="https://doi.org/10.1111/2041-210X.12105" class="external-link">https://doi.org/10.1111/2041-210X.12105</a>
+</li>
+</ul>
 </div>
 <div class="section level1">
 <h1 id="using-dsm">Using <code>dsm</code>
 <a class="anchor" aria-label="anchor" href="#using-dsm"></a>
 </h1>
-<p>Examples of how to use <code>dsm</code> and other distance sampling R packages are available at <a href="http://examples.distancesampling.org" class="external-link">examples.distancesampling.org</a>.</p>
+<p>Examples of how to use <code>dsm</code> see</p>
+<ul>
+<li><a href="https://distancesampling.org/dsm/articles/data_format/dsm-data-formatting.html" class="external-link uri">https://distancesampling.org/dsm/articles/data_format/dsm-data-formatting.html</a></li>
+<li><a href="https://distancesampling.org/dsm/articles/lines_gomex/mexico-analysis.html" class="external-link uri">https://distancesampling.org/dsm/articles/lines_gomex/mexico-analysis.html</a></li>
+</ul>
 </div>
 
   </main><aside class="col-md-3"><div class="links">
diff --git a/docs/news/index.html b/docs/news/index.html
index 28deef2..5d7faa0 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
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diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 3b8b19b..1e69818 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,7 +1,7 @@
-pandoc: '3.2'
+pandoc: '3.5'
 pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
   data_format/dsm-data-formatting: data_format/dsm-data-formatting.html
   lines_gomex/mexico-analysis: lines_gomex/mexico-analysis.html
-last_built: 2024-11-13T16:30Z
+last_built: 2024-11-18T17:01Z
diff --git a/docs/reference/block.info.per.su.html b/docs/reference/block.info.per.su.html
index 9aec416..7caf892 100644
--- a/docs/reference/block.info.per.su.html
+++ b/docs/reference/block.info.per.su.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Find the block information — block.info.per.su • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Find the block information — block.info.per.su"><meta name="description" content="Takes the transect data and works out how many blocks of a given
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Find the block information — block.info.per.su • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Find the block information — block.info.per.su"><meta name="description" content="Takes the transect data and works out how many blocks of a given
 size (in segment terms) fit into each."><meta property="og:description" content="Takes the transect data and works out how many blocks of a given
-size (in segment terms) fit into each."></head><body>
+size (in segment terms) fit into each."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/check.cols.html b/docs/reference/check.cols.html
index 906dd36..6e9be7e 100644
--- a/docs/reference/check.cols.html
+++ b/docs/reference/check.cols.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check column names exist — check.cols • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check column names exist — check.cols"><meta name="description" content="Internal function to check that supplied data.frames have the correct
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check column names exist — check.cols • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check column names exist — check.cols"><meta name="description" content="Internal function to check that supplied data.frames have the correct
 columns and checks that sample labels are all unique."><meta property="og:description" content="Internal function to check that supplied data.frames have the correct
-columns and checks that sample labels are all unique."></head><body>
+columns and checks that sample labels are all unique."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm-data.html b/docs/reference/dsm-data.html
index f4207dc..843467e 100644
--- a/docs/reference/dsm-data.html
+++ b/docs/reference/dsm-data.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Data format for DSM — dsm-data • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data format for DSM — dsm-data"><meta name="description" content="Two data.frames must be provided to dsm. They are referred to as
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Data format for DSM — dsm-data • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data format for DSM — dsm-data"><meta name="description" content="Two data.frames must be provided to dsm. They are referred to as
 observation.data and segment.data."><meta property="og:description" content="Two data.frames must be provided to dsm. They are referred to as
-observation.data and segment.data."></head><body>
+observation.data and segment.data."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm-package.html b/docs/reference/dsm-package.html
index 057a9a2..c6ee3fb 100644
--- a/docs/reference/dsm-package.html
+++ b/docs/reference/dsm-package.html
@@ -1,11 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Density surface modelling — dsm-package • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Density surface modelling — dsm-package"><meta name="description" content="dsm implements spatial models for distance sampling data. Models for
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Density surface modelling — dsm-package • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Density surface modelling — dsm-package"><meta name="description" content="dsm implements spatial models for distance sampling data. Models for
 detectability can be fitted using packages mrds or Distance. dsm fits
 generalized additive models to spatially-referenced data. See Miller et al
 (2013) for an introduction."><meta property="og:description" content="dsm implements spatial models for distance sampling data. Models for
 detectability can be fitted using packages mrds or Distance. dsm fits
 generalized additive models to spatially-referenced data. See Miller et al
-(2013) for an introduction."></head><body>
+(2013) for an introduction."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm.cor.html b/docs/reference/dsm.cor.html
index f57f32d..ac13bdd 100644
--- a/docs/reference/dsm.cor.html
+++ b/docs/reference/dsm.cor.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check for autocorrelation in residuals — dsm.cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm.cor"><meta name="description" content="This function is deprecated, use dsm_cor."><meta property="og:description" content="This function is deprecated, use dsm_cor."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check for autocorrelation in residuals — dsm.cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm.cor"><meta name="description" content="This function is deprecated, use dsm_cor."><meta property="og:description" content="This function is deprecated, use dsm_cor."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm.html b/docs/reference/dsm.html
index 57ad4d6..56cd193 100644
--- a/docs/reference/dsm.html
+++ b/docs/reference/dsm.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fit a density surface model to segment-specific estimates of abundance or density. — dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit a density surface model to segment-specific estimates of abundance or density. — dsm"><meta name="description" content="Fits a density surface model (DSM) to detection adjusted counts from a
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fit a density surface model to segment-specific estimates of abundance or density. — dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit a density surface model to segment-specific estimates of abundance or density. — dsm"><meta name="description" content="Fits a density surface model (DSM) to detection adjusted counts from a
 spatially-referenced distance sampling analysis. dsm takes observations of
 animals, allocates them to segments of line (or strip transects) and
 optionally adjusts the counts based on detectability using a supplied
@@ -11,7 +11,7 @@
 optionally adjusts the counts based on detectability using a supplied
 detection function model. A generalized additive model, generalized mixed
 model or generalized linear model is then used to model these adjusted
-counts based on a formula involving environmental covariates."></head><body>
+counts based on a formula involving environmental covariates."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm.var.gam.html b/docs/reference/dsm.var.gam.html
index d5f916d..b09c3b6 100644
--- a/docs/reference/dsm.var.gam.html
+++ b/docs/reference/dsm.var.gam.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance estimation assuming independence — dsm.var.gam • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance estimation assuming independence — dsm.var.gam"><meta name="description" content="This function is deprecated, use dsm_var_gam."><meta property="og:description" content="This function is deprecated, use dsm_var_gam."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance estimation assuming independence — dsm.var.gam • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance estimation assuming independence — dsm.var.gam"><meta name="description" content="This function is deprecated, use dsm_var_gam."><meta property="og:description" content="This function is deprecated, use dsm_var_gam."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm.var.movblk.html b/docs/reference/dsm.var.movblk.html
index 767ade9..fdc023a 100644
--- a/docs/reference/dsm.var.movblk.html
+++ b/docs/reference/dsm.var.movblk.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance estimation via parametric moving block bootstrap — dsm.var.movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm.var.movblk"><meta name="description" content="This function is deprecated, use dsm_var_movblk."><meta property="og:description" content="This function is deprecated, use dsm_var_movblk."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance estimation via parametric moving block bootstrap — dsm.var.movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm.var.movblk"><meta name="description" content="This function is deprecated, use dsm_var_movblk."><meta property="og:description" content="This function is deprecated, use dsm_var_movblk."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm.var.prop.html b/docs/reference/dsm.var.prop.html
index d7733a5..cf64c59 100644
--- a/docs/reference/dsm.var.prop.html
+++ b/docs/reference/dsm.var.prop.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance propagation for DSMs — dsm.var.prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm.var.prop"><meta name="description" content="This function is deprecated, use dsm_var_prop."><meta property="og:description" content="This function is deprecated, use dsm_var_prop."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance propagation for DSMs — dsm.var.prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm.var.prop"><meta name="description" content="This function is deprecated, use dsm_var_prop."><meta property="og:description" content="This function is deprecated, use dsm_var_prop."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm_cor.html b/docs/reference/dsm_cor.html
index d2c8741..a689d9e 100644
--- a/docs/reference/dsm_cor.html
+++ b/docs/reference/dsm_cor.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check for autocorrelation in residuals — dsm_cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm_cor"><meta name="description" content="Once a DSM has been fitted to data, this function can be used to check for
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check for autocorrelation in residuals — dsm_cor • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for autocorrelation in residuals — dsm_cor"><meta name="description" content="Once a DSM has been fitted to data, this function can be used to check for
 autocorrelation in the residuals."><meta property="og:description" content="Once a DSM has been fitted to data, this function can be used to check for
-autocorrelation in the residuals."></head><body>
+autocorrelation in the residuals."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm_var_gam.html b/docs/reference/dsm_var_gam.html
index 3697fd7..b7c4e3a 100644
--- a/docs/reference/dsm_var_gam.html
+++ b/docs/reference/dsm_var_gam.html
@@ -1,11 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance estimation assuming independence — dsm_var_gam • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance estimation assuming independence — dsm_var_gam"><meta name="description" content="If one is willing to assume the the detection function and spatial model are
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance estimation assuming independence — dsm_var_gam • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance estimation assuming independence — dsm_var_gam"><meta name="description" content="If one is willing to assume the the detection function and spatial model are
 independent, this function will produce estimates of variance of predictions
 of abundance, using the result that squared coefficients of variation will
 add."><meta property="og:description" content="If one is willing to assume the the detection function and spatial model are
 independent, this function will produce estimates of variance of predictions
 of abundance, using the result that squared coefficients of variation will
-add."></head><body>
+add."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm_var_movblk.html b/docs/reference/dsm_var_movblk.html
index 58e508d..f1dafa9 100644
--- a/docs/reference/dsm_var_movblk.html
+++ b/docs/reference/dsm_var_movblk.html
@@ -1,9 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance estimation via parametric moving block bootstrap — dsm_var_movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm_var_movblk"><meta name="description" content="Estimate the variance in abundance over an area using a moving block
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance estimation via parametric moving block bootstrap — dsm_var_movblk • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance estimation via parametric moving block bootstrap — dsm_var_movblk"><meta name="description" content="Estimate the variance in abundance over an area using a moving block
 bootstrap. Two procedures are implemented, one incorporating detection
 function uncertainty, one not."><meta property="og:description" content="Estimate the variance in abundance over an area using a moving block
 bootstrap. Two procedures are implemented, one incorporating detection
-function uncertainty, one not."></head><body>
+function uncertainty, one not."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm_var_prop.html b/docs/reference/dsm_var_prop.html
index e1af30d..3474e19 100644
--- a/docs/reference/dsm_var_prop.html
+++ b/docs/reference/dsm_var_prop.html
@@ -1,11 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance propagation for DSMs — dsm_var_prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm_var_prop"><meta name="description" content="To ensure that uncertainty from the detection function is correctly
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction variance propagation for DSMs — dsm_var_prop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction variance propagation for DSMs — dsm_var_prop"><meta name="description" content="To ensure that uncertainty from the detection function is correctly
 propagated to the final variance estimate of abundance, this function uses a
 method first detailed in Williams et al (2011), further explanation is given
 in Bravington et al. (2021)."><meta property="og:description" content="To ensure that uncertainty from the detection function is correctly
 propagated to the final variance estimate of abundance, this function uses a
 method first detailed in Williams et al (2011), further explanation is given
-in Bravington et al. (2021)."></head><body>
+in Bravington et al. (2021)."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dsm_varprop.html b/docs/reference/dsm_varprop.html
index 8a9b28d..af7019f 100644
--- a/docs/reference/dsm_varprop.html
+++ b/docs/reference/dsm_varprop.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance propagation for density surface models — dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance propagation for density surface models — dsm_varprop"><meta name="description" content="Calculate the uncertainty in predictions from a fitted DSM, including
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Variance propagation for density surface models — dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Variance propagation for density surface models — dsm_varprop"><meta name="description" content="Calculate the uncertainty in predictions from a fitted DSM, including
 uncertainty from the detection function."><meta property="og:description" content="Calculate the uncertainty in predictions from a fitted DSM, including
-uncertainty from the detection function."></head><body>
+uncertainty from the detection function."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/dummy_ddf.html b/docs/reference/dummy_ddf.html
index 60bf4b2..52e161d 100644
--- a/docs/reference/dummy_ddf.html
+++ b/docs/reference/dummy_ddf.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Detection function objects when detection is certain — dummy_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Detection function objects when detection is certain — dummy_ddf"><meta name="description" content="Create a detection function object for strip/plot surveys for use in density
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Detection function objects when detection is certain — dummy_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Detection function objects when detection is certain — dummy_ddf"><meta name="description" content="Create a detection function object for strip/plot surveys for use in density
 surface models."><meta property="og:description" content="Create a detection function object for strip/plot surveys for use in density
-surface models."></head><body>
+surface models."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/generate.ds.uncertainty.html b/docs/reference/generate.ds.uncertainty.html
index 51e01b5..3e2e8ee 100644
--- a/docs/reference/generate.ds.uncertainty.html
+++ b/docs/reference/generate.ds.uncertainty.html
@@ -1,9 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generate data from a fitted detection function — generate.ds.uncertainty • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generate data from a fitted detection function — generate.ds.uncertainty"><meta name="description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generate data from a fitted detection function — generate.ds.uncertainty • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generate data from a fitted detection function — generate.ds.uncertainty"><meta name="description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
 procedure generates data from the fitted detection function (assuming that
 it is correct)."><meta property="og:description" content="When using dsm.var.movblk if ds.uncertainty=TRUE, this
 procedure generates data from the fitted detection function (assuming that
-it is correct)."></head><body>
+it is correct)."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/generate.mb.sample.html b/docs/reference/generate.mb.sample.html
index 85c15ab..56ec98f 100644
--- a/docs/reference/generate.mb.sample.html
+++ b/docs/reference/generate.mb.sample.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Moving block bootstrap sampler — generate.mb.sample • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Moving block bootstrap sampler — generate.mb.sample"><meta name="description" content="Not usually used on its own, called from within
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Moving block bootstrap sampler — generate.mb.sample • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Moving block bootstrap sampler — generate.mb.sample"><meta name="description" content="Not usually used on its own, called from within
 dsm.var.movblk."><meta property="og:description" content="Not usually used on its own, called from within
-dsm.var.movblk."></head><body>
+dsm.var.movblk."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 0285be9..64ca626 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Package index • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Package index"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Package index • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Package index"><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/latlong2km.html b/docs/reference/latlong2km.html
index b471995..c9ef36a 100644
--- a/docs/reference/latlong2km.html
+++ b/docs/reference/latlong2km.html
@@ -1,9 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Convert latitude and longitude to Northings and Eastings — latlong2km • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert latitude and longitude to Northings and Eastings — latlong2km"><meta name="description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Convert latitude and longitude to Northings and Eastings — latlong2km • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert latitude and longitude to Northings and Eastings — latlong2km"><meta name="description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
 south-north from axes through (lon0,lat0) using the
 "spherical law of cosines".'><meta property="og:description" content='Convert longitude and latitude co-ordinates to kilometres west-east and
 south-north from axes through (lon0,lat0) using the
-"spherical law of cosines".'></head><body>
+"spherical law of cosines".'><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/make.soapgrid.html b/docs/reference/make.soapgrid.html
index 7d443df..b6d206a 100644
--- a/docs/reference/make.soapgrid.html
+++ b/docs/reference/make.soapgrid.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create a knot grid for the internal part of a soap film smoother. — make.soapgrid • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a knot grid for the internal part of a soap film smoother. — make.soapgrid"><meta name="description" content='This routine simply creates a grid of knots (in the correct format) to be
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create a knot grid for the internal part of a soap film smoother. — make.soapgrid • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a knot grid for the internal part of a soap film smoother. — make.soapgrid"><meta name="description" content='This routine simply creates a grid of knots (in the correct format) to be
 used as in the "internal" part of the soap film smoother'><meta property="og:description" content='This routine simply creates a grid of knots (in the correct format) to be
-used as in the "internal" part of the soap film smoother'></head><body>
+used as in the "internal" part of the soap film smoother'><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/mexdolphins.html b/docs/reference/mexdolphins.html
index d3137f8..15fead9 100644
--- a/docs/reference/mexdolphins.html
+++ b/docs/reference/mexdolphins.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins"><meta name="description" content="Data from a combination of several NOAA shipboard surveys conducted on
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins"><meta name="description" content="Data from a combination of several NOAA shipboard surveys conducted on
 pan-tropical spotted dolphins in the Gulf of Mexico. The data consist of 47
 observations of groups of dolphins. The group size was recorded, as well as
 the Beaufort sea state at the time of the observation. Coordinates for each
@@ -11,7 +11,7 @@
 the Beaufort sea state at the time of the observation. Coordinates for each
 observation and bathymetry data were also available as covariates for the
 analysis. A complete example analysis (and description of the data) is
-provided at http://distancesampling.org/R/vignettes/mexico-analysis.html."></head><body>
+provided at http://distancesampling.org/R/vignettes/mexico-analysis.html."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/obs_exp.html b/docs/reference/obs_exp.html
index d0b80c0..4c32399 100644
--- a/docs/reference/obs_exp.html
+++ b/docs/reference/obs_exp.html
@@ -1,9 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Observed versus expected diagnostics for fitted DSMs — obs_exp • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Observed versus expected diagnostics for fitted DSMs — obs_exp"><meta name="description" content="Given a covariate, calculate the observed and expected counts for each
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Observed versus expected diagnostics for fitted DSMs — obs_exp • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Observed versus expected diagnostics for fitted DSMs — obs_exp"><meta name="description" content="Given a covariate, calculate the observed and expected counts for each
 unique value of the covariate. This can be a useful goodness of fit check
 for DSMs."><meta property="og:description" content="Given a covariate, calculate the observed and expected counts for each
 unique value of the covariate. This can be a useful goodness of fit check
-for DSMs."></head><body>
+for DSMs."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/plot.dsm.html b/docs/reference/plot.dsm.html
index efb5e8a..363fc0e 100644
--- a/docs/reference/plot.dsm.html
+++ b/docs/reference/plot.dsm.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot a density surface model. — plot.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot a density surface model. — plot.dsm"><meta name="description" content="See plot.gam."><meta property="og:description" content="See plot.gam."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot a density surface model. — plot.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot a density surface model. — plot.dsm"><meta name="description" content="See plot.gam."><meta property="og:description" content="See plot.gam."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/plot.dsm.var.html b/docs/reference/plot.dsm.var.html
index 82b6ad5..25d746c 100644
--- a/docs/reference/plot.dsm.var.html
+++ b/docs/reference/plot.dsm.var.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create plots of abundance uncertainty — plot.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create plots of abundance uncertainty — plot.dsm.var"><meta name="description" content="Note that the prediction data set must have x and y columns even if
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create plots of abundance uncertainty — plot.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create plots of abundance uncertainty — plot.dsm.var"><meta name="description" content="Note that the prediction data set must have x and y columns even if
 these were not used in the model."><meta property="og:description" content="Note that the prediction data set must have x and y columns even if
-these were not used in the model."></head><body>
+these were not used in the model."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/plot_pred_by_term.html b/docs/reference/plot_pred_by_term.html
index c5a4f46..466b738 100644
--- a/docs/reference/plot_pred_by_term.html
+++ b/docs/reference/plot_pred_by_term.html
@@ -1,9 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Spatially plot predictions per model term — plot_pred_by_term • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Spatially plot predictions per model term — plot_pred_by_term"><meta name="description" content="Plot the effect of each smooth in the model spatially. For each term in the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Spatially plot predictions per model term — plot_pred_by_term • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Spatially plot predictions per model term — plot_pred_by_term"><meta name="description" content="Plot the effect of each smooth in the model spatially. For each term in the
 model, plot its effect in space. Plots are made on the same scale, so that
 the relative influence of each smooth can be seen."><meta property="og:description" content="Plot the effect of each smooth in the model spatially. For each term in the
 model, plot its effect in space. Plots are made on the same scale, so that
-the relative influence of each smooth can be seen."></head><body>
+the relative influence of each smooth can be seen."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/predict.dsm.html b/docs/reference/predict.dsm.html
index dd0b705..a7d0426 100644
--- a/docs/reference/predict.dsm.html
+++ b/docs/reference/predict.dsm.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Predict from a fitted density surface model — predict.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Predict from a fitted density surface model — predict.dsm"><meta name="description" content="Make predictions of density or abundance outside (or inside) the covered
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Predict from a fitted density surface model — predict.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Predict from a fitted density surface model — predict.dsm"><meta name="description" content="Make predictions of density or abundance outside (or inside) the covered
 area."><meta property="og:description" content="Make predictions of density or abundance outside (or inside) the covered
-area."></head><body>
+area."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/predict.fake_ddf.html b/docs/reference/predict.fake_ddf.html
index 55d80ac..242ccab 100644
--- a/docs/reference/predict.fake_ddf.html
+++ b/docs/reference/predict.fake_ddf.html
@@ -1,9 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for fake detection functions — predict.fake_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for fake detection functions — predict.fake_ddf"><meta name="description" content="Prediction function for dummy detection functions. The function returns as
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for fake detection functions — predict.fake_ddf • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for fake detection functions — predict.fake_ddf"><meta name="description" content="Prediction function for dummy detection functions. The function returns as
 many 1s as there are rows in newdata. If esw=TRUE then the
 strip width is returned."><meta property="og:description" content="Prediction function for dummy detection functions. The function returns as
 many 1s as there are rows in newdata. If esw=TRUE then the
-strip width is returned."></head><body>
+strip width is returned."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/print.dsm.html b/docs/reference/print.dsm.html
index 4d29578..bbf5348 100644
--- a/docs/reference/print.dsm.html
+++ b/docs/reference/print.dsm.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model object — print.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model object — print.dsm"><meta name="description" content="This method just gives a short description of the fitted model. Use the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model object — print.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model object — print.dsm"><meta name="description" content="This method just gives a short description of the fitted model. Use the
 summary.dsm method for more information."><meta property="og:description" content="This method just gives a short description of the fitted model. Use the
-summary.dsm method for more information."></head><body>
+summary.dsm method for more information."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/print.dsm.var.html b/docs/reference/print.dsm.var.html
index 2eebbe4..f88a088 100644
--- a/docs/reference/print.dsm.var.html
+++ b/docs/reference/print.dsm.var.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model variance object — print.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm.var"><meta name="description" content="This method only provides a short summary, use the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model variance object — print.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm.var"><meta name="description" content="This method only provides a short summary, use the
 summary.dsm.var method for information."><meta property="og:description" content="This method only provides a short summary, use the
-summary.dsm.var method for information."></head><body>
+summary.dsm.var method for information."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/print.dsm_varprop.html b/docs/reference/print.dsm_varprop.html
index 0f96ea3..24e7bb6 100644
--- a/docs/reference/print.dsm_varprop.html
+++ b/docs/reference/print.dsm_varprop.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model variance object — print.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm_varprop"><meta name="description" content="This method only provides a short summary, see
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print a description of a density surface model variance object — print.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print a description of a density surface model variance object — print.dsm_varprop"><meta name="description" content="This method only provides a short summary, see
 summary.dsm_varprop."><meta property="og:description" content="This method only provides a short summary, see
-summary.dsm_varprop."></head><body>
+summary.dsm_varprop."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/print.summary.dsm.var.html b/docs/reference/print.summary.dsm.var.html
index eb45394..b0091b5 100644
--- a/docs/reference/print.summary.dsm.var.html
+++ b/docs/reference/print.summary.dsm.var.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print summary of density surface model variance object — print.summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm.var"><meta name="description" content="See summary.dsm.var for information."><meta property="og:description" content="See summary.dsm.var for information."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print summary of density surface model variance object — print.summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm.var"><meta name="description" content="See summary.dsm.var for information."><meta property="og:description" content="See summary.dsm.var for information."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/print.summary.dsm_varprop.html b/docs/reference/print.summary.dsm_varprop.html
index af00fe1..2d2a063 100644
--- a/docs/reference/print.summary.dsm_varprop.html
+++ b/docs/reference/print.summary.dsm_varprop.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print summary of density surface model variance object — print.summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm_varprop"><meta name="description" content="See summary.dsm_varprop for information."><meta property="og:description" content="See summary.dsm_varprop for information."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print summary of density surface model variance object — print.summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print summary of density surface model variance object — print.summary.dsm_varprop"><meta name="description" content="See summary.dsm_varprop for information."><meta property="og:description" content="See summary.dsm_varprop for information."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/rqgam.check.html b/docs/reference/rqgam.check.html
index c7d2b1d..1888fe3 100644
--- a/docs/reference/rqgam.check.html
+++ b/docs/reference/rqgam.check.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check"><meta name="description" content="This function is deprecated, use rqgam_check."><meta property="og:description" content="This function is deprecated, use rqgam_check."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check"><meta name="description" content="This function is deprecated, use rqgam_check."><meta property="og:description" content="This function is deprecated, use rqgam_check."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/rqgam_check.html b/docs/reference/rqgam_check.html
index 0cd97ad..ebf5ddd 100644
--- a/docs/reference/rqgam_check.html
+++ b/docs/reference/rqgam_check.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check"><meta name="description" content='Reproduces the "Resids vs. linear pred" plot from
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check"><meta name="description" content='Reproduces the "Resids vs. linear pred" plot from
 gam.check but using randomised quantile residuals, a la
 Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking
 for "funnel"-type structures in the points, which is much easier with
@@ -9,7 +9,7 @@
 Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking
 for "funnel"-type structures in the points, which is much easier with
 randomised quantile residuals than with deviance residuals, when your model
-uses a count distribution as the response.'></head><body>
+uses a count distribution as the response.'><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/summary.dsm.html b/docs/reference/summary.dsm.html
index 8d72845..535d012 100644
--- a/docs/reference/summary.dsm.html
+++ b/docs/reference/summary.dsm.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize a fitted density surface model — summary.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize a fitted density surface model — summary.dsm"><meta name="description" content="Gives a brief summary of a fitted dsm object."><meta property="og:description" content="Gives a brief summary of a fitted dsm object."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize a fitted density surface model — summary.dsm • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize a fitted density surface model — summary.dsm"><meta name="description" content="Gives a brief summary of a fitted dsm object."><meta property="og:description" content="Gives a brief summary of a fitted dsm object."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/summary.dsm.var.html b/docs/reference/summary.dsm.var.html
index 1137279..8b09fae 100644
--- a/docs/reference/summary.dsm.var.html
+++ b/docs/reference/summary.dsm.var.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize the variance of a density surface model — summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm.var"><meta name="description" content="Gives a brief summary of a fitted dsm variance object."><meta property="og:description" content="Gives a brief summary of a fitted dsm variance object."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize the variance of a density surface model — summary.dsm.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm.var"><meta name="description" content="Gives a brief summary of a fitted dsm variance object."><meta property="og:description" content="Gives a brief summary of a fitted dsm variance object."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/summary.dsm_varprop.html b/docs/reference/summary.dsm_varprop.html
index 43e644d..1060a70 100644
--- a/docs/reference/summary.dsm_varprop.html
+++ b/docs/reference/summary.dsm_varprop.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize the variance of a density surface model — summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm_varprop"><meta name="description" content="Gives a brief summary of a fitted dsm_varprop variance
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summarize the variance of a density surface model — summary.dsm_varprop • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize the variance of a density surface model — summary.dsm_varprop"><meta name="description" content="Gives a brief summary of a fitted dsm_varprop variance
 object."><meta property="og:description" content="Gives a brief summary of a fitted dsm_varprop variance
-object."></head><body>
+object."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
diff --git a/docs/reference/trim.var.html b/docs/reference/trim.var.html
index ff21ce2..0773996 100644
--- a/docs/reference/trim.var.html
+++ b/docs/reference/trim.var.html
@@ -1,9 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Trimmed variance — trim.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Trimmed variance — trim.var"><meta name="description" content="Trim the variance estimates from the bootstrap. This is defined as the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Trimmed variance — trim.var • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Trimmed variance — trim.var"><meta name="description" content="Trim the variance estimates from the bootstrap. This is defined as the
 percentage defined as amount necessary to bring median and trimmed mean
 within 8% of each other these are defined as 'outliers'."><meta property="og:description" content="Trim the variance estimates from the bootstrap. This is defined as the
 percentage defined as amount necessary to bring median and trimmed mean
-within 8% of each other these are defined as 'outliers'."></head><body>
+within 8% of each other these are defined as 'outliers'."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
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diff --git a/docs/reference/vis.concurvity.html b/docs/reference/vis.concurvity.html
index 4f84ce4..fc21fde 100644
--- a/docs/reference/vis.concurvity.html
+++ b/docs/reference/vis.concurvity.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualise concurvity between terms in a GAM — vis.concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis.concurvity"><meta name="description" content="This function is deprecated, use vis_concurvity."><meta property="og:description" content="This function is deprecated, use vis_concurvity."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualise concurvity between terms in a GAM — vis.concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis.concurvity"><meta name="description" content="This function is deprecated, use vis_concurvity."><meta property="og:description" content="This function is deprecated, use vis_concurvity."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
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diff --git a/docs/reference/vis_concurvity.html b/docs/reference/vis_concurvity.html
index a385bbf..9079748 100644
--- a/docs/reference/vis_concurvity.html
+++ b/docs/reference/vis_concurvity.html
@@ -1,11 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualise concurvity between terms in a GAM — vis_concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis_concurvity"><meta name="description" content="Plot measures of how much one term in the model could be explained by
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualise concurvity between terms in a GAM — vis_concurvity • dsm</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualise concurvity between terms in a GAM — vis_concurvity"><meta name="description" content="Plot measures of how much one term in the model could be explained by
 another. When values are high, one should consider re-running variable
 selection with one of the offending variables removed to check for stability
 in term selection."><meta property="og:description" content="Plot measures of how much one term in the model could be explained by
 another. When values are high, one should consider re-running variable
 selection with one of the offending variables removed to check for stability
-in term selection."></head><body>
+in term selection."><script type="text/javascript"> (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);})(window, document, "clarity", "script","maw5mo10ad");</script></head><body>
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diff --git a/docs/search.json b/docs/search.json
index 09d1db7..10e3a20 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"/articles/data_format/dsm-data-formatting.html","id":"introduction","dir":"Articles > Data_format","previous_headings":"","what":"Introduction","title":"`dsm` data formatting example","text":"dsm package expects data specific format. example ’ll show get data format give explanation requirements exist. recommend getting grips fitting models dsm reading tutorial know end point look like trying grapple data. example Gulf Mexico pantropical spotted dolphins available .","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"prerequisites","dir":"Articles > Data_format","previous_headings":"","what":"Prerequisites","title":"`dsm` data formatting example","text":"run code , need ggplot2 sf libraries installed. can following code: Rhode Island data required downloaded : loon observations coastline dbf coastline projection coastline shapefile coastline shx transects dbf transects projection transects shapefile transects shx placed folder called data code work.","code":"install.packages(c(\"ggplot2\", \"sf\"))"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"overview","dir":"Articles > Data_format","previous_headings":"","what":"Overview","title":"`dsm` data formatting example","text":"collect distance sampling data, need record (1) distance observation (along detectability covariates like weather), (2) transect currently , (3) much effort expended transect (length number visits). want fit DSM information needs expanded include transect , can build spatial model. point transects, simple always point, line transects need know position detection (equivalently, far along transect ). abstractly, need information detections effort. also need link two. dsm use three objects hold information: fitted detection function (argument ddf.obj) holds detections ignores spatial information “segments” (argument segment.data) holds chunks effort (parts transect point visits) locations. might also include environmental covariate information. table linking two (argument observation.data), making sure detection corresponding segment. image gives overview different data.frames interact, along corresponds real life situation collecting data transect. rest example goes table turn, explaining construction.","code":""},{"path":[]},{"path":"/articles/data_format/dsm-data-formatting.html","id":"detection-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Detection data","title":"`dsm` data formatting example","text":"data requirements similar packages mrds Distance. cases, detection corresponding row. analysis using dsm additional requirements data (can auto-generated Distance recommend ensure know records link correctly). Distance need: distance : distance detection object : unique detection identifier (link observation table) size : number individuals detection (1 objects occur alone) mrds requires following extra columns (allow double observer surveys): detected : 1 detected observer 0 missed (always 1 single observer) observer : observer number (1 2) (always 1 single observer) covariates affect detectability recorded (e.g., sex weather) can included data analysis mrds Distance. model fitted, deal fitted model object dsm analysis.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segment-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Segment data","title":"`dsm` data formatting example","text":"use term “segment” refer points point transects small chunks transect line transect case. ’ll assume ’ve already segmented lines first describing data format, go example segment line transect data. data.frame required segments needs following columns: Effort : effort expended segment (either length segment lines, number visits points) Sample.Label : unique identifier segment (engaged complicated survey, might take format like “YEAR-AREA-LINE-SEGMENT”, labels like “2020-North--15”, can useful keep track data came later). addition, environmental covariates like location relevant covariates (sea surface temperature, foliage type, altitude, bathymetry, etc) can included data.frame used spatial model.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segmenting","dir":"Articles > Data_format","previous_headings":"Table construction > Segment data","what":"Segmenting","title":"`dsm` data formatting example","text":"data lines want chop segments, section give sample R code . things work extremely dependent input data, hopefully gives template want least. jump : fluent ArcGIS QGIS recommend task software familiar first. simple guide task QGIS ArcGIS recommend MGET toolbox. example, look aerial survey seabirds coast Rhode Island, USA. Data surveys collected University Rhode Island analysed K. Winiarski, Miller, Paton, & McWilliams (2013), K. J. Winiarski, Burt, et al. (2014) K. J. Winiarski, Miller, Paton, & McWilliams (2014). transects saved shapefile “chop-” segments. start let’s plot data coastline:  code loaded sf package. package use spatial work segmenting data. tools expected GIS, though package still developed functionality always increasing. Investigating transects data loaded, can see consists data.frame extra bits (spatial information, including locations (geometry) projection (CRS)): one row per transect (total 24 transects), consists line joining start end points make transects. want can plot individual rows via plot(transects[1,]); can useful check row single line, processing needed (can tricky covered tutorial see “information” ). data looks good shape, can use st_segmentize function take transect make segments . make sure everything works correctly, need project data first. ’m using appropriate projected coordinate system (EPSG code 6348), Rhode Island State Plane. know truncation used detection function 1000m (distances collected bins bin), since ’re trying make segments approximately square, set length twice (since truncation applies either side transect), 2000m. Looking : See now row many coordinates attached , just looking first row (transect 1) comparing coordinates transects segs transects just two coordinates (start end points line). Whereas: segs 5, corresponding segment cut points. now need break rows segs multiple rows, one per segment. bit fiddly. use function provided dieghernan site . first load function (don’t worry understanding code!): can use separate segments look results: now 590 segments 1km 2km long. can check lengths using st_length function plot histogram segment lengths:  Note setting dfMaxLength argument st_segmentize giving rough guide segment length algorithm inside st_segmentize tries get segments equal length possible (note \\(x\\) axis histogram). Note also Transect column segs now duplicate entries transect multiple segments . can now create required columns dsm. First, Effort column can generated using st_length: can generate Sample.Labels. ’ll use complicated naming scheme show ’s done, one simply write segs$Sample.Label <- 1:nrow(segs) sufficient. Instead, like Sample.Label “YEAR-AREA-LINE-SEGMENT” scheme suggested . fancier ways , clarity, let’s use loop: Now can check looks right: required columns taken care , can also calculate centroids segments get locations segment use spatial model. Handily can use st_centroid one step keep data time. get warning centroids might calculated correctly latitude/longitude used. need project data first (Rhode Island State Plane) step using st_transform. Notice geometry type now POINT. can plot centroids previous map:  present, dsm package doesn’t know talk sf objects, final step need simplify segs regular data.frame, dsm can interpret. can extract coordinates centroids using st_coordinates append onto data.frame version object geometry dropped, like : can double check looks right: done. See next section link segments detections. want include spatial covariate information segment table, use (example) rerrdap obtain remotely-sensed data. situ data (.e., weather conditions recorded effort), can complicated require use summarize, see thread information deal situation.","code":"library(ggplot2) library(sf) ## Warning: package 'sf' was built under R version 4.4.2 # coast data, just for reference coastline <- read_sf(\"data/ri_coast.shp\")  # our transect lines transects <- read_sf(\"data/ri_transects.shp\")  # now make the plot p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects, aes(colour=Transect)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal()  print(p) transects ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86581 41.25891) ##  2        2  (-71.82872 41.30738, -71.82145 41.2258) ##  3        3 (-71.78805 41.31562, -71.75676 40.94846) ##  4        4 (-71.74674 41.31728, -71.71394 40.92971) ##  5        5  (-71.70589 41.3241, -71.67156 40.91612) ##  6        6 (-71.66578 41.33943, -71.62329 40.82944) ##  7        7   (-71.5435 41.36397, -71.5064 40.91516) ##  8        8  (-71.5015 41.35994, -71.45266 40.82978) ##  9        9 (-71.46131 41.37378, -71.42458 40.92496) ## 10       10   (-71.421 41.38723, -71.37562 40.83641) ## # ℹ 16 more rows # project transects transects <- st_transform(transects, 6348)  # do the segmenting segs <- st_segmentize(transects, dfMaxLength=units::set_units(2000, \"metres\"))  # transform back to lat/long segs <- st_transform(segs, 4326) transects <- st_transform(transects, 4326) segs ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                                             geometry ##  *    <dbl>                                                     <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86826 41.2861, -71.86703 41.2725, -71.8658… ##  2        2 (-71.82872 41.30738, -71.82727 41.29106, -71.82581 41.27475, -71.82… ##  3        3 (-71.78805 41.31562, -71.78654 41.29813, -71.78505 41.28065, -71.78… ##  4        4 (-71.74674 41.31728, -71.74524 41.29967, -71.74373 41.28205, -71.74… ##  5        5 (-71.70589 41.3241, -71.70438 41.30636, -71.70288 41.28863, -71.701… ##  6        6 (-71.66578 41.33943, -71.6643 41.32185, -71.66283 41.30426, -71.661… ##  7        7 (-71.5435 41.36397, -71.542 41.34602, -71.54051 41.32807, -71.53901… ##  8        8 (-71.5015 41.35994, -71.49986 41.34227, -71.49821 41.3246, -71.4965… ##  9        9 (-71.46131 41.37378, -71.45983 41.35583, -71.45835 41.33788, -71.45… ## 10       10 (-71.421 41.38723, -71.41952 41.36946, -71.41804 41.3517, -71.41656… ## # ℹ 16 more rows st_coordinates(transects[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86581 41.25891  1 st_coordinates(segs[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86826 41.28610  1 ## [3,] -71.86703 41.27250  1 ## [4,] -71.86581 41.25891  1 stdh_cast_substring <- function(x, to = \"MULTILINESTRING\") {   ggg <- st_geometry(x)    if (!unique(st_geometry_type(ggg)) %in% c(\"POLYGON\", \"LINESTRING\")) {     stop(\"Input should be  LINESTRING or POLYGON\")   }   for (k in 1:length(st_geometry(ggg))) {     sub <- ggg[k]     geom <- lapply(       1:(length(st_coordinates(sub)[, 1]) - 1),       function(i)         rbind(           as.numeric(st_coordinates(sub)[i, 1:2]),           as.numeric(st_coordinates(sub)[i + 1, 1:2])         )     ) %>%       st_multilinestring() %>%       st_sfc()      if (k == 1) {       endgeom <- geom     }     else {       endgeom <- rbind(endgeom, geom)     }   }   endgeom <- endgeom %>% st_sfc(crs = st_crs(x))   if (class(x)[1] == \"sf\") {     endgeom <- st_set_geometry(x, endgeom)   }   if (to == \"LINESTRING\") {     endgeom <- endgeom %>% st_cast(\"LINESTRING\")   }   return(endgeom) } segs <- stdh_cast_substring(segs, to=\"LINESTRING\") ## Warning in st_cast.sf(., \"LINESTRING\"): repeating attributes for all ## sub-geometries for which they may not be constant segs ## Simple feature collection with 590 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1  (-71.86948 41.29969, -71.86826 41.2861) ##  2        1   (-71.86826 41.2861, -71.86703 41.2725) ##  3        1  (-71.86703 41.2725, -71.86581 41.25891) ##  4        2 (-71.82872 41.30738, -71.82727 41.29106) ##  5        2 (-71.82727 41.29106, -71.82581 41.27475) ##  6        2 (-71.82581 41.27475, -71.82436 41.25843) ##  7        2  (-71.82436 41.25843, -71.8229 41.24212) ##  8        2   (-71.8229 41.24212, -71.82145 41.2258) ##  9        3 (-71.78805 41.31562, -71.78654 41.29813) ## 10        3 (-71.78654 41.29813, -71.78505 41.28065) ## # ℹ 580 more rows hist(st_length(segs), xlab=\"Segment lengths (m)\", main=\"\") segs$Effort <- st_length(segs) # create a dummy column that we can fill in as we go segs$Sample.Label <- NA  # set the year and area once for this data year <- 2010 area <- \"RI\"  # loop over the transect IDs for(this_transect in unique(segs$Transect)){   # how many segments in this transect?   n_segs <- nrow(subset(segs, Transect==this_transect))   # generate the n_segs labels that we need   segs$Sample.Label[segs$Transect==this_transect] <- paste(year, area, this_transect,                                                            1:n_segs, sep=\"-\") } segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect                                 geometry Effort Sample.Label ##  *    <dbl>                         <LINESTRING [°]>    [m] <chr>        ##  1        1  (-71.86948 41.29969, -71.86826 41.2861)  1515. 2010-RI-1-1  ##  2        1   (-71.86826 41.2861, -71.86703 41.2725)  1515. 2010-RI-1-2  ##  3        1  (-71.86703 41.2725, -71.86581 41.25891)  1515. 2010-RI-1-3  ##  4        2 (-71.82872 41.30738, -71.82727 41.29106)  1818. 2010-RI-2-1  ##  5        2 (-71.82727 41.29106, -71.82581 41.27475)  1818. 2010-RI-2-2  ##  6        2 (-71.82581 41.27475, -71.82436 41.25843)  1818. 2010-RI-2-3  ##  7        2  (-71.82436 41.25843, -71.8229 41.24212)  1818. 2010-RI-2-4  ##  8        2   (-71.8229 41.24212, -71.82145 41.2258)  1818. 2010-RI-2-5  ##  9        3 (-71.78805 41.31562, -71.78654 41.29813)  1948. 2010-RI-3-1  ## 10        3 (-71.78654 41.29813, -71.78505 41.28065)  1948. 2010-RI-3-2  ## # ℹ 580 more rows # save the line version of segs for plotting later segs_lines <- segs  # project segs segs <- st_transform(segs, 6348)  # find centroids segs <- st_centroid(segs) ## Warning: st_centroid assumes attributes are constant over geometries # project back to lat/long segs <- st_transform(segs, 4326)  # how does that look? segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86887 ymin: 40.83823 xmax: -70.89506 ymax: 41.45646 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect             geometry Effort Sample.Label ##  *    <dbl>          <POINT [°]>    [m] <chr>        ##  1        1 (-71.86887 41.29289)  1515. 2010-RI-1-1  ##  2        1  (-71.86764 41.2793)  1515. 2010-RI-1-2  ##  3        1  (-71.86642 41.2657)  1515. 2010-RI-1-3  ##  4        2 (-71.82799 41.29922)  1818. 2010-RI-2-1  ##  5        2  (-71.82654 41.2829)  1818. 2010-RI-2-2  ##  6        2 (-71.82508 41.26659)  1818. 2010-RI-2-3  ##  7        2 (-71.82363 41.25028)  1818. 2010-RI-2-4  ##  8        2 (-71.82218 41.23396)  1818. 2010-RI-2-5  ##  9        3 (-71.78729 41.30687)  1948. 2010-RI-3-1  ## 10        3  (-71.7858 41.28939)  1948. 2010-RI-3-2  ## # ℹ 580 more rows p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=segs) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) segs_df <- cbind(as.data.frame(st_drop_geometry(segs)),               st_coordinates(segs)) head(segs_df) ##   Transect       Effort Sample.Label         X        Y ## 1        1 1515.147 [m]  2010-RI-1-1 -71.86887 41.29289 ## 2        1 1515.151 [m]  2010-RI-1-2 -71.86764 41.27930 ## 3        1 1515.155 [m]  2010-RI-1-3 -71.86642 41.26570 ## 4        2 1818.179 [m]  2010-RI-2-1 -71.82799 41.29922 ## 5        2 1818.184 [m]  2010-RI-2-2 -71.82654 41.28290 ## 6        2 1818.190 [m]  2010-RI-2-3 -71.82508 41.26659"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"linking-observations-and-segments","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Linking observations and segments","title":"`dsm` data formatting example","text":"link together data detection function spatial data giving effort, use observation data.frame. really just cross-reference two tables, detection lives one segment. observation data.frame must (least) following columns: object : unique object identifier (corresponding identifiers detection function) Sample.Label : identifier segment observation occurred size : size observed group (e.g., 1 animals occurred individually) distance : distance observation observation data many rows detection function.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"relating-observations-and-segments-in-practice","dir":"Articles > Data_format","previous_headings":"Table construction > Linking observations and segments","what":"Relating observations and segments in practice","title":"`dsm` data formatting example","text":"methods building observation table. ’ll illustrate one assigning detections segments based distance (.e., detections associated closest segment centroid). appropriate don’t see forwards far, ’s unlikely detections miss assigned. true, instance, aerial survey observers look windows located sides plane might inappropriate shipboard survey observers flying bridge can see way ahead ship. First loading detection data survey (use fit detection function, post-processing): can see columns latitude longitude (Lat Long). can make data.frame sf object telling sf columns contain spatial coordinates (sf lingo, “geometry”): can overlay previous map transects:  Going back segs object , can use st_join function, combined st_nearest_feature function control tables linked. , need project data avoid issues. Now obs acquired additional columns segs, including Sample.Label (want) Effort (don’t). check worked okay, can plot obs segs_lines (line version segments saved earlier) colour coding Sample.Label. randomized colour order easier tell observations misallocated.  segs can remove unnecessary columns geometry get required columns : (Note since binned data, just keep Bin column need process later.) different way approach timestamps waypoints GPS, related segments. One look whether observation made start end times segment.","code":"obs <- read.csv(\"data/loons.csv\") head(obs) ##         Date     Time Bin      Lat      Long Observer object size ## 1 2011-04-19 18991230   B 40.85296 -71.45511       JV     46    1 ## 2 2011-02-17 18991230   A 40.87022 -71.54842       JV    106    1 ## 3 2011-05-02 18991230   A 40.87595 -71.30304       KW    121    1 ## 4 2011-12-04 18991230   C 40.91753 -71.38273       KW    286    1 ## 5 2011-02-23 18991230   A 40.94153 -71.38517       KW    446    1 ## 6 2011-02-07 18991230   A 40.94225 -71.46438       KW    457    1 obs <- st_as_sf(obs, coords=c(\"Long\", \"Lat\")) # set the coordinate system st_crs(obs) <- 4326 obs ## Simple feature collection with 941 features and 6 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size                   geometry ## 1  2011-04-19 18991230   B       JV     46    1 POINT (-71.45511 40.85296) ## 2  2011-02-17 18991230   A       JV    106    1 POINT (-71.54842 40.87022) ## 3  2011-05-02 18991230   A       KW    121    1 POINT (-71.30304 40.87595) ## 4  2011-12-04 18991230   C       KW    286    1 POINT (-71.38273 40.91753) ## 5  2011-02-23 18991230   A       KW    446    1 POINT (-71.38517 40.94153) ## 6  2011-02-07 18991230   A       KW    457    1 POINT (-71.46438 40.94225) ## 7  2011-04-19 18991230   A       JV    500    2 POINT (-71.59495 40.94761) ## 8  2011-05-02 18991230   A       KW    506    1   POINT (-71.552 40.94795) ## 9  2011-02-23 18991230   B       JV    814    1 POINT (-71.51172 40.97912) ## 10 2011-04-19 18991230   A       KW    911    1 POINT (-71.59762 40.98571) p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=obs, size=0.5) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) # project segs segs <- st_transform(segs, 6348) obs <- st_transform(obs, 6348)  # do the join obs <- st_join(obs, segs, join=st_nearest_feature)   # project back to lat/long segs <- st_transform(segs, 4326) obs <- st_transform(obs, 4326)  # how does that look? obs ## Simple feature collection with 941 features and 9 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size Transect       Effort ## 1  2011-04-19 18991230   B       JV     46    1        8 1969.873 [m] ## 2  2011-02-17 18991230   A       JV    106    1       26 1894.100 [m] ## 3  2011-05-02 18991230   A       KW    121    1       12 1988.809 [m] ## 4  2011-12-04 18991230   C       KW    286    1       10 1979.630 [m] ## 5  2011-02-23 18991230   A       KW    446    1       10 1979.624 [m] ## 6  2011-02-07 18991230   A       KW    457    1        8 1969.839 [m] ## 7  2011-04-19 18991230   A       JV    500    2       24 1923.218 [m] ## 8  2011-05-02 18991230   A       KW    506    1       26 1894.075 [m] ## 9  2011-02-23 18991230   B       JV    814    1        7 2000.139 [m] ## 10 2011-04-19 18991230   A       KW    911    1       24 1923.199 [m] ##     Sample.Label                   geometry ## 1   2010-RI-8-29 POINT (-71.45511 40.85296) ## 2  2010-RI-26-16 POINT (-71.54842 40.87022) ## 3  2010-RI-12-31 POINT (-71.30304 40.87595) ## 4  2010-RI-10-27 POINT (-71.38273 40.91753) ## 5  2010-RI-10-26 POINT (-71.38517 40.94153) ## 6   2010-RI-8-24 POINT (-71.46438 40.94225) ## 7  2010-RI-24-12 POINT (-71.59495 40.94761) ## 8  2010-RI-26-12   POINT (-71.552 40.94795) ## 9   2010-RI-7-22 POINT (-71.51172 40.97912) ## 10  2010-RI-24-9 POINT (-71.59762 40.98571) # make a random colour palette to avoid similar colours being near each other pal <- rainbow(nrow(segs), s=.6, v=.9)[sample(1:nrow(segs),nrow(segs))] p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=segs_lines, aes(colour=Sample.Label), pch=21) +   geom_sf(data=obs, size=0.5, aes(colour=Sample.Label)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   scale_colour_manual(values=pal) +   # make the plot look simpler   theme_minimal() +   theme(legend.position = \"none\") print(p) # get rid of \"spatialness\" obs <- st_drop_geometry(obs) # select the columns we need obs <- obs[, c(\"object\", \"Sample.Label\", \"size\", \"Bin\")] head(obs) ##   object  Sample.Label size Bin ## 1     46  2010-RI-8-29    1   B ## 2    106 2010-RI-26-16    1   A ## 3    121 2010-RI-12-31    1   A ## 4    286 2010-RI-10-27    1   C ## 5    446 2010-RI-10-26    1   A ## 6    457  2010-RI-8-24    1   A"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"more-information","dir":"Articles > Data_format","previous_headings":"","what":"More information","title":"`dsm` data formatting example","text":"provided information segmenting wiki page, part US Navy-funded project DenMod. information summarized ?\"dsm-data\" dsm package. may also useful look data example dataset mexdolphins can loaded data(mexdolphins). vignette analysis data available . hard give general information segment lines, extent depends data originally formatted (going way back make model GPS unit used). information dealing spatial data R using sf package available R spatial website (see “Articles” drop header). Another source help distance sampling mailing list. sure search archives prior posting several threads segmenting previously might helpful.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"acknowledgements","dir":"Articles > Data_format","previous_headings":"","what":"Acknowledgements","title":"`dsm` data formatting example","text":"Thanks Phil Bouchet providing helpful comments early version document Iúri Correia finding important bug.","code":""},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"introduction","dir":"Articles > Lines_gomex","previous_headings":"","what":"Introduction","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"analysis based dataset observations pantropical dolphins Gulf Mexico (shipped Distance 6.0 later). convenience data bundled R-friendly format, although code necessary creating data Distance project files available github. OBIS-SEAMAP page data may found SEFSC GoMex Oceanic 1996 survey page. intention highlight features dsm package, rather perform full analysis data. reason, important steps fully explored. familiarity density surface modelling (Miller, Burt, Rexstad, & Thomas, 2013) (Hedley & Buckland, 2004) assumed.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"preamble","dir":"Articles > Lines_gomex","previous_headings":"","what":"Preamble","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"start, load dsm package (dependencies) set options: order run vignette, ’ll need install R packages. can done via following call install.packages:","code":"library(dsm) library(ggplot2)  # plotting options gg.opts <- theme(panel.grid.major=element_blank(),                  panel.grid.minor=element_blank(),                  panel.background=element_blank()) install.packages(c(\"dsm\", \"Distance\", \"knitr\", \"distill\", \"ggplot2\", \"rgdal\",                    \"maptools\", \"plyr\", \"tweedie\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"the-data","dir":"Articles > Lines_gomex","previous_headings":"","what":"The data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data need included dsm package, two additional objects needed plotting required can downloaded put directory file. data can loaded R using following code: add objects survey.area pred.polys environment.","code":"load(\"mexdolphins-extra.rda\")"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"observation-and-segment-data","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Observation and segment data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data analysis nicely pre-formatted shipped dsm. Loading data, can see four data.frames, first lines shown: segdata holds segment data: transects already “chopped” segments. distdata holds distance sampling data used fit detection function. obsdata links distance data segments. preddata holds prediction grid (includes necessary covariates). Typically (.e. datasets) necessary divide transects segments, allocate observations correct segments using GIS similar package1, starting analysis using dsm.","code":"data(mexdolphins) head(segdata) ##   longitude latitude        x        y Effort Transect.Label Sample.Label depth ## 1 -86.92712 29.94378 836105.9 -1011416  13800       19960417   19960417-1 135.0 ## 2 -86.83176 29.84030 846012.9 -1021407  14000       19960417   19960417-2 147.7 ## 3 -86.74445 29.75279 855022.6 -1029785  14000       19960417   19960417-3 152.1 ## 4 -86.65230 29.65522 864610.3 -1039168  13900       19960417   19960417-4 163.8 ## 5 -86.56648 29.56088 873598.1 -1048266  13800       19960417   19960417-5 179.7 ## 6 -86.49290 29.49000 881203.7 -1055004  13800       19960417   19960417-6 188.5 head(distdata) ##     object size  distance Effort detected beaufort latitude longitude ## 45      45   21 3296.6363  36300        1        4 27.72872 -86.00159 ## 61      61  150  929.1937  17800        1        4 25.99896 -87.62712 ## 63      63  125 6051.0009  21000        1        2 26.00693 -87.94881 ## 85      85   75 5499.6971  21800        1        1 27.50344 -90.44891 ## 114    114   50 7258.9837  13400        1        3 27.40568 -94.99483 ## 120    120   45 1454.7962  20900        1        5 26.01765 -95.97449 ##              x        y ## 45  948000.065 -1236192 ## 61  812161.653 -1436899 ## 63  780969.520 -1438985 ## 85  528656.807 -1297833 ## 114  95910.149 -1324562 ## 120   2477.665 -1473909 head(obsdata) ##     object Sample.Label size  distance Effort ## 45      45   19960421-9   21 3296.6363  36300 ## 61      61   19960423-7  150  929.1937  17800 ## 63      63   19960423-9  125 6051.0009  21000 ## 85      85   19960427-1   75 5499.6971  21800 ## 114    114   19960430-8   50 7258.9837  13400 ## 120    120   19960501-5   45 1454.7962  20900 head(preddata) ##   latitude longitude        x          y depth      area ## 1 30.08333 -87.58333 774402.9 -1002759.1    35 271236913 ## 2 30.08333 -87.41667 789688.6 -1001264.5    30 271236913 ## 3 30.08333 -87.25000 804971.3  -999740.6    27 271236913 ## 4 30.08333 -87.08333 820251.1  -998187.5    22 271236913 ## 5 30.08333 -86.91667 835528.0  -996605.2    46 271236913 ## 6 29.91667 -87.75000 760783.1 -1021810.3    14 271236913"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"shapefiles-and-converting-units","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Shapefiles and converting units","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often data spatial analysis comes many different sources. important ensure measurements used analysis compatible units, otherwise resulting estimates incorrect hard interpret. measurements SI units outset removes need conversion later, making life much easier. data already appropriate units (Northings Eastings: kilometres centroid, projected using North American Lambert Conformal Conic projection). extensive literature particular projections latitude longitude appropriate highly recommend reader review particular study area; (Bivand, Pebesma, & Gómez-Rubio, 2008) good starting point. data frames already measurements appropriately converted. convention directions named x y. Using latitude longitude performing spatial smoothing can problematic certain smoother bases used. particular bivariate isotropic bases used non-isotropic nature latitude longitude inconsistent (moving one degree one direction moving one degree ). give example projecting polygon defines survey area (simply read R using readShapeSpatial shapefile produced GIS). Next bit code works study.area outline, massaging use soap film smoother. using soap film smoothers, code extraneous. code generates plot, shows survey area transect lines overlaid (using data segdata).  Also note since projected prediction grid, “squares” look quite like squares. plotting use polygons saved, polygons (stored pred.polys) read shapefile created GIS, object class SpatialPolygons sp package. needs project proper plotting using functions sf package. predsf <- st_as_sf(pred.polys)  Tips plotting polygons available ggplot2 pkg_down.","code":"library(sf) ## Warning: package 'sf' was built under R version 4.4.2 library(plyr)  # tell R that the survey.area object is currently in lat/long sp::proj4string(survey.area) <- sp::CRS(\"+proj=longlat +datum=WGS84\")  predsf <- st_as_sf(pred.polys)  area.sf <- st_as_sf(survey.area) st_crs(area.sf) <- \"WGS84\" area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))  # Convert preddata to a spatial object preddata_sf <- st_as_sf(preddata, coords=c(\"x\", \"y\")) st_crs(preddata_sf) <- st_crs(area.sf.proj) # Perform the intersection preddata_sf <- st_intersection(preddata_sf, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries coords_preddata <- data.frame(st_coordinates(preddata_sf))  preddata_sf$x <- coords_preddata$X preddata_sf$y <- coords_preddata$Y # proj 4 string # using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/ lcc_proj4 <- sp::CRS(\"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs \")  # project using LCC survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)  # simplify the object survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords) names(survey.area) <- c(\"x\", \"y\") segdata_sf <- st_as_sf(segdata, coords = c(\"x\",\"y\")) st_crs(segdata_sf) <- st_crs(area.sf.proj) # study area outline and segment centres plot_segments <- ggplot() +   geom_sf(data = area.sf.proj, fill=\"lightblue\", color = \"blue\", linewidth=.1) +   geom_sf(data=segdata_sf, fill=NA, color=\"black\", linewidth=.3) +   labs(title=\"1996 SE Fisheries Science Center Gulf of Mexico cruise\",        subtitle = \"Points are segment centres\") +   scale_fill_viridis_c(option = \"magma\", guide = \"none\") plot_segments predsf <- st_as_sf(pred.polys) # plot as projected plot(st_geometry(predsf), axes=TRUE)"},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"distance-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Distance data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top panels EDA plots show histograms observed distances cluster size, bottom panels show relationship observed distance observed cluster size, relationship observed distance Beaufort sea state. plots show relationship cluster size observed distance (fewer smaller clusters seem seen larger distances). following code generates EDA plots:  Top row, left right: histograms distance cluster size; bottom row: plot distance cluster size plot distances Beaufort sea state.","code":"par(mfrow=c(2,2))  # histograms hist(distdata$distance,main=\"\",xlab=\"Distance (m)\") hist(distdata$size,main=\"\",xlab=\"Cluster size\")  # plots of distance vs. cluster size plot(distdata$distance, distdata$size, main=\"\", xlab=\"Distance (m)\",      ylab=\"Group size\", pch=19, cex=0.5, col=gray(0.7))  # lm fit l.dat <- data.frame(distance=seq(0,8000,len=1000)) lo <- lm(size~distance, data=distdata) lines(l.dat$distance, as.vector(predict(lo,l.dat)))  plot(distdata$distance,distdata$beaufort, main=\"\", xlab=\"Distance (m)\",      ylab=\"Beaufort sea state\", pch=19, cex=0.5, col=gray(0.7))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Spatial data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Looking separately spatial data without thinking distances, can plot observed group sizes space (point size proportional group size observation). Circle size indicates size group observation. rather large areas observations, might cause variance estimates abundance rather large. also see depth data use depth later explanatory covariate spatial model.","code":"prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid) cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries depth <- ggplot() +          geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +          labs(title = \"Spotted dolphins, Gulf of Mexico\",               subtitle = \"Depth in meters, size of detected dolphin groups\") +          xlab(\"Longitude\") + ylab(\"Latitude\") +          geom_point(aes(x, y, size=size), data=distdata, colour=\"red\",alpha=I(0.5)) +          scale_fill_viridis_c(option = \"viridis\", direction = 1) depth"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"estimating-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"","what":"Estimating the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"use ds function package Distance fit detection function. (Distance package intended make standard distance sampling R relatively straightforward. flexible complex alternative, see function ddf mrds library.) First, loading Distance library: can fit detection function hazard-rate key adjustment terms: Calling summary gives us information parameter estimates, probability detection, AIC, etc: following code generates plot fitted detection function (left) quantile-quantile plot (right):  quantile-quantile plot show relatively good goodness fit hazard-rate detection function.","code":"library(Distance) detfc.hr.null <- ds(distdata, max(distdata$distance), key=\"hr\", adjustment=NULL) ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). ## Warning in ds(distdata, max(distdata$distance), key = \"hr\", adjustment = NULL): ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). summary(detfc.hr.null) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  841.2528  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##             estimate        se ## (Intercept)  7.98244 0.9533779 ##  ## Shape coefficient(s):   ##             estimate        se ## (Intercept)        0 0.7833406 ##  ##                       Estimate         SE        CV ## Average p            0.5912252  0.2224714 0.3762887 ## N in covered region 79.4959326 30.8184416 0.3876732 plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2) gof_ds(detfc.hr.null) ##  ## Goodness of fit results for ddf object ##  ## Distance sampling Cramer-von Mises test (unweighted) ## Test statistic = 0.0972004 p-value = 0.598774"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-covariates-to-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Estimating the detection function","what":"Adding covariates to the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"common include covariates detection function (-called Multiple Covariate Distance Sampling MCDS). dataset two covariates collected individual: Beaufort sea state size. brevity fit hazard-rate detection functions sea state included factor covariate follows: looking summary, detection function covariates give lower AIC model without covariates (843.71 vs. 841.25 hazard-rate model without covariates). Looking back bottom-right panel EDA plots, can see discernible pattern plot Beaufort vs distance. brevity, detection function model selection omitted . practise fit many different forms detection function (select model based goodness fit testing AIC).","code":"detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),                   key=\"hr\", adjustment=NULL) summary(detfc.hr.beau) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  843.7115  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##                         estimate        se ## (Intercept)           7.66291936  1.078972 ## as.factor(beaufort)2  2.24809809 14.754677 ## as.factor(beaufort)3  0.28581820  1.020809 ## as.factor(beaufort)4  0.07133776  1.225360 ## as.factor(beaufort)5 -0.36421115  1.540324 ##  ## Shape coefficient(s):   ##              estimate        se ## (Intercept) 0.2994644 0.5184204 ##  ##                       Estimate         SE        CV ## Average p            0.5420677  0.1753555 0.3234937 ## N in covered region 86.7050383 29.4646397 0.3398262"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"fitting-a-dsm","dir":"Articles > Lines_gomex","previous_headings":"","what":"Fitting a DSM","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"fitting dsm model, data must segmented; consists chopping transects attributing counts segments. mentioned , data already segmented.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"a-simple-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"A simple model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"begin simple model. assume number individuals segment quasi-Poisson distributed smooth function spatial coordinates (note formula exactly one specify gam mgcv). setting group=TRUE, abundance clusters/groups rather individuals can estimated (though ignore ). Note set method=\"REML\" ensure smooth terms estimated reliably. Running model: can obtain summary fitted model: exact interpretation model summary results can found (S. N. Wood, 2017); can see various information smooth components fitted general model statistics. can use deviance explained compare models2. can also get rough idea smooth space looks like using vis.gam (white/yellow indicates high values, red low indicates low values):  type=\"response\" argument ensures plot scale abundance values relative (offsets set median values). means plot useful get idea general shape smooth interpreted directly.","code":"dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)   -18.20       0.53  -34.34   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##         edf Ref.df     F  p-value     ## s(x,y) 24.8  27.49 2.354 0.000733 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.121   Deviance explained = 43.4% ## -REML = 936.04  Scale est. = 94.367    n = 387 vis.gam(dsm.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-another-environmental-covariate-to-the-spatial-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Adding another environmental covariate to the spatial model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data set also contains depth covariate (plotted ). can include model simply: see drop deviance explained, perhaps model useful first. discuss setting k parameter Model checking, . Setting select=TRUE (argument gam) impose extra shrinkage terms smooth model (allowing smooth terms removed model fitting; see ?gam information). particularly useful , include . However many environmental predictors model can good way (along looking \\(p\\)-values) perform term selection. Simply calling plot model object allows us look relationship depth linear predictor:  Omitting argument select call plot plot smooth terms, one time.","code":"dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy.depth) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, k = 10) + s(depth, k = 20) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.740      1.236  -15.16   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F p-value   ## s(x,y)   6.062  7.371 0.923  0.5038   ## s(depth) 9.443 11.466 1.585  0.0824 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0909   Deviance explained = 34.3% ## -REML = 939.52  Scale est. = 124.9     n = 387 plot(dsm.xy.depth, select=2)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-models-when-there-are-covariates-in-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Spatial models when there are covariates in the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"code fit DSM covariates detection function similar models, . However since detection function observation-level covariates, must estimate abundance per segment using Horvitz-Thompson-like estimator modelling, change response abundance.est: can see, summary results rather similar: resulting spatial smooth (though resulting surface somewhat “amplified”):","code":"dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method=\"REML\") summary(dsm.est.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## abundance.est ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -18.0233     0.4739  -38.03   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value     ## s(x,y) 24.64  27.44 2.349 0.00029 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.129   Deviance explained = 42.2% ## -REML = 1043.6  Scale est. = 174.1     n = 387 vis.gam(dsm.est.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", zlim=c(0, 300), contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-response-distributions","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other response distributions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often quasi-Poisson distribution doesn’t give adequate flexibility capture overdispersion response data (see Model checking Model selection ), illustrate two additional distributions can used count data. models section, ’ll move back count response, though estimated abundance also work.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"tweedie","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Tweedie","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Response distributions quasi-Poisson can used, example Tweedie distribution. Tweedie distribution available dsm setting family=tw().","code":"dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method=\"REML\") summary(dsm.xy.tweedie) ##  ## Family: Tweedie(p=1.347)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -17.2640     0.2363  -73.07   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value   ## s(x,y) 12.82  17.13 1.628  0.0562 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0684   Deviance explained = 26.8% ## -REML = 351.49  Scale est. = 60.26     n = 387"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"negative-binomial","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Negative binomial","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Though used , similarly, two options negative binomial distribution: negbin nb. former requires user specification single parameter theta range values parameter (specified vector), latter estimates value theta model fitting process (generally faster). latter recommended users.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-spatial-modelling-options","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other spatial modelling options","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"large literature spatial modelling using GAMs, much can harnessed DSM context. highlights.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"soap-film-smoothing","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other spatial modelling options","what":"Soap film smoothing","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"account complex region (e.g., region includes peninsulae) can use soap film smoother (Simon N. Wood, Bravington, & Hedley, 2008). use soap film smoother spatial part model must create set knots smoother use. easily done using make.soapgrid() function dsm: second argument specifies number points (direction) grid used create knots (knots grid outside survey.area removed). saw exploratory analysis, transect lines outside survey area. cause soap film smoother fail, remove : Note soap_checker script available can useful ensuring boundary, data knots correct format use soap film smoother. can run model depth covariate along spatial (soap film) smooth. Note k argument now refers complexity boundary smooth soap film, complexity film controlled knots given xt argument.","code":"soap.knots <- make.soapgrid(survey.area,c(15,10)) x <- segdata$x; y<-segdata$y onoff <- inSide(x=x,y=y, bnd=as.list(survey.area)) rm(x,y) segdata.soap <- segdata[onoff,] dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs=\"so\", k=15, xt=list(bnd=list(survey.area))) +             s(depth),           family=tw(), method=\"REML\",           detfc.hr.null, segdata.soap, obsdata, knots=soap.knots) summary(dsm.xy.tweedie.soap) ##  ## Family: Tweedie(p=1.356)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, bs = \"so\", k = 15, xt = list(bnd = list(survey.area))) +  ##     s(depth) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.018      0.405  -44.49   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F  p-value     ## s(x,y)   3.050 72.000 0.064 0.114944     ## s(depth) 5.135  6.196 3.895 0.000793 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0653   Deviance explained = 33.1% ## -REML = 339.26  Scale est. = 54.022    n = 365"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-checking","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model checking","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Fitting models well good, ’d like confirm models reasonable; dsm provides functions model checking. can use gam.check generate diagnostic plots:  show deviation Q-Q plot. “line” points plot residuals vs. linear predictor plot corresponds zeros data. Note well plots, gam.check also produces information model fitting. particular interest us last lines tell us basis size. k parameter provided s (te) terms dsm controls complexity smooths model. setting k parameter specify largest complexity smooth term model; long high enough, can sure enough flexibility. output gam.check , can see “p-value” calculated size basis, can good guide whether basis size needs increased. ?choose.k manual page mgcv gives guidance technical details matter. can look model form Tweedie distribution specified response:  Q-Q plot now seems much better (closer \\(y=x\\) line). plots histogram residuals rather hard interpret due large number zeros data. guidance interpreting gam.check output can found (S. N. Wood, 2017).","code":"gam.check(dsm.xy) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 6 iterations. ## Gradient range [-7.105956e-08,-9.826305e-09] ## (score 936.0363 & scale 94.3674). ## Hessian positive definite, eigenvalue range [5.800328,192.5304]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 24.8    1.01    0.99 gam.check(dsm.xy.tweedie) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 8 iterations. ## Gradient range [-1.513175e-06,3.981813e-06] ## (score 351.486 & scale 60.26034). ## Hessian positive definite, eigenvalue range [0.6230615,103.5094]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 12.8    0.73    0.34"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"randomised-quantile-residuals","dir":"Articles > Lines_gomex","previous_headings":"Model checking","what":"Randomised quantile residuals","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top right panel gam.check plots residuals vs. linear predictor plot includes odd line predictions. artifact link function, showing exact zeros data. can misleading distracting, making difficult see whether residuals show heteroskedasticity. Randomised quantile residuals (Dunn & Smyth, 1996) avoid issue transforming residuals exactly normally distributed. makes residuals vs. linear predictor plot much easier interpret therefore doesn’t include artifacts generated link function. plots can produced using rqgam.check dsm:  can see issue heteroskedasticity (increase spread residuals vs. linear predictor plot increasing values linear predictor). One can also plot residuals covariate values check pattern residuals. Note general, plots “Resids vs. linear pred.” interpreted caution output rqgam.check residuals generated normal construction (example Q-Q plot histogram residuals always look fine).","code":"rqgam_check(dsm.xy.tweedie)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-selection","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model selection","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Assuming models “passed” checks gam.check, rqgam.check sufficiently flexible, may left choice model “best”. several methods choosing best model – AIC, REML/GCV scores, deviance explained, full cross-validation test data . Though document intend full analysis pantropical dolphin data, can create results table compare various models fitted far terms abundance estimates associated uncertainties. can use resulting data.frame build table results using kable function:","code":"# make a data.frame to print out mod_results <- data.frame(\"Model name\" = c(\"`dsm.xy`\", \"`dsm.xy.depth`\", \"`dsm.xy.tweedie`\", \"`dsm.xy.tweedie.soap`\",                                            \"`dsm.est.xy`\"),                           \"Description\" = c(\"Bivariate smooth of location, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie\",                                             \"Soap film smooth of location, smooth of depth, Tweedie\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function\"),                           \"Deviance explained\" = c(unlist(lapply(list(dsm.xy,                                                                       dsm.xy.depth,                                                                       dsm.xy.tweedie,                                                                       dsm.xy.tweedie.soap),                  function(x){paste0(round(summary(x)$dev.expl*100,2),\"%\")})),NA)) kable(mod_results, col.names=c(\"Model name\", \"Description\", \"Deviance explained\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"abundance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Abundance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"model checked selected, can make predictions grid calculate abundance. offset stored area column3. Given predicted abundance estimates cell prediction grid, connected prediction grid object mapping:  can calculate abundance survey area simply summing predictions: can compare plot predictions dsm.xy.depth:  can see inclusion depth model noticeable effect distribution (note difference legend scale two plots). can also look total abundance: see much change abundance, terms abundance alone isn’t much two models. Next ’ll go look variance next can see bigger differences models.","code":"dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area) prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid)  preddata_sf$Prediction_xy <- dsm.xy.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.pred) ## [1] 28462.22 dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)  preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, smooth of depth, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.depth.pred) ## [1] 27084.54"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"variance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Variance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Obviously point estimates abundance important, also calculate uncertainty around abundance estimates. Fortunately dsm provides functions perform calculations display resulting uncertainty estimates. two approaches estimating uncertainty abundance estimate dsm. dsm_var_gam assumes spatial model detection function parts model independent . case squared coefficients variation model component added. dsm_var_prop takes account fact detection probability may correlated spatial part model. uses methods described (Bravington, Miller, & Hedley, 2021). dsm_var_prop can applied covariate detection function varies level segments, recorded segment (example Beaufort). don’t situation , opt dsm_var_gam. methods estimate variance abundance element list provided pred.data. case wish obtain abundance prediction cells, use split chop data set list elements give dsm_var_gam (dsm_var_prop). Calling summary give information uncertainty estimation: can also make plot CVs (transect lines observations overlaid) using following code.  Note increase CV away transect lines. can revisit model included depth location smooths observe coefficient variation model larger model location smooth.","code":"preddata.var <- split(preddata, 1:nrow(preddata)) dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,                           off.set=preddata$area) summary(dsm.xy.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 16091.72 28430.76 50231.32  ## (Using log-Normal approximation) ##  ## Point estimate                 : 28430.76  ## CV of detection function       : 0.2471842  ## CV from GAM                    : 0.164  ## Total standard error           : 8433.356  ## Total coefficient of variation : 0.2966 preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries CV <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, uncertainty (CV)\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) CV dsm.xy.depth.var <- dsm_var_gam(dsm.xy.depth, pred.data=preddata.var,                                 off.set=preddata$area) summary(dsm.xy.depth.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 14377.57 27054.60 50909.23  ## (Using log-Normal approximation) ##  ## Point estimate                 : 27054.6  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.1741  ## Total standard error           : 8958.444  ## Total coefficient of variation : 0.3311"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"conclusions","dir":"Articles > Lines_gomex","previous_headings":"","what":"Conclusions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"document outlined analysis spatially-explicit distance sampling data using dsm package. Note many possible models can fitted using dsm aim show just options. Results models can rather different, care must taken performing model selection, discrimination criticism.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"software","dir":"Articles > Lines_gomex","previous_headings":"","what":"Software","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Distance available http://github.com/DistanceDevelopment/Distance well CRAN. dsm available http://github.com/DistanceDevelopment/dsm, well CRAN.","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"David L. Miller. Author. Eric Rexstad. Contributor. Louise Burt. Contributor. Mark V. Bravington. Contributor. Sharon Hedley. Contributor. Megan Ferguson. Contributor. Natalie Kelly. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller DL (2024). dsm: Density Surface Modelling Distance Sampling Data. R package version 2.3.3.9001, https://github.com/DistanceDevelopment/dsm.","code":"@Manual{,   title = {dsm: Density Surface Modelling of Distance Sampling Data},   author = {David L. Miller},   year = {2024},   note = {R package version 2.3.3.9001},   url = {https://github.com/DistanceDevelopment/dsm}, }"},{"path":[]},{"path":"/index.html","id":"what-is-dsm","dir":"","previous_headings":"","what":"Density Surface Modelling of Distance Sampling Data","title":"Density Surface Modelling of Distance Sampling Data","text":"dsm provides Generalized Additive Model (GAM)-based approach calculate spatially-explicit estimates animal abundance distance sampling (also presence/absence strip transect) data. Several utility functions provided model checking, plotting variance estimation. Open Access paper describing methods: Miller, DL, ML Burt, EA Rexstad L Thomas (2013). Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution.","code":""},{"path":"/index.html","id":"using-dsm","dir":"","previous_headings":"","what":"Using dsm","title":"Density Surface Modelling of Distance Sampling Data","text":"Examples use dsm distance sampling R packages available examples.distancesampling.org.","code":""},{"path":"/reference/block.info.per.su.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the block information — block.info.per.su","title":"Find the block information — block.info.per.su","text":"Takes transect data works many blocks given size (segment terms) fit .","code":""},{"path":"/reference/block.info.per.su.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the block information — block.info.per.su","text":"","code":"block.info.per.su(block.size, data, name.su)"},{"path":"/reference/block.info.per.su.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the block information — block.info.per.su","text":"block.size number segments per block data data used build model name.su names sampling units (.e., transects)","code":""},{"path":"/reference/block.info.per.su.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the block information — block.info.per.su","text":"data.frame following columns name        sample unit name (e.g. transect label) num.seg     number segments transect num.block   number blocks available start.block block number first block end.block   block number last block num.req     number blocks needed unit","code":""},{"path":"/reference/check.cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names exist — check.cols","title":"Check column names exist — check.cols","text":"Internal function check supplied data.frames correct columns checks sample labels unique.","code":""},{"path":"/reference/check.cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names exist — check.cols","text":"","code":"check.cols(ddf.obj, segment.data, observation.data, segment.area)"},{"path":"/reference/check.cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names exist — check.cols","text":"ddf.obj ddf object mrds segment.data segment data defined dsm observation.data observation data defined dsm segment.area area segments","code":""},{"path":"/reference/check.cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names exist — check.cols","text":"nothing, throws error something went wrong","code":""},{"path":"/reference/check.cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check column names exist — check.cols","text":"David Lawrence Miller","code":""},{"path":"/reference/dsm-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Data format for DSM — dsm-data","title":"Data format for DSM — dsm-data","text":"Two data.frames must provided dsm. referred observation.data segment.data.","code":""},{"path":"/reference/dsm-data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data format for DSM — dsm-data","text":"segment.data table sample identifiers define segments, corresponding effort (line length) expended environmental covariates used model abundance/density. observation.data provides link table observations used detection function samples (segments), can aggregate observations segments (.e., observation.data \"look-table\" observations segments). observation.data - observation data.frame must (least) following columns: object unique object identifier Sample.Label identifier segment observation occurred size size observed group (e.g., 1 animals occurred individually) distance distance observation One can often also use observation.data fit detection function (additional columns detection function covariates allowed table). segment.data: segment data.frame must (least) following columns: Effort effort (terms length segment) Sample.Label identifier segment (unique!) ??? environmental covariates, example location (projected latitude longitude), relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).","code":""},{"path":"/reference/dsm-data.html","id":"multiple-detection-functions","dir":"Reference","previous_headings":"","what":"Multiple detection functions","title":"Data format for DSM — dsm-data","text":"multiple detection functions used, column named ddfobj must included observation.data segment.data. lets model know detection function observation . numeric ordered ddf.obj argument dsm, e.g., ddf.obj=list(ship_ddf, aerial_ddf) means ship detections ddfobj=1 aerial detections ddfobj=2 observation data.","code":""},{"path":"/reference/dsm-data.html","id":"mark-recapture-distance-sampling-models","dir":"Reference","previous_headings":"","what":"Mark-recapture distance sampling models","title":"Data format for DSM — dsm-data","text":"using mrds models include mark-recapture components (currently independent observer trial modes supported) format observation data needs checked ensure observations duplicated. observer column also required observation.data. Independent observer mode unique observations (unique object IDs) required. Trial mode observations made observer 1 required.","code":""},{"path":"/reference/dsm-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Density surface modelling — dsm-package","title":"Density surface modelling — dsm-package","text":"dsm implements spatial models distance sampling data. Models detectability can fitted using packages mrds Distance. dsm fits generalized additive models spatially-referenced data. See Miller et al (2013) introduction.","code":""},{"path":"/reference/dsm-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density surface modelling — dsm-package","text":"information distance sampling methods example code available http://distancesampling.org/R/. help distance sampling package, Google Group https://groups.google.com/forum/#!forum/distance-sampling. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density surface modelling — dsm-package","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm.cor","title":"Check for autocorrelation in residuals — dsm.cor","text":"function deprecated, use dsm_cor.","code":""},{"path":"/reference/dsm.cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm.cor","text":"","code":"dsm.cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm.cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm.cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Fits density surface model (DSM) detection adjusted counts spatially-referenced distance sampling analysis. dsm takes observations animals, allocates segments line (strip transects) optionally adjusts counts based detectability using supplied detection function model. generalized additive model, generalized mixed model generalized linear model used model adjusted counts based formula involving environmental covariates.","code":""},{"path":"/reference/dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"dsm(   formula,   ddf.obj,   segment.data,   observation.data,   engine = \"gam\",   convert.units = 1,   family = quasipoisson(link = \"log\"),   group = FALSE,   control = list(keepData = TRUE),   availability = 1,   segment.area = NULL,   weights = NULL,   method = \"REML\",   ... )"},{"path":"/reference/dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"formula formula surface. valid formula. See \"Details\", , define response. ddf.obj result call ddf ds. multiple detection functions required list can provided. strip/circle transects assumed objects observed, see dummy_ddf. Mark-recapture distance sampling (mrds) models type io (independent observers) trial allowed. segment.data segment data, see dsm-data. observation.data observation data, see dsm-data. engine fitting engine used DSM (\"glm\"/\"gam\"/\"gamm\"/\"bam\"). convert.units conversion factor multiply area segments . See 'Units' . family response distribution (popular choices include quasipoisson, Tweedie/tw negbin/nb). Defaults quasipoisson. group TRUE abundance groups calculated rather abundance individuals. Setting option TRUE equivalent setting size group 1. control usual control argument gam; keepData must TRUE variance estimation work (though option set GLMs GAMMs). availability estimate availability bias. count models used multiply effective strip width (must vector length 1 length number rows segment.data); estimated abundance/estimated density models used scale response (must vector length 1 length number rows observation.data). Uncertainty availability handled present. segment.area NULL (default) segment areas calculated multiplying Effort column segment.data (right minus left) truncation distance ddf.obj strip.width. Alternatively vector segment areas can provided (must length number rows segment.data) character string giving name column segment.data contains areas. segment.area specified takes precedent. weights weights observation used model fitting. default, weights=NULL, weights observation area (see Details). Setting scalar value (e.g., weights=1) observations equally weighted. method smoothing parameter estimation method. Default \"REML\", using Restricted Maximum Likelihood. See gam options. Ignored engine=\"glm\". ... anything else passed straight glm, gam, gamm bam.","code":""},{"path":"/reference/dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"glm, gam, gamm bam object, additional element, $ddf holds detection function object.","code":""},{"path":"/reference/dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"response (LHS formula) can one following (restrictions outlined ): count count segment abundance.est estimated abundance per segment, estimation via Horvitz-Thompson estimator density.est density per segment offset used model dependent response: count area segment multiplied average probability detection segment abundance.est area segment density zero count response can used detection function covariates vary segments/points (within). example, weather conditions (like visibility sea state) foliage cover usually acceptable change within segment, animal sex behaviour work. abundance.est response can used covariates detection function. density case, observations can weighted segment areas via weights= argument. default (weights=NULL), density estimated weights set segment areas (using segment.area calculated detection function object metadata Effort data). Alternatively weights=1 set weights equal. third alternative pass vector length equal number segments, containing appropriate weights. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"often case distances collected metres segment lengths recorded kilometres. dsm allows provide conversion factor (convert.units) multiply areas . example: distances metres segment lengths kilometres setting convert.units=1000 lead analysis metres. Setting convert.units=1/1000 lead analysis kilometres. conversion factor applied segment.area specified.","code":""},{"path":"/reference/dsm.html","id":"large-models","dir":"Reference","previous_headings":"","what":"Large models","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"large models, engine=\"bam\" method=\"fREML\" may useful. Models specified bam gam. Read bam using option; option considered EXPERIMENTAL moment. particular note default basis choice (thin plate regression splines) slow general fitting less stable using gam. negative binomial response, theta must specified using bam.","code":""},{"path":"/reference/dsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"David L. Miller","code":""},{"path":"/reference/dsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) summary(mod1)  # predict over a grid mod1.pred <- predict(mod1, preddata, preddata$area)  # calculate the predicted abundance over the grid sum(mod1.pred)  # plot the smooth plot(mod1) } # }"},{"path":"/reference/dsm.var.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm.var.gam","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"function deprecated, use dsm_var_gam.","code":""},{"path":"/reference/dsm.var.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"","code":"dsm.var.gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm.var.movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"function deprecated, use dsm_var_movblk.","code":""},{"path":"/reference/dsm.var.movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"","code":"dsm.var.movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm.var.movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL – file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm.var.prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm.var.prop","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"function deprecated, use dsm_var_prop.","code":""},{"path":"/reference/dsm.var.prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"","code":"dsm.var.prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm_cor","title":"Check for autocorrelation in residuals — dsm_cor","text":"DSM fitted data, function can used check autocorrelation residuals.","code":""},{"path":"/reference/dsm_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"dsm_cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm_cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for autocorrelation in residuals — dsm_cor","text":"plot vector fun applied lags.","code":""},{"path":"/reference/dsm_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for autocorrelation in residuals — dsm_cor","text":"Within Transect.Label, segments sorted according Segment.Labels. may require time get right particular data. one multiple surveys transects revisited, example, one may want make Transect.Label unique transect-survey identifier. Neither label need included model, must just present $data field model. usually means segment data passed dsm. current iteration function plot correlations nicely, things can get function return data (assigning result object). NA values residuals correlogram calculated. usually occurs due NA values covariates (smoother fitted values ). Code like (.na(dsm.obj$data)) might helpful.","code":""},{"path":"/reference/dsm_cor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for autocorrelation in residuals — dsm_cor","text":"David L. Miller","code":""},{"path":"/reference/dsm_cor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"# \\donttest{  library(Distance) #>  #> Attaching package: 'Distance' #> The following object is masked from 'package:dsm': #>  #>     dummy_ddf #> The following object is masked from 'package:mrds': #>  #>     create.bins  library(dsm)   # load the data, see ?mexdolphins  data(mexdolphins)   # fit a model  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.433 #> No survey area information supplied, only estimating detection function.  mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) #> Warning: Some observations are outside of detection function 1 truncation!   # look at lag 1 differences up to a maximum of lag 9, using deviance  # residuals  dsm_cor(mod1, resid.type=\"deviance\", max.lag=9,          Segment.Label=\"Sample.Label\")  # }"},{"path":"/reference/dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance propagation for density surface models — dsm_varprop","title":"Variance propagation for density surface models — dsm_varprop","text":"Calculate uncertainty predictions fitted DSM, including uncertainty detection function.","code":""},{"path":"/reference/dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance propagation for density surface models — dsm_varprop","text":"","code":"dsm_varprop(   model,   newdata = NULL,   trace = FALSE,   var.type = \"Vp\",   var_type = NULL )"},{"path":"/reference/dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance propagation for density surface models — dsm_varprop","text":"model fitted dsm. newdata prediction grid. Set NULL avoid making predictions just return model objects. trace debugging, see scale parameter estimation going. var.type variance-covariance matrix used (\"Vp\" variance-covariance conditional smoothing parameter(s), \"Vc\" unconditional). See gamObject details/explanation. doubt, stick default, \"Vp\". var_type deprecated, use var.type instead.","code":""},{"path":"/reference/dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance propagation for density surface models — dsm_varprop","text":"list elements: old_model fitted model supplied function model refit refitted model object, extra term pred point estimates predictions newdata var total variance calculated newdata ses standard error prediction cell newdata newdata=NULL last three entries NA.","code":""},{"path":"/reference/dsm_varprop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance propagation for density surface models — dsm_varprop","text":"make predictions spatial model, also want know uncertainty abundance estimate. Since density surface models 2 () stage models, need incorporate uncertainty earlier stages (.e. detection function) \"final\" uncertainty estimate. function refit spatial model include Hessian offset extra term. Variance estimates using new model can used calculate variance predicted abundance estimates incorporate detection function uncertainty. Importantly requires detection function covariates, vary within segment (, example covariates like sex used). information construct prediction grid data.frame, newdata, see predict.dsm. routine useful detection function covariates used DSM. Note can use var.type=\"Vc\" (see gamObject), variance-covariance matrix spatial model, corrected smoothing parameter uncertainty. See Wood, Pya & S\\\"afken (2016) information. Models fixed scale parameters (e.g., negative binomial) require extra round optimisation.","code":""},{"path":"/reference/dsm_varprop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Variance propagation for density surface models — dsm_varprop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_varprop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance propagation for density surface models — dsm_varprop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535. Wood, S.N., Pya, N. S\\\"afken, B. (2016) Smoothing parameter model selection general smooth models. Journal American Statistical Association, 1-45.","code":""},{"path":"/reference/dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance propagation for density surface models — dsm_varprop","text":"David L. Miller, based code Mark V. Bravington Sharon L. Hedley.","code":""},{"path":"/reference/dsm_var_gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm_var_gam","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"one willing assume detection function spatial model independent, function produce estimates variance predictions abundance, using result squared coefficients variation add.","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"dsm_var_gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"list elements model fitted model object pred.var variance regions given pred.data. bootstrap logical, always FALSE model fitted model extra term dsm.object original model (dsm.obj )","code":""},{"path":"/reference/dsm_var_gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"David L. Miller","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"if (FALSE) { # \\dontrun{  library(Distance)  library(dsm)   # load the Gulf of Mexico dolphin data (see ?mexdolphins)  data(mexdolphins)   # fit a detection function and look at the summary  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL)  summary(hr.model)   # fit a simple smooth of x and y  mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)   # Calculate the variance  # this will give a summary over the whole area in mexdolphins$preddata  mod1.var <- dsm_var_gam(mod1, preddata, off.set=preddata$area) } # }"},{"path":"/reference/dsm_var_movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Estimate variance abundance area using moving block bootstrap. Two procedures implemented, one incorporating detection function uncertainty, one .","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"dsm_var_movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm_var_movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL – file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm_var_movblk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Setting ds.uncertainty=TRUE incorporate detection function uncertainty directly bootstrap. done generating observations fitted detection function re-fitting new detection function (form), calculating new effective strip width. Rejection sampling used generate observations (except half-normal case) procedure can rather slow. Note currently supported covariates detection function. Setting ds.uncertainty=FALSE incorporate detection function uncertainty using delta method. assumes detection function spatial model INDEPENDENT. probably reasonable.","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata) summary(mod1)  # calculate the variance by 500 moving block bootstraps mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,    block.size = 3, samp.unit.name = \"Transect.Label\",    off.set = preddata$area,    bar = TRUE, bs.file = \"mexico-bs.csv\", ds.uncertainty = TRUE) } # }"},{"path":"/reference/dsm_var_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm_var_prop","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"ensure uncertainty detection function correctly propagated final variance estimate abundance, function uses method first detailed Williams et al (2011), explanation given Bravington et al. (2021).","code":""},{"path":"/reference/dsm_var_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"","code":"dsm_var_prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"list elements model fitted model object pred.var variance region given pred.data bootstrap logical, always FALSE pred.data .set model fitted model extra term dsm.object original model, model.check simple check subtracting coefficients two models see large difference deriv numerically calculated Hessian offset","code":""},{"path":"/reference/dsm_var_prop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"idea refit spatial model including extra random effect. random effect zero mean hence effect point estimates. variance Hessian detection function. Variance estimates incorporate detection function uncertainty. mathematical details given paper references . Many prediction grids can supplied supplying list data.frames function. Note routine simply calls dsm_varprop. require multiple prediction grids, routine probably faster. routine useful detection function covariates used DSM.","code":""},{"path":"/reference/dsm_var_prop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_var_prop.html","id":"limitations","dir":"Reference","previous_headings":"","what":"Limitations","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Note routine useful detection function used DSM. used abundance.est density.est responses used. Importantly requires detection function covariates, vary within segment (, example covariates like sex used).","code":""},{"path":"/reference/dsm_var_prop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535.","code":""},{"path":"/reference/dsm_var_prop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Mark V. Bravington, Sharon L. Hedley. Bugs added David L. Miller.","code":""},{"path":"/reference/dummy_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection function objects when detection is certain — dummy_ddf","title":"Detection function objects when detection is certain — dummy_ddf","text":"Create detection function object strip/plot surveys use density surface models.","code":""},{"path":"/reference/dummy_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection function objects when detection is certain — dummy_ddf","text":"","code":"dummy_ddf(object, size = 1, width, left = 0, transect = \"line\")"},{"path":"/reference/dummy_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection function objects when detection is certain — dummy_ddf","text":"object numeric vector object identifiers, relating object field observation data DSM. size group size observation (default groups size 1) width right truncation left left truncation (default 0, left truncation) transect \"line\" \"point\" transect","code":""},{"path":"/reference/dummy_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection function objects when detection is certain — dummy_ddf","text":"David L Miller","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data from a fitted detection function — generate.ds.uncertainty","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"using dsm.var.movblk ds.uncertainty=TRUE, procedure generates data fitted detection function (assuming correct).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"","code":"generate.ds.uncertainty(ds.object)"},{"path":"/reference/generate.ds.uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"ds.object fitted detection function object (returned call ddf.ds).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"function changes random number generator seed. avoid potential side-effects, use something like: seed <- get(\".Random.seed\",envir=.GlobalEnv) running code assign(\".Random.seed\",seed,envir=.GlobalEnv) .","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"David L. Miller","code":""},{"path":"/reference/generate.mb.sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Moving block bootstrap sampler — generate.mb.sample","title":"Moving block bootstrap sampler — generate.mb.sample","text":"usually used , called within dsm.var.movblk.","code":""},{"path":"/reference/generate.mb.sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Moving block bootstrap sampler — generate.mb.sample","text":"","code":"generate.mb.sample(   num.blocks.required,   block.size,   which.blocks,   dsm.data,   unit.info,   n.units )"},{"path":"/reference/generate.mb.sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Moving block bootstrap sampler — generate.mb.sample","text":"num.blocks.required number blocks need. block.size number segments per block. .blocks blocks sampled. dsm.data $data element result call dsm. unit.info result calling block.info.per.su. n.units number sampling units.","code":""},{"path":"/reference/generate.mb.sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Moving block bootstrap sampler — generate.mb.sample","text":"vector log-residuals","code":""},{"path":"/reference/latlong2km.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert latitude and longitude to Northings and Eastings — latlong2km","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Convert longitude latitude co-ordinates kilometres west-east south-north axes (lon0,lat0) using \"spherical law cosines\".","code":""},{"path":"/reference/latlong2km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"","code":"latlong2km(lon, lat, lon0 = sum(range(lon))/2, lat0 = sum(range(lat))/2)"},{"path":"/reference/latlong2km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"lon longitude lat latitude lon0 longitude reference point (defaults mean longitude) lat0 latitude reference point (defaults mean latitude)","code":""},{"path":"/reference/latlong2km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"list elements km.e km.n.","code":""},{"path":"/reference/latlong2km.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"WARNING: approximate procedure converting latitude/ longitude Northing/Easting. Consider using projection conversions available packages sp, sf rgdal better results.","code":""},{"path":"/reference/latlong2km.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Simon N. Wood","code":""},{"path":"/reference/make.soapgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"routine simply creates grid knots (correct format) used \"internal\" part soap film smoother","code":""},{"path":"/reference/make.soapgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"","code":"make.soapgrid(bnd, n.grid)"},{"path":"/reference/make.soapgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"bnd list elements x y give locations boundary vertices. first last elements . n.grid either one number giving number points along x y axes used create grid, vector giving number x direction, y direction.","code":""},{"path":"/reference/make.soapgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"list elements x y, containing knot locations.","code":""},{"path":"/reference/make.soapgrid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"David L Miller","code":""},{"path":"/reference/mexdolphins.html","id":null,"dir":"Reference","previous_headings":"","what":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Data combination several NOAA shipboard surveys conducted pan-tropical spotted dolphins Gulf Mexico. data consist 47 observations groups dolphins. group size recorded, well Beaufort sea state time observation. Coordinates observation bathymetry data also available covariates analysis. complete example analysis (description data) provided http://distancesampling.org/R/vignettes/mexico-analysis.html.","code":""},{"path":"/reference/mexdolphins.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Halpin, P.N., .J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, . Dimatteo, J. Cleary, C. Good, L.B. Crowder, K.D. Hyrenbach. 2009. OBIS-SEAMAP: world data center marine mammal, sea bird, sea turtle distributions. Oceanography 22(2):104-115 NOAA Southeast Fisheries Science Center. 1996. Report Cetacean Survey Oceanic Selected Continental Shelf Waters Northern Gulf Mexico aboard NOAA Ship Oregon II (Cruise 220)","code":""},{"path":"/reference/obs_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Observed versus expected diagnostics for fitted DSMs — obs_exp","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"Given covariate, calculate observed expected counts unique value covariate. can useful goodness fit check DSMs.","code":""},{"path":"/reference/obs_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"obs_exp(model, covar, cut = NULL)"},{"path":"/reference/obs_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"model fitted dsm model object covar covariate aggregate (character) cut vector cut points aggregate . supplied, unique values covar used.","code":""},{"path":"/reference/obs_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"data.frame values observed expected counts.","code":""},{"path":"/reference/obs_exp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"One strategy model checking calculate observed expected counts different aggregations variable. match well model fit good.","code":""},{"path":"/reference/obs_exp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"David L Miller, suggestion Mark Bravington.","code":""},{"path":"/reference/obs_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # example with the Gulf of Mexico dolphin data data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) } # }"},{"path":"/reference/plot.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a density surface model. — plot.dsm","title":"Plot a density surface model. — plot.dsm","text":"See plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a density surface model. — plot.dsm","text":"","code":"# S3 method for class 'dsm' plot(x, ...)"},{"path":"/reference/plot.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a density surface model. — plot.dsm","text":"x model fitted dsm ... arguments passed plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a density surface model. — plot.dsm","text":"plot!","code":""},{"path":[]},{"path":"/reference/plot.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a density surface model. — plot.dsm","text":"David L. Miller","code":""},{"path":"/reference/plot.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Create plots of abundance uncertainty — plot.dsm.var","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"Note prediction data set must x y columns even used model.","code":""},{"path":"/reference/plot.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"","code":"# S3 method for class 'dsm.var' plot(   x,   poly = NULL,   limits = NULL,   breaks = NULL,   legend.breaks = NULL,   xlab = \"x\",   ylab = \"y\",   observations = TRUE,   plot = TRUE,   boxplot.coef = 1.5,   x.name = \"x\",   y.name = \"y\",   gg.grad = NULL,   ... )"},{"path":"/reference/plot.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"x dsm.var object poly list data.frame columns x y, gives coordinates polygon draw. may also optionally column group, many polygons. limits limits fill colours breaks breaks colour fill legend.breaks breaks displayed xlab label x axis ylab label y axis observations observations plotted? plot actually plot map, just return ggplot2 object? boxplot.coef control trimming (summary.dsm.var), effect bootstrap file saved. x.name name variable plot x axis. y.name name variable plot y axis. gg.grad optional ggplot gradient object. ... arguments","code":""},{"path":"/reference/plot.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"plot","code":""},{"path":"/reference/plot.dsm.var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"order get plotting work dsm_var_prop dsm_var_gam, one must first format data correctly since functions designed compute general summaries. One summary calculated element list pred supplied dsm_var_prop dsm_var_gam. plot uncertainty prediction grid, pred (data.frame), say, can create correct format simply using pred.new <- split(pred,1:nrow(pred)).","code":""},{"path":[]},{"path":"/reference/plot.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/plot_pred_by_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially plot predictions per model term — plot_pred_by_term","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"Plot effect smooth model spatially. term model, plot effect space. Plots made scale, relative influence smooth can seen.","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"plot_pred_by_term(dsm.obj, data, location.cov = c(\"x\", \"y\"))"},{"path":"/reference/plot_pred_by_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"dsm.obj fitted dsm object data data use plot (often prediction grid), data also include width height columns plotting location.cov deprecated, use location.cov","code":""},{"path":"/reference/plot_pred_by_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"ggplot2 plot","code":""},{"path":"/reference/plot_pred_by_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"David L Miller (idea taken inlabru)","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data and fit a model data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y) + s(depth), hr.model, segdata, obsdata)  preddata$width <- preddata$height <- sqrt(preddata$area)  # make the plot plot_pred_by_term(mod1, preddata, c(\"x\",\"y\"))  # better plot would be # library(viridis) # plot_pred_by_term(mod1, preddata, c(\"x\",\"y\")) + scale_fill_viridis() } # }"},{"path":"/reference/predict.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from a fitted density surface model — predict.dsm","title":"Predict from a fitted density surface model — predict.dsm","text":"Make predictions density abundance outside (inside) covered area.","code":""},{"path":"/reference/predict.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from a fitted density surface model — predict.dsm","text":"","code":"# S3 method for class 'dsm' predict(object, newdata = NULL, off.set = NULL, type = \"response\", ...)"},{"path":"/reference/predict.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from a fitted density surface model — predict.dsm","text":"object fitted dsm object newdata spatially referenced covariates e.g. altitude, depth, distance shore, etc. Covariates data.frame must names identical variable names used fitting model .set area cells prediction grid. units segments/distances given dsm. Replaces column newdata called .set supplied. Ignored newdata supplied type scale results . default \"response\", see predict.gam explanation options (usually necessary) ... arguments passed predict.gam","code":""},{"path":"/reference/predict.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from a fitted density surface model — predict.dsm","text":"predicted values response scale default (unless type specified, case see predict.gam).","code":""},{"path":"/reference/predict.dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict from a fitted density surface model — predict.dsm","text":"newdata supplied, predictions made data built model. Note order results necessarily segdata (segment data) data.frame supplied dsm. area .set used argument supplied, otherwise look areas column named .set newdata. Either way link function (usually log) applied offsets, need log passing function.","code":""},{"path":[]},{"path":"/reference/predict.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict from a fitted density surface model — predict.dsm","text":"David L. Miller","code":""},{"path":"/reference/predict.fake_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for fake detection functions — predict.fake_ddf","title":"Prediction for fake detection functions — predict.fake_ddf","text":"Prediction function dummy detection functions. function returns many 1s rows newdata. esw=TRUE strip width returned.","code":""},{"path":"/reference/predict.fake_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for fake detection functions — predict.fake_ddf","text":"","code":"# S3 method for class 'fake_ddf' predict(   object,   newdata = NULL,   compute = FALSE,   int.range = NULL,   esw = FALSE,   ... )"},{"path":"/reference/predict.fake_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for fake detection functions — predict.fake_ddf","text":"object model object newdata many 1s return? compute unused, compatibility mrds::predict int.range unused, compatibility mrds::predict esw strip width returned? ... S3 consistency","code":""},{"path":"/reference/predict.fake_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction for fake detection functions — predict.fake_ddf","text":"David L Miller","code":""},{"path":"/reference/print.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model object — print.dsm","title":"Print a description of a density surface model object — print.dsm","text":"method just gives short description fitted model. Use summary.dsm method information.","code":""},{"path":"/reference/print.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model object — print.dsm","text":"","code":"# S3 method for class 'dsm' print(x, ...)"},{"path":"/reference/print.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model object — print.dsm","text":"x model fitted dsm ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model object — print.dsm","text":"NULL","code":""},{"path":"/reference/print.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model object — print.dsm","text":"David L. Miller","code":""},{"path":"/reference/print.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm.var","title":"Print a description of a density surface model variance object — print.dsm.var","text":"method provides short summary, use summary.dsm.var method information.","code":""},{"path":"/reference/print.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm.var","text":"","code":"# S3 method for class 'dsm.var' print(x, ...)"},{"path":"/reference/print.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm.var","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model variance object — print.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm_varprop","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"method provides short summary, see summary.dsm_varprop.","code":""},{"path":"/reference/print.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"","code":"# S3 method for class 'dsm_varprop' print(x, ...)"},{"path":"/reference/print.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":[]},{"path":"/reference/print.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm.var","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"See summary.dsm.var information.","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"","code":"# S3 method for class 'summary.dsm.var' print(x, ...)"},{"path":"/reference/print.summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm_varprop","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"See summary.dsm_varprop information.","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"","code":"# S3 method for class 'summary.dsm_varprop' print(x, ...)"},{"path":"/reference/print.summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/rqgam.check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"function deprecated, use rqgam_check.","code":""},{"path":"/reference/rqgam.check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"","code":"rqgam.check(gam.obj, ...)"},{"path":"/reference/rqgam.check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Reproduces \"Resids vs. linear pred\" plot gam.check using randomised quantile residuals, la Dunn Smyth (1996). Checks heteroskedasticity usual, looking \"funnel\"-type structures points, much easier randomised quantile residuals deviance residuals, model uses count distribution response.","code":""},{"path":"/reference/rqgam_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"rqgam_check(gam.obj, ...)"},{"path":"/reference/rqgam_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"just plots!","code":""},{"path":"/reference/rqgam_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Note function works negative binomial Tweedie response distributions. Earlier versions function produced full gam.check output, confusing one plots really useful.  Checks k computed, need done using gam.check.","code":""},{"path":"/reference/rqgam_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Based code Natalie Kelly, bugs added Dave Miller","code":""},{"path":"/reference/rqgam_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"# \\donttest{ library(Distance) library(dsm) library(tweedie)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.433 #> No survey area information supplied, only estimating detection function.  # fit a simple smooth of x and y with a Tweedie response with estimated #  p parameter mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw()) #> Warning: Some observations are outside of detection function 1 truncation! rqgam_check(mod1)  # }"},{"path":"/reference/summary.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted density surface model — summary.dsm","title":"Summarize a fitted density surface model — summary.dsm","text":"Gives brief summary fitted dsm object.","code":""},{"path":"/reference/summary.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted density surface model — summary.dsm","text":"","code":"# S3 method for class 'dsm' summary(object, ...)"},{"path":"/reference/summary.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted density surface model — summary.dsm","text":"object model fitted dsm ... arguments passed summary.gam","code":""},{"path":"/reference/summary.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted density surface model — summary.dsm","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize a fitted density surface model — summary.dsm","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm.var","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"Gives brief summary fitted dsm variance object.","code":""},{"path":"/reference/summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"","code":"# S3 method for class 'dsm.var' summary(   object,   alpha = 0.05,   boxplot.coef = 1.5,   bootstrap.subregions = NULL,   ... )"},{"path":"/reference/summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95\\ confidence intervals) boxplot.coef value coef used calculate outliers see boxplot. bootstrap.subregions list vectors logicals indices subregions variances need calculated (bootstraps (see dsm.var.prop use subregions variance propagation). ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm_varprop","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"Gives brief summary fitted dsm_varprop variance object.","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"","code":"# S3 method for class 'dsm_varprop' summary(object, alpha = 0.05, ...)"},{"path":"/reference/summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95% confidence internal) ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/trim.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimmed variance — trim.var","title":"Trimmed variance — trim.var","text":"Trim variance estimates bootstrap. defined percentage defined amount necessary bring median trimmed mean within 8% defined 'outliers'.","code":""},{"path":"/reference/trim.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimmed variance — trim.var","text":"","code":"trim.var(untrimmed.bootstraps, boxplot.coef = 1.5)"},{"path":"/reference/trim.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimmed variance — trim.var","text":"untrimmed.bootstraps (usually $study.area.total element returned dsm.var.movblk bootstrap object. boxplot.coef value coef used calculate outliers see boxplot.","code":""},{"path":"/reference/trim.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimmed variance — trim.var","text":"trimmed variance","code":""},{"path":"/reference/trim.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trimmed variance — trim.var","text":"Louise Burt","code":""},{"path":"/reference/vis.concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis.concurvity","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"function deprecated, use vis_concurvity.","code":""},{"path":"/reference/vis.concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"","code":"vis.concurvity(model, type = \"estimate\")"},{"path":"/reference/vis.concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis_concurvity","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"Plot measures much one term model explained another. values high, one consider re-running variable selection one offending variables removed check stability term selection.","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"vis_concurvity(model, type = \"estimate\")"},{"path":"/reference/vis_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"methods considered somewhat experimental time. Consult concurvity information concurvity measures calculated.","code":""},{"path":"/reference/vis_concurvity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"David L Miller","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata)  # visualise concurvity using the \"estimate\" metric vis_concurvity(mod1) } # }"},{"path":"/news/index.html","id":"dsm-234","dir":"Changelog","previous_headings":"","what":"dsm 2.3.4","title":"dsm 2.3.4","text":"fix bug predictions NA (e.g., predictions outside soap boundary), variances NA due propagation (#4)","code":""},{"path":"/news/index.html","id":"dsm-233","dir":"Changelog","previous_headings":"","what":"dsm 2.3.3","title":"dsm 2.3.3","text":"CRAN release: 2022-08-20 dsm.var.prop -> dsm_var_prop dsm.var.gam -> dsm_var_gam dsm.var.movblk -> dsm_var_movblk dsm.cor -> dsm_cor rqgam.check -> rqgam_check vis.concurvity -> vis_concurvity var_type -> var.type","code":""},{"path":"/news/index.html","id":"dsm-232","dir":"Changelog","previous_headings":"","what":"dsm 2.3.2","title":"dsm 2.3.2","text":"CRAN release: 2022-03-17 fixed bug offset calculation dsm_varprop se estimation offsets logged twice. Thanks Megan Ferguson spotting . documentation now rmarkdown format dsm 2.3.1 transect type (line point) now determined detection function rather using transect= argument multiple detection functions now possible, use list() fitted detection functions supplied ddf.obj= argument dsm(). dsm.var.gam, dsm.var.prop dsm_varprop compatible. Vignette coming soon. Thanks Dave Fifield Ewan Wakefield extensive testing suggestions. See ?“dsm-data” information set data. strip transects now handled using dummy_ddf() function. presence~ response type removed Response options “D”, “density”, “Dhat”, “N”, “Nhat” “n” deprecated. Now “count”, “abundance.est” “density.est” allowed. Make predict.dsm work tibbles .set column newdata (error thrown previous code’s check ). Thanks Dave Fifield reporting issue. Fix bug inconsistent predictions density.est used response. Code predict.dsm simplified documentation updated. Thanks Dave Fifield reporting issue. mrds independent observer (io) trial models allowed detection function.Variance can estimated via dsm_varprop (usual limitation covariates may vary segment level) dsm.var.gam. feature currently experimental. Thanks Doug Sigourney Megan Ferguson help suggestions. See ?“dsm-data” information set data. example multiddf mrds analysis dsm https://github.com/densitymodelling/nefsc_fin_mrds_dsm availability= argument now works differently count models. availability must now length number segments (nrow(segment.data)) multiply offset (effective area) rather number animals observed coherent detectability handled case. dsm_varprop longer requies newdata supplied. newdata isn’t present just refitted original models returned, entries NA.","code":""},{"path":"/news/index.html","id":"dsm-230","dir":"Changelog","previous_headings":"","what":"dsm 2.3.0","title":"dsm 2.3.0","text":"CRAN release: 2020-04-22 fixed dsm_varprop (gam.fixed.priors) response distributions fixed scale parameters. Negative binomial results now may incorrect!","code":""},{"path":"/news/index.html","id":"dsm-2218","dir":"Changelog","previous_headings":"","what":"dsm 2.2.18","title":"dsm 2.2.18","text":"Clarification predict.dsm return documentation. Thanks Dave Fifield suggestion. .set=NULL lead predictions 0 density used. Now just returns density. Thanks Dave Fifield catch.","code":""},{"path":"/news/index.html","id":"dsm-2217","dir":"Changelog","previous_headings":"","what":"dsm 2.2.17","title":"dsm 2.2.17","text":"CRAN release: 2019-01-19 Now Suggests package “sp” (needed parts mexdolphin example data)","code":""},{"path":"/news/index.html","id":"dsm-2216","dir":"Changelog","previous_headings":"","what":"dsm 2.2.16","title":"dsm 2.2.16","text":"CRAN release: 2018-06-27 check Sample.Labels match , none throw error. Thanks Madhura Davate bringing issue attention. gamma option removed, since now ignored gam(). using method=“GCV.Cp” gamma=1.4 still used (generally advise using REML rather GCV smoothing parameter selection) dsm now requires mgcv version 1.8-23 higher. dsm.var.prop (dsm_varprop) now throw error covariates detection function can’t anything useful new function obs_exp diagnostic function compare observed expected counts different covariate aggregations","code":""},{"path":"/news/index.html","id":"dsm-2215","dir":"Changelog","previous_headings":"","what":"dsm 2.2.15","title":"dsm 2.2.15","text":"CRAN release: 2017-07-03 mexdolphins loaded using data(), data longer need attach()ed dsm.var.prop throws error count N used response. dsm.var.prop now calls dsm_varprop, keeps scale parameter constant refitting models variance propagation. dsm_varprop provides lower-level interface variance propagation probably faster flexible. add vis.concurvity visually assess concurvity (colinearity extended smooth terms) model new model diagnostic function: plot_pred_by_term – plots effect smooth model spatially. remove “abundance” option response, use “count” dsm.var.prop dsm_varprop better diagnostics checking variance model hasn’t done something weird dsm.var.movblk discards replicates model didn’t converge fix potential issue negative binomial response used variance propagation (due scale parameterisations). dsm_varprop now refits nb() models negbin() advise users look deeply. Thanks Eric Rexstad spotting . fix bug predict.dsm() predictions “density” response including offset. Thanks Len Thomas spotting .","code":""},{"path":"/news/index.html","id":"dsm-2214","dir":"Changelog","previous_headings":"","what":"dsm 2.2.14","title":"dsm 2.2.14","text":"CRAN release: 2017-01-26 nasty bug predict.dsm fixed: logging offset twice newdata provided, leading odd checking plots. also effected results predict() newdata= specified. Thanks Megan Ferguson alerting us . add support point transects(!) rqgam.check() now produced linear predictor vs. randomised quantile residuals plot avoid confusion interpreting plots REML now default method smoothing parameter selection","code":""},{"path":"/news/index.html","id":"dsm-2213","dir":"Changelog","previous_headings":"","what":"dsm 2.2.13","title":"dsm 2.2.13","text":"CRAN release: 2016-10-03 dsm.var.gam now example dsm.var.prop now checks detection function model covariates","code":""},{"path":"/news/index.html","id":"dsm-2212","dir":"Changelog","previous_headings":"","what":"dsm 2.2.12","title":"dsm 2.2.12","text":"CRAN release: 2016-08-10 alpha argument now works analytical variance reported using dsm.var.prop dsm.var.gam print.summary summary methods. Thanks Jason Roberts Eric Rexstad spotting issue. dsm now uses numDeriv::grad calculate derivatives variance estimation – much faster. Thanks Jason Roberts fix. dsm now requires mrds version 2.1.15 higher, due internal changes fixed issue Effort column required even segment.area specified. Thanks Greg Lollback spotting issue. ensured ordering segments matched ordering observations aggregation observations correct. Thanks Dave Fifield reporting bug suggesting fix. check.cols longer checks see column called “distance” strip transects used","code":""},{"path":"/news/index.html","id":"dsm-2211","dir":"Changelog","previous_headings":"","what":"dsm 2.2.11","title":"dsm 2.2.11","text":"dsm.var.prop uses update() rather messing around eval() print.summary.dsm.var didn’t work GAM variance detection function specified, now fixed (thanks Natalia Dellabianca spotting ) Removed non-working plot() code variance examples – see Mexico pantropical spotted dolphins proper treatment plotting using projections (http://distancesampling.org/R/vignettes/mexico-analysis.html) plot.var.gam longer crashes default distance data doesn’t columns “x” “y”","code":""},{"path":"/news/index.html","id":"dsm-2210","dir":"Changelog","previous_headings":"","what":"dsm 2.2.10","title":"dsm 2.2.10","text":"corrected offsets examples dsm, dsm.var.prop, dsm.var.movblk updated mexdolphins data lat/long reversal prediction data non-list storage stopped use rqgam.check used non-negbin/Tweedie responses (thanks Phil Bouchet highlighting issue).","code":""},{"path":"/news/index.html","id":"dsm-229","dir":"Changelog","previous_headings":"","what":"dsm 2.2.9","title":"dsm 2.2.9","text":"CRAN release: 2015-07-29 Moving block bootstrap now works detection function (suggested Laura Williamson) Mexico dolphin data areas prediction grids segments re-projected. Thanks Phil Bouchet pointing error. Context: https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ","code":""},{"path":"/news/index.html","id":"dsm-228","dir":"Changelog","previous_headings":"","what":"dsm 2.2.8","title":"dsm 2.2.8","text":"Speed reduced memory usage variance calculations, thanks Filipe Dias asking . Better error observations matched detection function. Thanks Ricardo Lima highlighting . Check missing columns incorrect logic, now corrected tests. Thanks Julia Migné spotting .","code":""},{"path":"/news/index.html","id":"dsm-227","dir":"Changelog","previous_headings":"","what":"dsm 2.2.7","title":"dsm 2.2.7","text":"Plotting check plots using random quantile residuals (rqgam.check) didn’t work Tweedie tw family models, now fixed. Thanks Laura Mannocci Jason Roberts pointing . Plotting CV used matrix() coerce data data.frame – thus converting elements simplest type. Errors ensued. Thanks Adrian Schiavini finding bug!","code":""},{"path":"/news/index.html","id":"dsm-226","dir":"Changelog","previous_headings":"","what":"dsm 2.2.6","title":"dsm 2.2.6","text":"dsm.var.prop dsm.var.gam now allow supply single number offset, copy many times need. Thanks Filipe Dias suggesting .","code":""},{"path":"/news/index.html","id":"dsm-225","dir":"Changelog","previous_headings":"","what":"dsm 2.2.5","title":"dsm 2.2.5","text":"CRAN release: 2014-09-16 dsm.var.prop now throw error detection function dsm ’s trying calculate variance (thanks Adrián Schiavini finding ) variance/CV plotting threw error detection function supplied observation=TRUE. Now error sidestepped (, thanks Adrián Schiavini finding ).","code":""},{"path":"/news/index.html","id":"dsm-224","dir":"Changelog","previous_headings":"","what":"dsm 2.2.4","title":"dsm 2.2.4","text":"CRAN release: 2014-06-17 changed stop() warning() data detection function included observation data frame, automatically excluded later, bug found Eric Rexstad Anna Cucknell","code":""},{"path":"/news/index.html","id":"dsm-223","dir":"Changelog","previous_headings":"","what":"dsm 2.2.3","title":"dsm 2.2.3","text":"CRAN release: 2014-04-23 moving block boostrap using observed abundances residuals therefore gave wrong answers (Eric Rexstad found bug)","code":""},{"path":"/news/index.html","id":"dsm-222","dir":"Changelog","previous_headings":"","what":"dsm 2.2.2","title":"dsm 2.2.2","text":"version number reporting now works (Len Thomas found bug) documentation default density presence models now weighted area segment","code":""},{"path":"/news/index.html","id":"dsm-221","dir":"Changelog","previous_headings":"","what":"dsm 2.2.1","title":"dsm 2.2.1","text":"switch new testthat spec added new response variables (Dhat, density.est, count, n), suggestion Len Thomas","code":""},{"path":"/news/index.html","id":"dsm-214","dir":"Changelog","previous_headings":"","what":"dsm 2.1.4","title":"dsm 2.1.4","text":"check observed distances within width now throw error","code":""},{"path":"/news/index.html","id":"dsm-213","dir":"Changelog","previous_headings":"","what":"dsm 2.1.3","title":"dsm 2.1.3","text":"CRAN release: 2013-08-20 CRAN-compatibility fixes","code":""},{"path":"/news/index.html","id":"dsm-212","dir":"Changelog","previous_headings":"","what":"dsm 2.1.2","title":"dsm 2.1.2","text":"updates dsm.cor unified plotting variance estimation tweaks non-dsm models","code":""},{"path":"/news/index.html","id":"dsm-206","dir":"Changelog","previous_headings":"","what":"dsm 2.0.6","title":"dsm 2.0.6","text":"removed warning () comparison length make.soapgrid need pass bnd list() dsm.cor now handles gamm objects dsm.cor allows one check residual autocorrelation models presence/absence data now longer needs dummy detection function object","code":""},{"path":"/news/index.html","id":"dsm-205","dir":"Changelog","previous_headings":"","what":"dsm 2.0.5","title":"dsm 2.0.5","text":"started NEWS file fixed bug dsm.var.gam() didn’t return predictions plotting result fail silly typo docs check ’re using mgcv version >= 1.7-24 GAMM fitting warn ","code":""}]
+[{"path":"/articles/data_format/dsm-data-formatting.html","id":"introduction","dir":"Articles > Data_format","previous_headings":"","what":"Introduction","title":"`dsm` data formatting example","text":"dsm package expects data specific format. example ’ll show get data format give explanation requirements exist. recommend getting grips fitting models dsm reading tutorial know end point look like trying grapple data. example Gulf Mexico pantropical spotted dolphins available .","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"prerequisites","dir":"Articles > Data_format","previous_headings":"","what":"Prerequisites","title":"`dsm` data formatting example","text":"run code , need ggplot2 sf libraries installed. can following code: Rhode Island data required downloaded : loon observations coastline dbf coastline projection coastline shapefile coastline shx transects dbf transects projection transects shapefile transects shx placed folder called data code work.","code":"install.packages(c(\"ggplot2\", \"sf\"))"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"overview","dir":"Articles > Data_format","previous_headings":"","what":"Overview","title":"`dsm` data formatting example","text":"collect distance sampling data, need record (1) distance observation (along detectability covariates like weather), (2) transect currently , (3) much effort expended transect (length number visits). want fit DSM information needs expanded include transect , can build spatial model. point transects, simple always point, line transects need know position detection (equivalently, far along transect ). abstractly, need information detections effort. also need link two. dsm use three objects hold information: fitted detection function (argument ddf.obj) holds detections ignores spatial information “segments” (argument segment.data) holds chunks effort (parts transect point visits) locations. might also include environmental covariate information. table linking two (argument observation.data), making sure detection corresponding segment. image gives overview different data.frames interact, along corresponds real life situation collecting data transect. rest example goes table turn, explaining construction.","code":""},{"path":[]},{"path":"/articles/data_format/dsm-data-formatting.html","id":"detection-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Detection data","title":"`dsm` data formatting example","text":"data requirements similar packages mrds Distance. cases, detection corresponding row. analysis using dsm additional requirements data (can auto-generated Distance recommend ensure know records link correctly). Distance need: distance : distance detection object : unique detection identifier (link observation table) size : number individuals detection (1 objects occur alone) mrds requires following extra columns (allow double observer surveys): detected : 1 detected observer 0 missed (always 1 single observer) observer : observer number (1 2) (always 1 single observer) covariates affect detectability recorded (e.g., sex weather) can included data analysis mrds Distance. model fitted, deal fitted model object dsm analysis.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segment-data","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Segment data","title":"`dsm` data formatting example","text":"use term “segment” refer points point transects small chunks transect line transect case. ’ll assume ’ve already segmented lines first describing data format, go example segment line transect data. data.frame required segments needs following columns: Effort : effort expended segment (either length segment lines, number visits points) Sample.Label : unique identifier segment (engaged complicated survey, might take format like “YEAR-AREA-LINE-SEGMENT”, labels like “2020-North--15”, can useful keep track data came later). addition, environmental covariates like location relevant covariates (sea surface temperature, foliage type, altitude, bathymetry, etc) can included data.frame used spatial model.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"segmenting","dir":"Articles > Data_format","previous_headings":"Table construction > Segment data","what":"Segmenting","title":"`dsm` data formatting example","text":"data lines want chop segments, section give sample R code . things work extremely dependent input data, hopefully gives template want least. jump : fluent ArcGIS QGIS recommend task software familiar first. simple guide task QGIS ArcGIS recommend MGET toolbox. example, look aerial survey seabirds coast Rhode Island, USA. Data surveys collected University Rhode Island analysed K. Winiarski, Miller, Paton, & McWilliams (2013), K. J. Winiarski, Burt, et al. (2014) K. J. Winiarski, Miller, Paton, & McWilliams (2014). transects saved shapefile “chop-” segments. start let’s plot data coastline:  code loaded sf package. package use spatial work segmenting data. tools expected GIS, though package still developed functionality always increasing. Investigating transects data loaded, can see consists data.frame extra bits (spatial information, including locations (geometry) projection (CRS)): one row per transect (total 24 transects), consists line joining start end points make transects. want can plot individual rows via plot(transects[1,]); can useful check row single line, processing needed (can tricky covered tutorial see “information” ). data looks good shape, can use st_segmentize function take transect make segments . make sure everything works correctly, need project data first. ’m using appropriate projected coordinate system (EPSG code 6348), Rhode Island State Plane. know truncation used detection function 1000m (distances collected bins bin), since ’re trying make segments approximately square, set length twice (since truncation applies either side transect), 2000m. Looking : See now row many coordinates attached , just looking first row (transect 1) comparing coordinates transects segs transects just two coordinates (start end points line). Whereas: segs 5, corresponding segment cut points. now need break rows segs multiple rows, one per segment. bit fiddly. use function provided dieghernan site . first load function (don’t worry understanding code!): can use separate segments look results: now 590 segments 1km 2km long. can check lengths using st_length function plot histogram segment lengths:  Note setting dfMaxLength argument st_segmentize giving rough guide segment length algorithm inside st_segmentize tries get segments equal length possible (note \\(x\\) axis histogram). Note also Transect column segs now duplicate entries transect multiple segments . can now create required columns dsm. First, Effort column can generated using st_length: can generate Sample.Labels. ’ll use complicated naming scheme show ’s done, one simply write segs$Sample.Label <- 1:nrow(segs) sufficient. Instead, like Sample.Label “YEAR-AREA-LINE-SEGMENT” scheme suggested . fancier ways , clarity, let’s use loop: Now can check looks right: required columns taken care , can also calculate centroids segments get locations segment use spatial model. Handily can use st_centroid one step keep data time. get warning centroids might calculated correctly latitude/longitude used. need project data first (Rhode Island State Plane) step using st_transform. Notice geometry type now POINT. can plot centroids previous map:  present, dsm package doesn’t know talk sf objects, final step need simplify segs regular data.frame, dsm can interpret. can extract coordinates centroids using st_coordinates append onto data.frame version object geometry dropped, like : can double check looks right: done. See next section link segments detections. want include spatial covariate information segment table, use (example) rerrdap obtain remotely-sensed data. situ data (.e., weather conditions recorded effort), can complicated require use summarize, see thread information deal situation.","code":"library(ggplot2) library(sf)  # coast data, just for reference coastline <- read_sf(\"data/ri_coast.shp\")  # our transect lines transects <- read_sf(\"data/ri_transects.shp\")  # now make the plot p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects, aes(colour=Transect)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal()  print(p) transects ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86581 41.25891) ##  2        2  (-71.82872 41.30738, -71.82145 41.2258) ##  3        3 (-71.78805 41.31562, -71.75676 40.94846) ##  4        4 (-71.74674 41.31728, -71.71394 40.92971) ##  5        5  (-71.70589 41.3241, -71.67156 40.91612) ##  6        6 (-71.66578 41.33943, -71.62329 40.82944) ##  7        7   (-71.5435 41.36397, -71.5064 40.91516) ##  8        8  (-71.5015 41.35994, -71.45266 40.82978) ##  9        9 (-71.46131 41.37378, -71.42458 40.92496) ## 10       10   (-71.421 41.38723, -71.37562 40.83641) ## # ℹ 16 more rows # project transects transects <- st_transform(transects, 6348)  # do the segmenting segs <- st_segmentize(transects, dfMaxLength=units::set_units(2000, \"metres\"))  # transform back to lat/long segs <- st_transform(segs, 4326) transects <- st_transform(transects, 4326) segs ## Simple feature collection with 26 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 26 × 2 ##    Transect                                                             geometry ##  *    <dbl>                                                     <LINESTRING [°]> ##  1        1 (-71.86948 41.29969, -71.86826 41.2861, -71.86703 41.2725, -71.8658… ##  2        2 (-71.82872 41.30738, -71.82727 41.29106, -71.82581 41.27475, -71.82… ##  3        3 (-71.78805 41.31562, -71.78654 41.29813, -71.78505 41.28065, -71.78… ##  4        4 (-71.74674 41.31728, -71.74524 41.29967, -71.74373 41.28205, -71.74… ##  5        5 (-71.70589 41.3241, -71.70438 41.30636, -71.70288 41.28863, -71.701… ##  6        6 (-71.66578 41.33943, -71.6643 41.32185, -71.66283 41.30426, -71.661… ##  7        7 (-71.5435 41.36397, -71.542 41.34602, -71.54051 41.32807, -71.53901… ##  8        8 (-71.5015 41.35994, -71.49986 41.34227, -71.49821 41.3246, -71.4965… ##  9        9 (-71.46131 41.37378, -71.45983 41.35583, -71.45835 41.33788, -71.45… ## 10       10 (-71.421 41.38723, -71.41952 41.36946, -71.41804 41.3517, -71.41656… ## # ℹ 16 more rows st_coordinates(transects[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86581 41.25891  1 st_coordinates(segs[1,]) ##              X        Y L1 ## [1,] -71.86948 41.29969  1 ## [2,] -71.86826 41.28610  1 ## [3,] -71.86703 41.27250  1 ## [4,] -71.86581 41.25891  1 stdh_cast_substring <- function(x, to = \"MULTILINESTRING\") {   ggg <- st_geometry(x)    if (!unique(st_geometry_type(ggg)) %in% c(\"POLYGON\", \"LINESTRING\")) {     stop(\"Input should be  LINESTRING or POLYGON\")   }   for (k in 1:length(st_geometry(ggg))) {     sub <- ggg[k]     geom <- lapply(       1:(length(st_coordinates(sub)[, 1]) - 1),       function(i)         rbind(           as.numeric(st_coordinates(sub)[i, 1:2]),           as.numeric(st_coordinates(sub)[i + 1, 1:2])         )     ) %>%       st_multilinestring() %>%       st_sfc()      if (k == 1) {       endgeom <- geom     }     else {       endgeom <- rbind(endgeom, geom)     }   }   endgeom <- endgeom %>% st_sfc(crs = st_crs(x))   if (class(x)[1] == \"sf\") {     endgeom <- st_set_geometry(x, endgeom)   }   if (to == \"LINESTRING\") {     endgeom <- endgeom %>% st_cast(\"LINESTRING\")   }   return(endgeom) } segs <- stdh_cast_substring(segs, to=\"LINESTRING\") ## Warning in st_cast.sf(., \"LINESTRING\"): repeating attributes for all ## sub-geometries for which they may not be constant segs ## Simple feature collection with 590 features and 1 field ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 2 ##    Transect                                 geometry ##       <dbl>                         <LINESTRING [°]> ##  1        1  (-71.86948 41.29969, -71.86826 41.2861) ##  2        1   (-71.86826 41.2861, -71.86703 41.2725) ##  3        1  (-71.86703 41.2725, -71.86581 41.25891) ##  4        2 (-71.82872 41.30738, -71.82727 41.29106) ##  5        2 (-71.82727 41.29106, -71.82581 41.27475) ##  6        2 (-71.82581 41.27475, -71.82436 41.25843) ##  7        2  (-71.82436 41.25843, -71.8229 41.24212) ##  8        2   (-71.8229 41.24212, -71.82145 41.2258) ##  9        3 (-71.78805 41.31562, -71.78654 41.29813) ## 10        3 (-71.78654 41.29813, -71.78505 41.28065) ## # ℹ 580 more rows hist(st_length(segs), xlab=\"Segment lengths (m)\", main=\"\") segs$Effort <- st_length(segs) # create a dummy column that we can fill in as we go segs$Sample.Label <- NA  # set the year and area once for this data year <- 2010 area <- \"RI\"  # loop over the transect IDs for(this_transect in unique(segs$Transect)){   # how many segments in this transect?   n_segs <- nrow(subset(segs, Transect==this_transect))   # generate the n_segs labels that we need   segs$Sample.Label[segs$Transect==this_transect] <- paste(year, area, this_transect,                                                            1:n_segs, sep=\"-\") } segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: LINESTRING ## Dimension:     XY ## Bounding box:  xmin: -71.86948 ymin: 40.82944 xmax: -70.8944 ymax: 41.46521 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect                                 geometry Effort Sample.Label ##  *    <dbl>                         <LINESTRING [°]>    [m] <chr>        ##  1        1  (-71.86948 41.29969, -71.86826 41.2861)  1515. 2010-RI-1-1  ##  2        1   (-71.86826 41.2861, -71.86703 41.2725)  1515. 2010-RI-1-2  ##  3        1  (-71.86703 41.2725, -71.86581 41.25891)  1515. 2010-RI-1-3  ##  4        2 (-71.82872 41.30738, -71.82727 41.29106)  1818. 2010-RI-2-1  ##  5        2 (-71.82727 41.29106, -71.82581 41.27475)  1818. 2010-RI-2-2  ##  6        2 (-71.82581 41.27475, -71.82436 41.25843)  1818. 2010-RI-2-3  ##  7        2  (-71.82436 41.25843, -71.8229 41.24212)  1818. 2010-RI-2-4  ##  8        2   (-71.8229 41.24212, -71.82145 41.2258)  1818. 2010-RI-2-5  ##  9        3 (-71.78805 41.31562, -71.78654 41.29813)  1948. 2010-RI-3-1  ## 10        3 (-71.78654 41.29813, -71.78505 41.28065)  1948. 2010-RI-3-2  ## # ℹ 580 more rows # save the line version of segs for plotting later segs_lines <- segs  # project segs segs <- st_transform(segs, 6348)  # find centroids segs <- st_centroid(segs) ## Warning: st_centroid assumes attributes are constant over geometries # project back to lat/long segs <- st_transform(segs, 4326)  # how does that look? segs ## Simple feature collection with 590 features and 3 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86887 ymin: 40.83823 xmax: -70.89506 ymax: 41.45646 ## Geodetic CRS:  WGS 84 ## # A tibble: 590 × 4 ##    Transect             geometry Effort Sample.Label ##  *    <dbl>          <POINT [°]>    [m] <chr>        ##  1        1 (-71.86887 41.29289)  1515. 2010-RI-1-1  ##  2        1  (-71.86764 41.2793)  1515. 2010-RI-1-2  ##  3        1  (-71.86642 41.2657)  1515. 2010-RI-1-3  ##  4        2 (-71.82799 41.29922)  1818. 2010-RI-2-1  ##  5        2  (-71.82654 41.2829)  1818. 2010-RI-2-2  ##  6        2 (-71.82508 41.26659)  1818. 2010-RI-2-3  ##  7        2 (-71.82363 41.25028)  1818. 2010-RI-2-4  ##  8        2 (-71.82218 41.23396)  1818. 2010-RI-2-5  ##  9        3 (-71.78729 41.30687)  1948. 2010-RI-3-1  ## 10        3  (-71.7858 41.28939)  1948. 2010-RI-3-2  ## # ℹ 580 more rows p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=segs) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) segs_df <- cbind(as.data.frame(st_drop_geometry(segs)),               st_coordinates(segs)) head(segs_df) ##   Transect       Effort Sample.Label         X        Y ## 1        1 1515.147 [m]  2010-RI-1-1 -71.86887 41.29289 ## 2        1 1515.151 [m]  2010-RI-1-2 -71.86764 41.27930 ## 3        1 1515.155 [m]  2010-RI-1-3 -71.86642 41.26570 ## 4        2 1818.179 [m]  2010-RI-2-1 -71.82799 41.29922 ## 5        2 1818.184 [m]  2010-RI-2-2 -71.82654 41.28290 ## 6        2 1818.190 [m]  2010-RI-2-3 -71.82508 41.26659"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"linking-observations-and-segments","dir":"Articles > Data_format","previous_headings":"Table construction","what":"Linking observations and segments","title":"`dsm` data formatting example","text":"link together data detection function spatial data giving effort, use observation data.frame. really just cross-reference two tables, detection lives one segment. observation data.frame must (least) following columns: object : unique object identifier (corresponding identifiers detection function) Sample.Label : identifier segment observation occurred size : size observed group (e.g., 1 animals occurred individually) distance : distance observation observation data many rows detection function.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"relating-observations-and-segments-in-practice","dir":"Articles > Data_format","previous_headings":"Table construction > Linking observations and segments","what":"Relating observations and segments in practice","title":"`dsm` data formatting example","text":"methods building observation table. ’ll illustrate one assigning detections segments based distance (.e., detections associated closest segment centroid). appropriate don’t see forwards far, ’s unlikely detections miss assigned. true, instance, aerial survey observers look windows located sides plane might inappropriate shipboard survey observers flying bridge can see way ahead ship. First loading detection data survey (use fit detection function, post-processing): can see columns latitude longitude (Lat Long). can make data.frame sf object telling sf columns contain spatial coordinates (sf lingo, “geometry”): can overlay previous map transects:  Going back segs object , can use st_join function, combined st_nearest_feature function control tables linked. , need project data avoid issues. Now obs acquired additional columns segs, including Sample.Label (want) Effort (don’t). check worked okay, can plot obs segs_lines (line version segments saved earlier) colour coding Sample.Label. randomized colour order easier tell observations misallocated.  segs can remove unnecessary columns geometry get required columns : (Note since binned data, just keep Bin column need process later.) different way approach timestamps waypoints GPS, related segments. One look whether observation made start end times segment.","code":"obs <- read.csv(\"data/loons.csv\") head(obs) ##         Date     Time Bin      Lat      Long Observer object size ## 1 2011-04-19 18991230   B 40.85296 -71.45511       JV     46    1 ## 2 2011-02-17 18991230   A 40.87022 -71.54842       JV    106    1 ## 3 2011-05-02 18991230   A 40.87595 -71.30304       KW    121    1 ## 4 2011-12-04 18991230   C 40.91753 -71.38273       KW    286    1 ## 5 2011-02-23 18991230   A 40.94153 -71.38517       KW    446    1 ## 6 2011-02-07 18991230   A 40.94225 -71.46438       KW    457    1 obs <- st_as_sf(obs, coords=c(\"Long\", \"Lat\")) # set the coordinate system st_crs(obs) <- 4326 obs ## Simple feature collection with 941 features and 6 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size                   geometry ## 1  2011-04-19 18991230   B       JV     46    1 POINT (-71.45511 40.85296) ## 2  2011-02-17 18991230   A       JV    106    1 POINT (-71.54842 40.87022) ## 3  2011-05-02 18991230   A       KW    121    1 POINT (-71.30304 40.87595) ## 4  2011-12-04 18991230   C       KW    286    1 POINT (-71.38273 40.91753) ## 5  2011-02-23 18991230   A       KW    446    1 POINT (-71.38517 40.94153) ## 6  2011-02-07 18991230   A       KW    457    1 POINT (-71.46438 40.94225) ## 7  2011-04-19 18991230   A       JV    500    2 POINT (-71.59495 40.94761) ## 8  2011-05-02 18991230   A       KW    506    1   POINT (-71.552 40.94795) ## 9  2011-02-23 18991230   B       JV    814    1 POINT (-71.51172 40.97912) ## 10 2011-04-19 18991230   A       KW    911    1 POINT (-71.59762 40.98571) p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=transects) +   geom_sf(data=obs, size=0.5) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   # make the plot look simpler   theme_minimal() print(p) # project segs segs <- st_transform(segs, 6348) obs <- st_transform(obs, 6348)  # do the join obs <- st_join(obs, segs, join=st_nearest_feature)   # project back to lat/long segs <- st_transform(segs, 4326) obs <- st_transform(obs, 4326)  # how does that look? obs ## Simple feature collection with 941 features and 9 fields ## Geometry type: POINT ## Dimension:     XY ## Bounding box:  xmin: -71.86962 ymin: 40.85296 xmax: -70.89175 ymax: 41.46472 ## Geodetic CRS:  WGS 84 ## First 10 features: ##          Date     Time Bin Observer object size Transect       Effort ## 1  2011-04-19 18991230   B       JV     46    1        8 1969.873 [m] ## 2  2011-02-17 18991230   A       JV    106    1       26 1894.100 [m] ## 3  2011-05-02 18991230   A       KW    121    1       12 1988.809 [m] ## 4  2011-12-04 18991230   C       KW    286    1       10 1979.630 [m] ## 5  2011-02-23 18991230   A       KW    446    1       10 1979.624 [m] ## 6  2011-02-07 18991230   A       KW    457    1        8 1969.839 [m] ## 7  2011-04-19 18991230   A       JV    500    2       24 1923.218 [m] ## 8  2011-05-02 18991230   A       KW    506    1       26 1894.075 [m] ## 9  2011-02-23 18991230   B       JV    814    1        7 2000.139 [m] ## 10 2011-04-19 18991230   A       KW    911    1       24 1923.199 [m] ##     Sample.Label                   geometry ## 1   2010-RI-8-29 POINT (-71.45511 40.85296) ## 2  2010-RI-26-16 POINT (-71.54842 40.87022) ## 3  2010-RI-12-31 POINT (-71.30304 40.87595) ## 4  2010-RI-10-27 POINT (-71.38273 40.91753) ## 5  2010-RI-10-26 POINT (-71.38517 40.94153) ## 6   2010-RI-8-24 POINT (-71.46438 40.94225) ## 7  2010-RI-24-12 POINT (-71.59495 40.94761) ## 8  2010-RI-26-12   POINT (-71.552 40.94795) ## 9   2010-RI-7-22 POINT (-71.51172 40.97912) ## 10  2010-RI-24-9 POINT (-71.59762 40.98571) # make a random colour palette to avoid similar colours being near each other pal <- rainbow(nrow(segs), s=.6, v=.9)[sample(1:nrow(segs),nrow(segs))] p <- ggplot() +   # geom_sf knows what to do with spatial data   geom_sf(data=coastline) +   geom_sf(data=segs_lines, aes(colour=Sample.Label), pch=21) +   geom_sf(data=obs, size=0.5, aes(colour=Sample.Label)) +   # chop to be on a better scale   coord_sf(xlim=c(-72, -70.8), ylim=c(40.8, 41.5), expand=FALSE) +   scale_colour_manual(values=pal) +   # make the plot look simpler   theme_minimal() +   theme(legend.position = \"none\") print(p) # get rid of \"spatialness\" obs <- st_drop_geometry(obs) # select the columns we need obs <- obs[, c(\"object\", \"Sample.Label\", \"size\", \"Bin\")] head(obs) ##   object  Sample.Label size Bin ## 1     46  2010-RI-8-29    1   B ## 2    106 2010-RI-26-16    1   A ## 3    121 2010-RI-12-31    1   A ## 4    286 2010-RI-10-27    1   C ## 5    446 2010-RI-10-26    1   A ## 6    457  2010-RI-8-24    1   A"},{"path":"/articles/data_format/dsm-data-formatting.html","id":"more-information","dir":"Articles > Data_format","previous_headings":"","what":"More information","title":"`dsm` data formatting example","text":"provided information segmenting wiki page, part US Navy-funded project DenMod. information summarized ?\"dsm-data\" dsm package. may also useful look data example dataset mexdolphins can loaded data(mexdolphins). vignette analysis data available . hard give general information segment lines, extent depends data originally formatted (going way back make model GPS unit used). information dealing spatial data R using sf package available R spatial website (see “Articles” drop header). Another source help distance sampling mailing list. sure search archives prior posting several threads segmenting previously might helpful.","code":""},{"path":"/articles/data_format/dsm-data-formatting.html","id":"acknowledgements","dir":"Articles > Data_format","previous_headings":"","what":"Acknowledgements","title":"`dsm` data formatting example","text":"Thanks Phil Bouchet providing helpful comments early version document Iúri Correia finding important bug.","code":""},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"introduction","dir":"Articles > Lines_gomex","previous_headings":"","what":"Introduction","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"analysis based dataset observations pantropical dolphins Gulf Mexico (shipped Distance 6.0 later). convenience data bundled R-friendly format, although code necessary creating data Distance project files available github. OBIS-SEAMAP page data may found SEFSC GoMex Oceanic 1996 survey page. intention highlight features dsm package, rather perform full analysis data. reason, important steps fully explored. familiarity density surface modelling (Miller, Burt, Rexstad, & Thomas, 2013) (Hedley & Buckland, 2004) assumed.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"preamble","dir":"Articles > Lines_gomex","previous_headings":"","what":"Preamble","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"start, load dsm package (dependencies) set options: order run vignette, ’ll need install R packages. can done via following call install.packages:","code":"library(dsm) library(ggplot2)  # plotting options gg.opts <- theme(panel.grid.major=element_blank(),                  panel.grid.minor=element_blank(),                  panel.background=element_blank()) install.packages(c(\"dsm\", \"Distance\", \"knitr\", \"distill\", \"ggplot2\", \"rgdal\",                    \"maptools\", \"plyr\", \"tweedie\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"the-data","dir":"Articles > Lines_gomex","previous_headings":"","what":"The data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data need included dsm package, two additional objects needed plotting required can downloaded put directory file. data can loaded R using following code: add objects survey.area pred.polys environment.","code":"load(\"mexdolphins-extra.rda\")"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"observation-and-segment-data","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Observation and segment data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data analysis nicely pre-formatted shipped dsm. Loading data, can see four data.frames, first lines shown: segdata holds segment data: transects already “chopped” segments. distdata holds distance sampling data used fit detection function. obsdata links distance data segments. preddata holds prediction grid (includes necessary covariates). Typically (.e. datasets) necessary divide transects segments, allocate observations correct segments using GIS similar package1, starting analysis using dsm.","code":"data(mexdolphins) head(segdata) ##   longitude latitude        x        y Effort Transect.Label Sample.Label depth ## 1 -86.92712 29.94378 836105.9 -1011416  13800       19960417   19960417-1 135.0 ## 2 -86.83176 29.84030 846012.9 -1021407  14000       19960417   19960417-2 147.7 ## 3 -86.74445 29.75279 855022.6 -1029785  14000       19960417   19960417-3 152.1 ## 4 -86.65230 29.65522 864610.3 -1039168  13900       19960417   19960417-4 163.8 ## 5 -86.56648 29.56088 873598.1 -1048266  13800       19960417   19960417-5 179.7 ## 6 -86.49290 29.49000 881203.7 -1055004  13800       19960417   19960417-6 188.5 head(distdata) ##     object size  distance Effort detected beaufort latitude longitude ## 45      45   21 3296.6363  36300        1        4 27.72872 -86.00159 ## 61      61  150  929.1937  17800        1        4 25.99896 -87.62712 ## 63      63  125 6051.0009  21000        1        2 26.00693 -87.94881 ## 85      85   75 5499.6971  21800        1        1 27.50344 -90.44891 ## 114    114   50 7258.9837  13400        1        3 27.40568 -94.99483 ## 120    120   45 1454.7962  20900        1        5 26.01765 -95.97449 ##              x        y ## 45  948000.065 -1236192 ## 61  812161.653 -1436899 ## 63  780969.520 -1438985 ## 85  528656.807 -1297833 ## 114  95910.149 -1324562 ## 120   2477.665 -1473909 head(obsdata) ##     object Sample.Label size  distance Effort ## 45      45   19960421-9   21 3296.6363  36300 ## 61      61   19960423-7  150  929.1937  17800 ## 63      63   19960423-9  125 6051.0009  21000 ## 85      85   19960427-1   75 5499.6971  21800 ## 114    114   19960430-8   50 7258.9837  13400 ## 120    120   19960501-5   45 1454.7962  20900 head(preddata) ##   latitude longitude        x          y depth      area ## 1 30.08333 -87.58333 774402.9 -1002759.1    35 271236913 ## 2 30.08333 -87.41667 789688.6 -1001264.5    30 271236913 ## 3 30.08333 -87.25000 804971.3  -999740.6    27 271236913 ## 4 30.08333 -87.08333 820251.1  -998187.5    22 271236913 ## 5 30.08333 -86.91667 835528.0  -996605.2    46 271236913 ## 6 29.91667 -87.75000 760783.1 -1021810.3    14 271236913"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"shapefiles-and-converting-units","dir":"Articles > Lines_gomex","previous_headings":"The data","what":"Shapefiles and converting units","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often data spatial analysis comes many different sources. important ensure measurements used analysis compatible units, otherwise resulting estimates incorrect hard interpret. measurements SI units outset removes need conversion later, making life much easier. data already appropriate units (Northings Eastings: kilometres centroid, projected using North American Lambert Conformal Conic projection). extensive literature particular projections latitude longitude appropriate highly recommend reader review particular study area; (Bivand, Pebesma, & Gómez-Rubio, 2008) good starting point. data frames already measurements appropriately converted. convention directions named x y. Using latitude longitude performing spatial smoothing can problematic certain smoother bases used. particular bivariate isotropic bases used non-isotropic nature latitude longitude inconsistent (moving one degree one direction moving one degree ). give example projecting polygon defines survey area (simply read R using readShapeSpatial shapefile produced GIS). Next bit code works study.area outline, massaging use soap film smoother. using soap film smoothers, code extraneous. code generates plot, shows survey area transect lines overlaid (using data segdata).  Also note since projected prediction grid, “squares” look quite like squares. plotting use polygons saved, polygons (stored pred.polys) read shapefile created GIS, object class SpatialPolygons sp package. needs project proper plotting using functions sf package. predsf <- st_as_sf(pred.polys)  Tips plotting polygons available ggplot2 pkg_down.","code":"library(sf) library(plyr)  # tell R that the survey.area object is currently in lat/long sp::proj4string(survey.area) <- sp::CRS(\"+proj=longlat +datum=WGS84\")  predsf <- st_as_sf(pred.polys)  area.sf <- st_as_sf(survey.area) st_crs(area.sf) <- \"WGS84\" area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))  # Convert preddata to a spatial object preddata_sf <- st_as_sf(preddata, coords=c(\"x\", \"y\")) st_crs(preddata_sf) <- st_crs(area.sf.proj) # Perform the intersection preddata_sf <- st_intersection(preddata_sf, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries coords_preddata <- data.frame(st_coordinates(preddata_sf))  preddata_sf$x <- coords_preddata$X preddata_sf$y <- coords_preddata$Y # proj 4 string # using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/ lcc_proj4 <- sp::CRS(\"+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs \")  # project using LCC survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)  # simplify the object survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords) names(survey.area) <- c(\"x\", \"y\") segdata_sf <- st_as_sf(segdata, coords = c(\"x\",\"y\")) st_crs(segdata_sf) <- st_crs(area.sf.proj) # study area outline and segment centres plot_segments <- ggplot() +   geom_sf(data = area.sf.proj, fill=\"lightblue\", color = \"blue\", linewidth=.1) +   geom_sf(data=segdata_sf, fill=NA, color=\"black\", linewidth=.3) +   labs(title=\"1996 SE Fisheries Science Center Gulf of Mexico cruise\",        subtitle = \"Points are segment centres\") +   scale_fill_viridis_c(option = \"magma\", guide = \"none\") plot_segments predsf <- st_as_sf(pred.polys) # plot as projected plot(st_geometry(predsf), axes=TRUE)"},{"path":[]},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"distance-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Distance data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top panels EDA plots show histograms observed distances cluster size, bottom panels show relationship observed distance observed cluster size, relationship observed distance Beaufort sea state. plots show relationship cluster size observed distance (fewer smaller clusters seem seen larger distances). following code generates EDA plots:  Top row, left right: histograms distance cluster size; bottom row: plot distance cluster size plot distances Beaufort sea state.","code":"par(mfrow=c(2,2))  # histograms hist(distdata$distance,main=\"\",xlab=\"Distance (m)\") hist(distdata$size,main=\"\",xlab=\"Cluster size\")  # plots of distance vs. cluster size plot(distdata$distance, distdata$size, main=\"\", xlab=\"Distance (m)\",      ylab=\"Group size\", pch=19, cex=0.5, col=gray(0.7))  # lm fit l.dat <- data.frame(distance=seq(0,8000,len=1000)) lo <- lm(size~distance, data=distdata) lines(l.dat$distance, as.vector(predict(lo,l.dat)))  plot(distdata$distance,distdata$beaufort, main=\"\", xlab=\"Distance (m)\",      ylab=\"Beaufort sea state\", pch=19, cex=0.5, col=gray(0.7))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-data","dir":"Articles > Lines_gomex","previous_headings":"Exploratory data analysis","what":"Spatial data","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Looking separately spatial data without thinking distances, can plot observed group sizes space (point size proportional group size observation). Circle size indicates size group observation. rather large areas observations, might cause variance estimates abundance rather large. also see depth data use depth later explanatory covariate spatial model.","code":"prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid) cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries depth <- ggplot() +          geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +          labs(title = \"Spotted dolphins, Gulf of Mexico\",               subtitle = \"Depth in meters, size of detected dolphin groups\") +          xlab(\"Longitude\") + ylab(\"Latitude\") +          geom_point(aes(x, y, size=size), data=distdata, colour=\"red\",alpha=I(0.5)) +          scale_fill_viridis_c(option = \"viridis\", direction = 1) depth"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"estimating-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"","what":"Estimating the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"use ds function package Distance fit detection function. (Distance package intended make standard distance sampling R relatively straightforward. flexible complex alternative, see function ddf mrds library.) First, loading Distance library: can fit detection function hazard-rate key adjustment terms: Calling summary gives us information parameter estimates, probability detection, AIC, etc: following code generates plot fitted detection function (left) quantile-quantile plot (right):  quantile-quantile plot show relatively good goodness fit hazard-rate detection function.","code":"library(Distance) detfc.hr.null <- ds(distdata, max(distdata$distance), key=\"hr\", adjustment=NULL) ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). ## Warning in ddf.ds(dsmodel = dsmodel, data = data, meta.data = meta.data, : ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). ## Warning in ds(distdata, max(distdata$distance), key = \"hr\", adjustment = NULL): ## Estimated hazard-rate scale parameter close to 0 (on log scale). Possible ## problem in data (e.g., spike near zero distance). summary(detfc.hr.null) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  841.2528  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##             estimate        se ## (Intercept)  7.98244 0.9533779 ##  ## Shape coefficient(s):   ##             estimate        se ## (Intercept)        0 0.7833406 ##  ##                       Estimate         SE        CV ## Average p            0.5912252  0.2224714 0.3762887 ## N in covered region 79.4959326 30.8184416 0.3876732 plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2) gof_ds(detfc.hr.null) ##  ## Goodness of fit results for ddf object ##  ## Distance sampling Cramer-von Mises test (unweighted) ## Test statistic = 0.0972004 p-value = 0.598774"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-covariates-to-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Estimating the detection function","what":"Adding covariates to the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"common include covariates detection function (-called Multiple Covariate Distance Sampling MCDS). dataset two covariates collected individual: Beaufort sea state size. brevity fit hazard-rate detection functions sea state included factor covariate follows: looking summary, detection function covariates give lower AIC model without covariates (843.71 vs. 841.25 hazard-rate model without covariates). Looking back bottom-right panel EDA plots, can see discernible pattern plot Beaufort vs distance. brevity, detection function model selection omitted . practise fit many different forms detection function (select model based goodness fit testing AIC).","code":"detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),                   key=\"hr\", adjustment=NULL) summary(detfc.hr.beau) ##  ## Summary for distance analysis  ## Number of observations :  47  ## Distance range         :  0  -  7847.467  ##  ## Model       : Hazard-rate key function  ## AIC         :  843.7115  ## Optimisation:  mrds (nlminb)  ##  ## Detection function parameters ## Scale coefficient(s):   ##                         estimate        se ## (Intercept)           7.66291936  1.078972 ## as.factor(beaufort)2  2.24809809 14.754677 ## as.factor(beaufort)3  0.28581820  1.020809 ## as.factor(beaufort)4  0.07133776  1.225360 ## as.factor(beaufort)5 -0.36421115  1.540324 ##  ## Shape coefficient(s):   ##              estimate        se ## (Intercept) 0.2994644 0.5184204 ##  ##                       Estimate         SE        CV ## Average p            0.5420677  0.1753555 0.3234937 ## N in covered region 86.7050383 29.4646397 0.3398262"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"fitting-a-dsm","dir":"Articles > Lines_gomex","previous_headings":"","what":"Fitting a DSM","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"fitting dsm model, data must segmented; consists chopping transects attributing counts segments. mentioned , data already segmented.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"a-simple-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"A simple model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"begin simple model. assume number individuals segment quasi-Poisson distributed smooth function spatial coordinates (note formula exactly one specify gam mgcv). setting group=TRUE, abundance clusters/groups rather individuals can estimated (though ignore ). Note set method=\"REML\" ensure smooth terms estimated reliably. Running model: can obtain summary fitted model: exact interpretation model summary results can found (S. N. Wood, 2017); can see various information smooth components fitted general model statistics. can use deviance explained compare models2. can also get rough idea smooth space looks like using vis.gam (white/yellow indicates high values, red low indicates low values):  type=\"response\" argument ensures plot scale abundance values relative (offsets set median values). means plot useful get idea general shape smooth interpreted directly.","code":"dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)   -18.20       0.53  -34.34   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##         edf Ref.df     F  p-value     ## s(x,y) 24.8  27.49 2.354 0.000733 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.121   Deviance explained = 43.4% ## -REML = 936.04  Scale est. = 94.367    n = 387 vis.gam(dsm.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"adding-another-environmental-covariate-to-the-spatial-model","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Adding another environmental covariate to the spatial model","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"data set also contains depth covariate (plotted ). can include model simply: see drop deviance explained, perhaps model useful first. discuss setting k parameter Model checking, . Setting select=TRUE (argument gam) impose extra shrinkage terms smooth model (allowing smooth terms removed model fitting; see ?gam information). particularly useful , include . However many environmental predictors model can good way (along looking \\(p\\)-values) perform term selection. Simply calling plot model object allows us look relationship depth linear predictor:  Omitting argument select call plot plot smooth terms, one time.","code":"dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method=\"REML\") summary(dsm.xy.depth) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, k = 10) + s(depth, k = 20) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.740      1.236  -15.16   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F p-value   ## s(x,y)   6.062  7.371 0.923  0.5038   ## s(depth) 9.443 11.466 1.585  0.0824 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0909   Deviance explained = 34.3% ## -REML = 939.52  Scale est. = 124.9     n = 387 plot(dsm.xy.depth, select=2)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"spatial-models-when-there-are-covariates-in-the-detection-function","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Spatial models when there are covariates in the detection function","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"code fit DSM covariates detection function similar models, . However since detection function observation-level covariates, must estimate abundance per segment using Horvitz-Thompson-like estimator modelling, change response abundance.est: can see, summary results rather similar: resulting spatial smooth (though resulting surface somewhat “amplified”):","code":"dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method=\"REML\") summary(dsm.est.xy) ##  ## Family: quasipoisson  ## Link function: log  ##  ## Formula: ## abundance.est ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -18.0233     0.4739  -38.03   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value     ## s(x,y) 24.64  27.44 2.349 0.00029 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.129   Deviance explained = 42.2% ## -REML = 1043.6  Scale est. = 174.1     n = 387 vis.gam(dsm.est.xy, plot.type=\"contour\", view=c(\"x\",\"y\"), asp=1, type=\"response\", zlim=c(0, 300), contour.col=\"black\", n.grid=100)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-response-distributions","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other response distributions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Often quasi-Poisson distribution doesn’t give adequate flexibility capture overdispersion response data (see Model checking Model selection ), illustrate two additional distributions can used count data. models section, ’ll move back count response, though estimated abundance also work.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"tweedie","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Tweedie","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Response distributions quasi-Poisson can used, example Tweedie distribution. Tweedie distribution available dsm setting family=tw().","code":"dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method=\"REML\") summary(dsm.xy.tweedie) ##  ## Family: Tweedie(p=1.347)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept) -17.2640     0.2363  -73.07   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##          edf Ref.df     F p-value   ## s(x,y) 12.82  17.13 1.628  0.0562 . ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0684   Deviance explained = 26.8% ## -REML = 351.49  Scale est. = 60.26     n = 387"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"negative-binomial","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other response distributions","what":"Negative binomial","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Though used , similarly, two options negative binomial distribution: negbin nb. former requires user specification single parameter theta range values parameter (specified vector), latter estimates value theta model fitting process (generally faster). latter recommended users.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"other-spatial-modelling-options","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM","what":"Other spatial modelling options","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"large literature spatial modelling using GAMs, much can harnessed DSM context. highlights.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"soap-film-smoothing","dir":"Articles > Lines_gomex","previous_headings":"Fitting a DSM > Other spatial modelling options","what":"Soap film smoothing","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"account complex region (e.g., region includes peninsulae) can use soap film smoother (Simon N. Wood, Bravington, & Hedley, 2008). use soap film smoother spatial part model must create set knots smoother use. easily done using make.soapgrid() function dsm: second argument specifies number points (direction) grid used create knots (knots grid outside survey.area removed). saw exploratory analysis, transect lines outside survey area. cause soap film smoother fail, remove : Note soap_checker script available can useful ensuring boundary, data knots correct format use soap film smoother. can run model depth covariate along spatial (soap film) smooth. Note k argument now refers complexity boundary smooth soap film, complexity film controlled knots given xt argument.","code":"soap.knots <- make.soapgrid(survey.area,c(15,10)) x <- segdata$x; y<-segdata$y onoff <- inSide(x=x,y=y, bnd=as.list(survey.area)) rm(x,y) segdata.soap <- segdata[onoff,] dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs=\"so\", k=15, xt=list(bnd=list(survey.area))) +             s(depth),           family=tw(), method=\"REML\",           detfc.hr.null, segdata.soap, obsdata, knots=soap.knots) summary(dsm.xy.tweedie.soap) ##  ## Family: Tweedie(p=1.356)  ## Link function: log  ##  ## Formula: ## count ~ s(x, y, bs = \"so\", k = 15, xt = list(bnd = list(survey.area))) +  ##     s(depth) + offset(off.set) ##  ## Parametric coefficients: ##             Estimate Std. Error t value Pr(>|t|)     ## (Intercept)  -18.018      0.405  -44.49   <2e-16 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Approximate significance of smooth terms: ##            edf Ref.df     F  p-value     ## s(x,y)   3.050 72.000 0.064 0.114944     ## s(depth) 5.135  6.196 3.895 0.000793 *** ## --- ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## R-sq.(adj) =  0.0653   Deviance explained = 33.1% ## -REML = 339.26  Scale est. = 54.022    n = 365"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-checking","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model checking","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Fitting models well good, ’d like confirm models reasonable; dsm provides functions model checking. can use gam.check generate diagnostic plots:  show deviation Q-Q plot. “line” points plot residuals vs. linear predictor plot corresponds zeros data. Note well plots, gam.check also produces information model fitting. particular interest us last lines tell us basis size. k parameter provided s (te) terms dsm controls complexity smooths model. setting k parameter specify largest complexity smooth term model; long high enough, can sure enough flexibility. output gam.check , can see “p-value” calculated size basis, can good guide whether basis size needs increased. ?choose.k manual page mgcv gives guidance technical details matter. can look model form Tweedie distribution specified response:  Q-Q plot now seems much better (closer \\(y=x\\) line). plots histogram residuals rather hard interpret due large number zeros data. guidance interpreting gam.check output can found (S. N. Wood, 2017).","code":"gam.check(dsm.xy) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 6 iterations. ## Gradient range [-7.105956e-08,-9.826305e-09] ## (score 936.0363 & scale 94.3674). ## Hessian positive definite, eigenvalue range [5.800328,192.5304]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 24.8    1.01    0.99 gam.check(dsm.xy.tweedie) ##  ## Method: REML   Optimizer: outer newton ## full convergence after 8 iterations. ## Gradient range [-1.513175e-06,3.981813e-06] ## (score 351.486 & scale 60.26034). ## Hessian positive definite, eigenvalue range [0.6230615,103.5094]. ## Model rank =  30 / 30  ##  ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ##  ##          k'  edf k-index p-value ## s(x,y) 29.0 12.8    0.73    0.34"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"randomised-quantile-residuals","dir":"Articles > Lines_gomex","previous_headings":"Model checking","what":"Randomised quantile residuals","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"top right panel gam.check plots residuals vs. linear predictor plot includes odd line predictions. artifact link function, showing exact zeros data. can misleading distracting, making difficult see whether residuals show heteroskedasticity. Randomised quantile residuals (Dunn & Smyth, 1996) avoid issue transforming residuals exactly normally distributed. makes residuals vs. linear predictor plot much easier interpret therefore doesn’t include artifacts generated link function. plots can produced using rqgam.check dsm:  can see issue heteroskedasticity (increase spread residuals vs. linear predictor plot increasing values linear predictor). One can also plot residuals covariate values check pattern residuals. Note general, plots “Resids vs. linear pred.” interpreted caution output rqgam.check residuals generated normal construction (example Q-Q plot histogram residuals always look fine).","code":"rqgam_check(dsm.xy.tweedie)"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"model-selection","dir":"Articles > Lines_gomex","previous_headings":"","what":"Model selection","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Assuming models “passed” checks gam.check, rqgam.check sufficiently flexible, may left choice model “best”. several methods choosing best model – AIC, REML/GCV scores, deviance explained, full cross-validation test data . Though document intend full analysis pantropical dolphin data, can create results table compare various models fitted far terms abundance estimates associated uncertainties. can use resulting data.frame build table results using kable function:","code":"# make a data.frame to print out mod_results <- data.frame(\"Model name\" = c(\"`dsm.xy`\", \"`dsm.xy.depth`\", \"`dsm.xy.tweedie`\", \"`dsm.xy.tweedie.soap`\",                                            \"`dsm.est.xy`\"),                           \"Description\" = c(\"Bivariate smooth of location, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, quasipoisson\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie\",                                             \"Soap film smooth of location, smooth of depth, Tweedie\",                                             \"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function\"),                           \"Deviance explained\" = c(unlist(lapply(list(dsm.xy,                                                                       dsm.xy.depth,                                                                       dsm.xy.tweedie,                                                                       dsm.xy.tweedie.soap),                  function(x){paste0(round(summary(x)$dev.expl*100,2),\"%\")})),NA)) kable(mod_results, col.names=c(\"Model name\", \"Description\", \"Deviance explained\"))"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"abundance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Abundance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"model checked selected, can make predictions grid calculate abundance. offset stored area column3. Given predicted abundance estimates cell prediction grid, connected prediction grid object mapping:  can calculate abundance survey area simply summing predictions: can compare plot predictions dsm.xy.depth:  can see inclusion depth model noticeable effect distribution (note difference legend scale two plots). can also look total abundance: see much change abundance, terms abundance alone isn’t much two models. Next ’ll go look variance next can see bigger differences models.","code":"dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area) prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000)) prediction_grid_sf <- st_sf(geometry = prediction_grid)  preddata_sf$Prediction_xy <- dsm.xy.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.pred) ## [1] 28462.22 dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)  preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries pred <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, abundance estimates\",        subtitle = \"Bivariate smooth of location, smooth of depth, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) pred sum(dsm.xy.depth.pred) ## [1] 27084.54"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"variance-estimation","dir":"Articles > Lines_gomex","previous_headings":"","what":"Variance estimation","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Obviously point estimates abundance important, also calculate uncertainty around abundance estimates. Fortunately dsm provides functions perform calculations display resulting uncertainty estimates. two approaches estimating uncertainty abundance estimate dsm. dsm_var_gam assumes spatial model detection function parts model independent . case squared coefficients variation model component added. dsm_var_prop takes account fact detection probability may correlated spatial part model. uses methods described (Bravington, Miller, & Hedley, 2021). dsm_var_prop can applied covariate detection function varies level segments, recorded segment (example Beaufort). don’t situation , opt dsm_var_gam. methods estimate variance abundance element list provided pred.data. case wish obtain abundance prediction cells, use split chop data set list elements give dsm_var_gam (dsm_var_prop). Calling summary give information uncertainty estimation: can also make plot CVs (transect lines observations overlaid) using following code.  Note increase CV away transect lines. can revisit model included depth location smooths observe coefficient variation model larger model location smooth.","code":"preddata.var <- split(preddata, 1:nrow(preddata)) dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,                           off.set=preddata$area) summary(dsm.xy.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 16091.72 28430.76 50231.32  ## (Using log-Normal approximation) ##  ## Point estimate                 : 28430.76  ## CV of detection function       : 0.2471842  ## CV from GAM                    : 0.164  ## Total standard error           : 8433.356  ## Total coefficient of variation : 0.2966 preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy  cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature) cropped_grid <- st_intersection(cropped_grid, area.sf.proj) ## Warning: attribute variables are assumed to be spatially constant throughout ## all geometries CV <- ggplot() +   geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +   geom_sf(data=segdata_sf, fill=NA, color=\"white\", linewidth=.001) +   labs(title=\"Spotted dolphins, Gulf of Mexico, uncertainty (CV)\",        subtitle = \"Bivariate smooth of location, quasipoisson\") +   scale_fill_viridis_c(option = \"viridis\", direction = 1) CV dsm.xy.depth.var <- dsm_var_gam(dsm.xy.depth, pred.data=preddata.var,                                 off.set=preddata$area) summary(dsm.xy.depth.var) ## Summary of uncertainty in a density surface model calculated ##  analytically for GAM, with delta method ##  ## Approximate asymptotic confidence interval: ##     2.5%     Mean    97.5%  ## 14377.57 27054.60 50909.23  ## (Using log-Normal approximation) ##  ## Point estimate                 : 27054.6  ## CV of detection function       : 0.2816462  ## CV from GAM                    : 0.1741  ## Total standard error           : 8958.444  ## Total coefficient of variation : 0.3311"},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"conclusions","dir":"Articles > Lines_gomex","previous_headings":"","what":"Conclusions","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"document outlined analysis spatially-explicit distance sampling data using dsm package. Note many possible models can fitted using dsm aim show just options. Results models can rather different, care must taken performing model selection, discrimination criticism.","code":""},{"path":"/articles/lines_gomex/mexico-analysis.html","id":"software","dir":"Articles > Lines_gomex","previous_headings":"","what":"Software","title":"Example `dsm` analysis: pantropical dolphins in the Gulf of Mexico","text":"Distance available http://github.com/DistanceDevelopment/Distance well CRAN. dsm available http://github.com/DistanceDevelopment/dsm, well CRAN.","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"David L. Miller. Author. Eric Rexstad. Contributor. Louise Burt. Contributor. Mark V. Bravington. Contributor. Sharon Hedley. Contributor. Megan Ferguson. Contributor. Natalie Kelly. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller DL (2024). dsm: Density Surface Modelling Distance Sampling Data. R package version 2.3.3.9001, https://github.com/DistanceDevelopment/dsm.","code":"@Manual{,   title = {dsm: Density Surface Modelling of Distance Sampling Data},   author = {David L. Miller},   year = {2024},   note = {R package version 2.3.3.9001},   url = {https://github.com/DistanceDevelopment/dsm}, }"},{"path":[]},{"path":"/index.html","id":"what-is-dsm","dir":"","previous_headings":"","what":"Density Surface Modelling of Distance Sampling Data","title":"Density Surface Modelling of Distance Sampling Data","text":"dsm provides Generalized Additive Model (GAM)-based approach calculate spatially-explicit estimates animal abundance distance sampling (also presence/absence strip transect) data. Several utility functions provided model checking, plotting variance estimation. Open Access paper describing methods: Miller, DL, ML Burt, EA Rexstad L Thomas (2013). Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution. https://doi.org/10.1111/2041-210X.12105","code":""},{"path":"/index.html","id":"using-dsm","dir":"","previous_headings":"","what":"Using dsm","title":"Density Surface Modelling of Distance Sampling Data","text":"Examples use dsm see https://distancesampling.org/dsm/articles/data_format/dsm-data-formatting.html https://distancesampling.org/dsm/articles/lines_gomex/mexico-analysis.html","code":""},{"path":"/reference/block.info.per.su.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the block information — block.info.per.su","title":"Find the block information — block.info.per.su","text":"Takes transect data works many blocks given size (segment terms) fit .","code":""},{"path":"/reference/block.info.per.su.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the block information — block.info.per.su","text":"","code":"block.info.per.su(block.size, data, name.su)"},{"path":"/reference/block.info.per.su.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the block information — block.info.per.su","text":"block.size number segments per block data data used build model name.su names sampling units (.e., transects)","code":""},{"path":"/reference/block.info.per.su.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the block information — block.info.per.su","text":"data.frame following columns name        sample unit name (e.g. transect label) num.seg     number segments transect num.block   number blocks available start.block block number first block end.block   block number last block num.req     number blocks needed unit","code":""},{"path":"/reference/check.cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Check column names exist — check.cols","title":"Check column names exist — check.cols","text":"Internal function check supplied data.frames correct columns checks sample labels unique.","code":""},{"path":"/reference/check.cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check column names exist — check.cols","text":"","code":"check.cols(ddf.obj, segment.data, observation.data, segment.area)"},{"path":"/reference/check.cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check column names exist — check.cols","text":"ddf.obj ddf object mrds segment.data segment data defined dsm observation.data observation data defined dsm segment.area area segments","code":""},{"path":"/reference/check.cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check column names exist — check.cols","text":"nothing, throws error something went wrong","code":""},{"path":"/reference/check.cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check column names exist — check.cols","text":"David Lawrence Miller","code":""},{"path":"/reference/dsm-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Data format for DSM — dsm-data","title":"Data format for DSM — dsm-data","text":"Two data.frames must provided dsm. referred observation.data segment.data.","code":""},{"path":"/reference/dsm-data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data format for DSM — dsm-data","text":"segment.data table sample identifiers define segments, corresponding effort (line length) expended environmental covariates used model abundance/density. observation.data provides link table observations used detection function samples (segments), can aggregate observations segments (.e., observation.data \"look-table\" observations segments). observation.data - observation data.frame must (least) following columns: object unique object identifier Sample.Label identifier segment observation occurred size size observed group (e.g., 1 animals occurred individually) distance distance observation One can often also use observation.data fit detection function (additional columns detection function covariates allowed table). segment.data: segment data.frame must (least) following columns: Effort effort (terms length segment) Sample.Label identifier segment (unique!) ??? environmental covariates, example location (projected latitude longitude), relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).","code":""},{"path":"/reference/dsm-data.html","id":"multiple-detection-functions","dir":"Reference","previous_headings":"","what":"Multiple detection functions","title":"Data format for DSM — dsm-data","text":"multiple detection functions used, column named ddfobj must included observation.data segment.data. lets model know detection function observation . numeric ordered ddf.obj argument dsm, e.g., ddf.obj=list(ship_ddf, aerial_ddf) means ship detections ddfobj=1 aerial detections ddfobj=2 observation data.","code":""},{"path":"/reference/dsm-data.html","id":"mark-recapture-distance-sampling-models","dir":"Reference","previous_headings":"","what":"Mark-recapture distance sampling models","title":"Data format for DSM — dsm-data","text":"using mrds models include mark-recapture components (currently independent observer trial modes supported) format observation data needs checked ensure observations duplicated. observer column also required observation.data. Independent observer mode unique observations (unique object IDs) required. Trial mode observations made observer 1 required.","code":""},{"path":"/reference/dsm-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Density surface modelling — dsm-package","title":"Density surface modelling — dsm-package","text":"dsm implements spatial models distance sampling data. Models detectability can fitted using packages mrds Distance. dsm fits generalized additive models spatially-referenced data. See Miller et al (2013) introduction.","code":""},{"path":"/reference/dsm-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density surface modelling — dsm-package","text":"information distance sampling methods example code available http://distancesampling.org/R/. help distance sampling package, Google Group https://groups.google.com/forum/#!forum/distance-sampling. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density surface modelling — dsm-package","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm.cor","title":"Check for autocorrelation in residuals — dsm.cor","text":"function deprecated, use dsm_cor.","code":""},{"path":"/reference/dsm.cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm.cor","text":"","code":"dsm.cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm.cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm.cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Fits density surface model (DSM) detection adjusted counts spatially-referenced distance sampling analysis. dsm takes observations animals, allocates segments line (strip transects) optionally adjusts counts based detectability using supplied detection function model. generalized additive model, generalized mixed model generalized linear model used model adjusted counts based formula involving environmental covariates.","code":""},{"path":"/reference/dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"dsm(   formula,   ddf.obj,   segment.data,   observation.data,   engine = \"gam\",   convert.units = 1,   family = quasipoisson(link = \"log\"),   group = FALSE,   control = list(keepData = TRUE),   availability = 1,   segment.area = NULL,   weights = NULL,   method = \"REML\",   ... )"},{"path":"/reference/dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"formula formula surface. valid formula. See \"Details\", , define response. ddf.obj result call ddf ds. multiple detection functions required list can provided. strip/circle transects assumed objects observed, see dummy_ddf. Mark-recapture distance sampling (mrds) models type io (independent observers) trial allowed. segment.data segment data, see dsm-data. observation.data observation data, see dsm-data. engine fitting engine used DSM (\"glm\"/\"gam\"/\"gamm\"/\"bam\"). convert.units conversion factor multiply area segments . See 'Units' . family response distribution (popular choices include quasipoisson, Tweedie/tw negbin/nb). Defaults quasipoisson. group TRUE abundance groups calculated rather abundance individuals. Setting option TRUE equivalent setting size group 1. control usual control argument gam; keepData must TRUE variance estimation work (though option set GLMs GAMMs). availability estimate availability bias. count models used multiply effective strip width (must vector length 1 length number rows segment.data); estimated abundance/estimated density models used scale response (must vector length 1 length number rows observation.data). Uncertainty availability handled present. segment.area NULL (default) segment areas calculated multiplying Effort column segment.data (right minus left) truncation distance ddf.obj strip.width. Alternatively vector segment areas can provided (must length number rows segment.data) character string giving name column segment.data contains areas. segment.area specified takes precedent. weights weights observation used model fitting. default, weights=NULL, weights observation area (see Details). Setting scalar value (e.g., weights=1) observations equally weighted. method smoothing parameter estimation method. Default \"REML\", using Restricted Maximum Likelihood. See gam options. Ignored engine=\"glm\". ... anything else passed straight glm, gam, gamm bam.","code":""},{"path":"/reference/dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"glm, gam, gamm bam object, additional element, $ddf holds detection function object.","code":""},{"path":"/reference/dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"response (LHS formula) can one following (restrictions outlined ): count count segment abundance.est estimated abundance per segment, estimation via Horvitz-Thompson estimator density.est density per segment offset used model dependent response: count area segment multiplied average probability detection segment abundance.est area segment density zero count response can used detection function covariates vary segments/points (within). example, weather conditions (like visibility sea state) foliage cover usually acceptable change within segment, animal sex behaviour work. abundance.est response can used covariates detection function. density case, observations can weighted segment areas via weights= argument. default (weights=NULL), density estimated weights set segment areas (using segment.area calculated detection function object metadata Effort data). Alternatively weights=1 set weights equal. third alternative pass vector length equal number segments, containing appropriate weights. example analyses available http://examples.distancesampling.org.","code":""},{"path":"/reference/dsm.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"often case distances collected metres segment lengths recorded kilometres. dsm allows provide conversion factor (convert.units) multiply areas . example: distances metres segment lengths kilometres setting convert.units=1000 lead analysis metres. Setting convert.units=1/1000 lead analysis kilometres. conversion factor applied segment.area specified.","code":""},{"path":"/reference/dsm.html","id":"large-models","dir":"Reference","previous_headings":"","what":"Large models","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"large models, engine=\"bam\" method=\"fREML\" may useful. Models specified bam gam. Read bam using option; option considered EXPERIMENTAL moment. particular note default basis choice (thin plate regression splines) slow general fitting less stable using gam. negative binomial response, theta must specified using bam.","code":""},{"path":"/reference/dsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"Hedley, S. S. T. Buckland. 2004. Spatial models line transect sampling. JABES 9:181-199. Miller, D. L., Burt, M. L., Rexstad, E. ., Thomas, L. (2013), Spatial models distance sampling data: recent developments future directions. Methods Ecology Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access) Wood, S.N. 2006. Generalized Additive Models: Introduction R. CRC/Chapman & Hall.","code":""},{"path":"/reference/dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"David L. Miller","code":""},{"path":"/reference/dsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a density surface model to segment-specific estimates of abundance or density. — dsm","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) summary(mod1)  # predict over a grid mod1.pred <- predict(mod1, preddata, preddata$area)  # calculate the predicted abundance over the grid sum(mod1.pred)  # plot the smooth plot(mod1) } # }"},{"path":"/reference/dsm.var.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm.var.gam","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"function deprecated, use dsm_var_gam.","code":""},{"path":"/reference/dsm.var.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"","code":"dsm.var.gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm.var.gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm.var.movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"function deprecated, use dsm_var_movblk.","code":""},{"path":"/reference/dsm.var.movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"","code":"dsm.var.movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm.var.movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm.var.movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL – file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm.var.prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm.var.prop","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"function deprecated, use dsm_var_prop.","code":""},{"path":"/reference/dsm.var.prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"","code":"dsm.var.prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm.var.prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm.var.prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for autocorrelation in residuals — dsm_cor","title":"Check for autocorrelation in residuals — dsm_cor","text":"DSM fitted data, function can used check autocorrelation residuals.","code":""},{"path":"/reference/dsm_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"dsm_cor(   dsm.obj,   Transect.Label = \"Transect.Label\",   Segment.Label = \"Segment.Label\",   max.lag = 10,   resid.type = \"scaled.pearson\",   fun = cor,   ylim = c(0, 1),   subset = \"all\",   ... )"},{"path":"/reference/dsm_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for autocorrelation in residuals — dsm_cor","text":"dsm.obj fitted dsm object. Transect.Label label transect (default: Transect.Label). Using different labels can useful transects split geographical features transects surveyed multiple times. Segment.Label label segments (default: Segment.Label).result calling order must make sense. max.lag maximum lag calculate . resid.type type residuals used, see residuals.gam. Defaults \"scaled.pearson\" GAM case \"normalized\" GAMM case (equivalent). fun function use, default cor, must take two column vectors arguments. ylim user defined limits y direction. subset subset data correlation function calculated ? ... options pass plot.","code":""},{"path":"/reference/dsm_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for autocorrelation in residuals — dsm_cor","text":"plot vector fun applied lags.","code":""},{"path":"/reference/dsm_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for autocorrelation in residuals — dsm_cor","text":"Within Transect.Label, segments sorted according Segment.Labels. may require time get right particular data. one multiple surveys transects revisited, example, one may want make Transect.Label unique transect-survey identifier. Neither label need included model, must just present $data field model. usually means segment data passed dsm. current iteration function plot correlations nicely, things can get function return data (assigning result object). NA values residuals correlogram calculated. usually occurs due NA values covariates (smoother fitted values ). Code like (.na(dsm.obj$data)) might helpful.","code":""},{"path":"/reference/dsm_cor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for autocorrelation in residuals — dsm_cor","text":"David L. Miller","code":""},{"path":"/reference/dsm_cor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for autocorrelation in residuals — dsm_cor","text":"","code":"# \\donttest{  library(Distance) #>  #> Attaching package: 'Distance' #> The following object is masked from 'package:dsm': #>  #>     dummy_ddf #> The following object is masked from 'package:mrds': #>  #>     create.bins  library(dsm)   # load the data, see ?mexdolphins  data(mexdolphins)   # fit a model  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.433 #> No survey area information supplied, only estimating detection function.  mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) #> Warning: Some observations are outside of detection function 1 truncation!   # look at lag 1 differences up to a maximum of lag 9, using deviance  # residuals  dsm_cor(mod1, resid.type=\"deviance\", max.lag=9,          Segment.Label=\"Sample.Label\")  # }"},{"path":"/reference/dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance propagation for density surface models — dsm_varprop","title":"Variance propagation for density surface models — dsm_varprop","text":"Calculate uncertainty predictions fitted DSM, including uncertainty detection function.","code":""},{"path":"/reference/dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance propagation for density surface models — dsm_varprop","text":"","code":"dsm_varprop(   model,   newdata = NULL,   trace = FALSE,   var.type = \"Vp\",   var_type = NULL )"},{"path":"/reference/dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance propagation for density surface models — dsm_varprop","text":"model fitted dsm. newdata prediction grid. Set NULL avoid making predictions just return model objects. trace debugging, see scale parameter estimation going. var.type variance-covariance matrix used (\"Vp\" variance-covariance conditional smoothing parameter(s), \"Vc\" unconditional). See gamObject details/explanation. doubt, stick default, \"Vp\". var_type deprecated, use var.type instead.","code":""},{"path":"/reference/dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance propagation for density surface models — dsm_varprop","text":"list elements: old_model fitted model supplied function model refit refitted model object, extra term pred point estimates predictions newdata var total variance calculated newdata ses standard error prediction cell newdata newdata=NULL last three entries NA.","code":""},{"path":"/reference/dsm_varprop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance propagation for density surface models — dsm_varprop","text":"make predictions spatial model, also want know uncertainty abundance estimate. Since density surface models 2 () stage models, need incorporate uncertainty earlier stages (.e. detection function) \"final\" uncertainty estimate. function refit spatial model include Hessian offset extra term. Variance estimates using new model can used calculate variance predicted abundance estimates incorporate detection function uncertainty. Importantly requires detection function covariates, vary within segment (, example covariates like sex used). information construct prediction grid data.frame, newdata, see predict.dsm. routine useful detection function covariates used DSM. Note can use var.type=\"Vc\" (see gamObject), variance-covariance matrix spatial model, corrected smoothing parameter uncertainty. See Wood, Pya & S\\\"afken (2016) information. Models fixed scale parameters (e.g., negative binomial) require extra round optimisation.","code":""},{"path":"/reference/dsm_varprop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Variance propagation for density surface models — dsm_varprop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_varprop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance propagation for density surface models — dsm_varprop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535. Wood, S.N., Pya, N. S\\\"afken, B. (2016) Smoothing parameter model selection general smooth models. Journal American Statistical Association, 1-45.","code":""},{"path":"/reference/dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance propagation for density surface models — dsm_varprop","text":"David L. Miller, based code Mark V. Bravington Sharon L. Hedley.","code":""},{"path":"/reference/dsm_var_gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance estimation assuming independence — dsm_var_gam","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"one willing assume detection function spatial model independent, function produce estimates variance predictions abundance, using result squared coefficients variation add.","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"dsm_var_gam(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"list elements model fitted model object pred.var variance regions given pred.data. bootstrap logical, always FALSE model fitted model extra term dsm.object original model (dsm.obj )","code":""},{"path":"/reference/dsm_var_gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"David L. Miller","code":""},{"path":"/reference/dsm_var_gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prediction variance estimation assuming independence — dsm_var_gam","text":"","code":"if (FALSE) { # \\dontrun{  library(Distance)  library(dsm)   # load the Gulf of Mexico dolphin data (see ?mexdolphins)  data(mexdolphins)   # fit a detection function and look at the summary  hr.model <- ds(distdata, truncation=6000,                 key = \"hr\", adjustment = NULL)  summary(hr.model)   # fit a simple smooth of x and y  mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)   # Calculate the variance  # this will give a summary over the whole area in mexdolphins$preddata  mod1.var <- dsm_var_gam(mod1, preddata, off.set=preddata$area) } # }"},{"path":"/reference/dsm_var_movblk.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Estimate variance abundance area using moving block bootstrap. Two procedures implemented, one incorporating detection function uncertainty, one .","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"dsm_var_movblk(   dsm.object,   pred.data,   n.boot,   block.size,   off.set,   ds.uncertainty = FALSE,   samp.unit.name = \"Transect.Label\",   progress.file = NULL,   bs.file = NULL,   bar = TRUE )"},{"path":"/reference/dsm_var_movblk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"dsm.object object returned dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. n.boot number bootstrap resamples. block.size number segments block. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. ds.uncertainty incorporate uncertainty detection function? See Details, . Note feature EXPERIMENTAL moment. samp.unit.name name sampling unit resample (default 'Transect.Label'). progress.file path file used (usually Distance) generate progress bar (default NULL – file written). bs.file path file store bootstrap round. stores bootstrap results rather just summaries, enabling outliers detected removed. (Default NULL). bar progress bar printed screen? (Default TRUE).","code":""},{"path":"/reference/dsm_var_movblk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"Setting ds.uncertainty=TRUE incorporate detection function uncertainty directly bootstrap. done generating observations fitted detection function re-fitting new detection function (form), calculating new effective strip width. Rejection sampling used generate observations (except half-normal case) procedure can rather slow. Note currently supported covariates detection function. Setting ds.uncertainty=FALSE incorporate detection function uncertainty using delta method. assumes detection function spatial model INDEPENDENT. probably reasonable.","code":""},{"path":"/reference/dsm_var_movblk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance estimation via parametric moving block bootstrap — dsm_var_movblk","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) summary(hr.model)  # fit a simple smooth of x and y mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata) summary(mod1)  # calculate the variance by 500 moving block bootstraps mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,    block.size = 3, samp.unit.name = \"Transect.Label\",    off.set = preddata$area,    bar = TRUE, bs.file = \"mexico-bs.csv\", ds.uncertainty = TRUE) } # }"},{"path":"/reference/dsm_var_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction variance propagation for DSMs — dsm_var_prop","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"ensure uncertainty detection function correctly propagated final variance estimate abundance, function uses method first detailed Williams et al (2011), explanation given Bravington et al. (2021).","code":""},{"path":"/reference/dsm_var_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"","code":"dsm_var_prop(   dsm.obj,   pred.data,   off.set,   seglen.varname = \"Effort\",   type.pred = \"response\" )"},{"path":"/reference/dsm_var_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"dsm.obj model object fitted dsm. pred.data either: single prediction grid list prediction grids. grid data.frame columns original data. .set vector list vectors many elements pred.data. vector long number rows corresponding element pred.data. give area associated prediction cell. single number supplied replicated length pred.data. seglen.varname name column holds segment length (default value \"Effort\"). type.pred predictions \"response\" \"link\" scale? (default \"response\").","code":""},{"path":"/reference/dsm_var_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"list elements model fitted model object pred.var variance region given pred.data bootstrap logical, always FALSE pred.data .set model fitted model extra term dsm.object original model, model.check simple check subtracting coefficients two models see large difference deriv numerically calculated Hessian offset","code":""},{"path":"/reference/dsm_var_prop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"idea refit spatial model including extra random effect. random effect zero mean hence effect point estimates. variance Hessian detection function. Variance estimates incorporate detection function uncertainty. mathematical details given paper references . Many prediction grids can supplied supplying list data.frames function. Note routine simply calls dsm_varprop. require multiple prediction grids, routine probably faster. routine useful detection function covariates used DSM.","code":""},{"path":"/reference/dsm_var_prop.html","id":"diagnostics","dir":"Reference","previous_headings":"","what":"Diagnostics","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"summary output function includes simply diagnostic shows average probability detection \"original\" fitted model (model supplied function; column Fitted.model) probability detection refitted model (used variance propagation; column Refitted.model) along standard error probability detection fitted model (Fitted.model.se), unique values factor covariates used detection function (continuous covariates 5%, 50% 95% quantiles shown). large differences probabilities detection potentially problems fitted model, variance propagation . can fitted model account enough variability data refitting variance model accounts random effect.","code":""},{"path":"/reference/dsm_var_prop.html","id":"limitations","dir":"Reference","previous_headings":"","what":"Limitations","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Note routine useful detection function used DSM. used abundance.est density.est responses used. Importantly requires detection function covariates, vary within segment (, example covariates like sex used).","code":""},{"path":"/reference/dsm_var_prop.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation Density Surface Models. Journal Agricultural, Biological Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2 Williams, R., Hedley, S.L., Branch, T.., Bravington, M.V., Zerbini, .N. Findlay, K.P. (2011). Chilean Blue Whales Case Study Illustrate Methods Estimate Abundance Evaluate Conservation Status Rare Species. Conservation Biology 25(3), 526-535.","code":""},{"path":"/reference/dsm_var_prop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction variance propagation for DSMs — dsm_var_prop","text":"Mark V. Bravington, Sharon L. Hedley. Bugs added David L. Miller.","code":""},{"path":"/reference/dummy_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection function objects when detection is certain — dummy_ddf","title":"Detection function objects when detection is certain — dummy_ddf","text":"Create detection function object strip/plot surveys use density surface models.","code":""},{"path":"/reference/dummy_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection function objects when detection is certain — dummy_ddf","text":"","code":"dummy_ddf(object, size = 1, width, left = 0, transect = \"line\")"},{"path":"/reference/dummy_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection function objects when detection is certain — dummy_ddf","text":"object numeric vector object identifiers, relating object field observation data DSM. size group size observation (default groups size 1) width right truncation left left truncation (default 0, left truncation) transect \"line\" \"point\" transect","code":""},{"path":"/reference/dummy_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection function objects when detection is certain — dummy_ddf","text":"David L Miller","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data from a fitted detection function — generate.ds.uncertainty","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"using dsm.var.movblk ds.uncertainty=TRUE, procedure generates data fitted detection function (assuming correct).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"","code":"generate.ds.uncertainty(ds.object)"},{"path":"/reference/generate.ds.uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"ds.object fitted detection function object (returned call ddf.ds).","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"function changes random number generator seed. avoid potential side-effects, use something like: seed <- get(\".Random.seed\",envir=.GlobalEnv) running code assign(\".Random.seed\",seed,envir=.GlobalEnv) .","code":""},{"path":"/reference/generate.ds.uncertainty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data from a fitted detection function — generate.ds.uncertainty","text":"David L. Miller","code":""},{"path":"/reference/generate.mb.sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Moving block bootstrap sampler — generate.mb.sample","title":"Moving block bootstrap sampler — generate.mb.sample","text":"usually used , called within dsm.var.movblk.","code":""},{"path":"/reference/generate.mb.sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Moving block bootstrap sampler — generate.mb.sample","text":"","code":"generate.mb.sample(   num.blocks.required,   block.size,   which.blocks,   dsm.data,   unit.info,   n.units )"},{"path":"/reference/generate.mb.sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Moving block bootstrap sampler — generate.mb.sample","text":"num.blocks.required number blocks need. block.size number segments per block. .blocks blocks sampled. dsm.data $data element result call dsm. unit.info result calling block.info.per.su. n.units number sampling units.","code":""},{"path":"/reference/generate.mb.sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Moving block bootstrap sampler — generate.mb.sample","text":"vector log-residuals","code":""},{"path":"/reference/latlong2km.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert latitude and longitude to Northings and Eastings — latlong2km","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Convert longitude latitude co-ordinates kilometres west-east south-north axes (lon0,lat0) using \"spherical law cosines\".","code":""},{"path":"/reference/latlong2km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"","code":"latlong2km(lon, lat, lon0 = sum(range(lon))/2, lat0 = sum(range(lat))/2)"},{"path":"/reference/latlong2km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"lon longitude lat latitude lon0 longitude reference point (defaults mean longitude) lat0 latitude reference point (defaults mean latitude)","code":""},{"path":"/reference/latlong2km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"list elements km.e km.n.","code":""},{"path":"/reference/latlong2km.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"WARNING: approximate procedure converting latitude/ longitude Northing/Easting. Consider using projection conversions available packages sp, sf rgdal better results.","code":""},{"path":"/reference/latlong2km.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert latitude and longitude to Northings and Eastings — latlong2km","text":"Simon N. Wood","code":""},{"path":"/reference/make.soapgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"routine simply creates grid knots (correct format) used \"internal\" part soap film smoother","code":""},{"path":"/reference/make.soapgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"","code":"make.soapgrid(bnd, n.grid)"},{"path":"/reference/make.soapgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"bnd list elements x y give locations boundary vertices. first last elements . n.grid either one number giving number points along x y axes used create grid, vector giving number x direction, y direction.","code":""},{"path":"/reference/make.soapgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"list elements x y, containing knot locations.","code":""},{"path":"/reference/make.soapgrid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a knot grid for the internal part of a soap film smoother. — make.soapgrid","text":"David L Miller","code":""},{"path":"/reference/mexdolphins.html","id":null,"dir":"Reference","previous_headings":"","what":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Data combination several NOAA shipboard surveys conducted pan-tropical spotted dolphins Gulf Mexico. data consist 47 observations groups dolphins. group size recorded, well Beaufort sea state time observation. Coordinates observation bathymetry data also available covariates analysis. complete example analysis (description data) provided http://distancesampling.org/R/vignettes/mexico-analysis.html.","code":""},{"path":"/reference/mexdolphins.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pan-tropical spotted dolphins in the Gulf of Mexico — mexdolphins","text":"Halpin, P.N., .J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, . Dimatteo, J. Cleary, C. Good, L.B. Crowder, K.D. Hyrenbach. 2009. OBIS-SEAMAP: world data center marine mammal, sea bird, sea turtle distributions. Oceanography 22(2):104-115 NOAA Southeast Fisheries Science Center. 1996. Report Cetacean Survey Oceanic Selected Continental Shelf Waters Northern Gulf Mexico aboard NOAA Ship Oregon II (Cruise 220)","code":""},{"path":"/reference/obs_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Observed versus expected diagnostics for fitted DSMs — obs_exp","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"Given covariate, calculate observed expected counts unique value covariate. can useful goodness fit check DSMs.","code":""},{"path":"/reference/obs_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"obs_exp(model, covar, cut = NULL)"},{"path":"/reference/obs_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"model fitted dsm model object covar covariate aggregate (character) cut vector cut points aggregate . supplied, unique values covar used.","code":""},{"path":"/reference/obs_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"data.frame values observed expected counts.","code":""},{"path":"/reference/obs_exp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"One strategy model checking calculate observed expected counts different aggregations variable. match well model fit good.","code":""},{"path":"/reference/obs_exp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"David L Miller, suggestion Mark Bravington.","code":""},{"path":"/reference/obs_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Observed versus expected diagnostics for fitted DSMs — obs_exp","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # example with the Gulf of Mexico dolphin data data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata) } # }"},{"path":"/reference/plot.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a density surface model. — plot.dsm","title":"Plot a density surface model. — plot.dsm","text":"See plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a density surface model. — plot.dsm","text":"","code":"# S3 method for class 'dsm' plot(x, ...)"},{"path":"/reference/plot.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a density surface model. — plot.dsm","text":"x model fitted dsm ... arguments passed plot.gam.","code":""},{"path":"/reference/plot.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a density surface model. — plot.dsm","text":"plot!","code":""},{"path":[]},{"path":"/reference/plot.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a density surface model. — plot.dsm","text":"David L. Miller","code":""},{"path":"/reference/plot.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Create plots of abundance uncertainty — plot.dsm.var","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"Note prediction data set must x y columns even used model.","code":""},{"path":"/reference/plot.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"","code":"# S3 method for class 'dsm.var' plot(   x,   poly = NULL,   limits = NULL,   breaks = NULL,   legend.breaks = NULL,   xlab = \"x\",   ylab = \"y\",   observations = TRUE,   plot = TRUE,   boxplot.coef = 1.5,   x.name = \"x\",   y.name = \"y\",   gg.grad = NULL,   ... )"},{"path":"/reference/plot.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"x dsm.var object poly list data.frame columns x y, gives coordinates polygon draw. may also optionally column group, many polygons. limits limits fill colours breaks breaks colour fill legend.breaks breaks displayed xlab label x axis ylab label y axis observations observations plotted? plot actually plot map, just return ggplot2 object? boxplot.coef control trimming (summary.dsm.var), effect bootstrap file saved. x.name name variable plot x axis. y.name name variable plot y axis. gg.grad optional ggplot gradient object. ... arguments","code":""},{"path":"/reference/plot.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"plot","code":""},{"path":"/reference/plot.dsm.var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"order get plotting work dsm_var_prop dsm_var_gam, one must first format data correctly since functions designed compute general summaries. One summary calculated element list pred supplied dsm_var_prop dsm_var_gam. plot uncertainty prediction grid, pred (data.frame), say, can create correct format simply using pred.new <- split(pred,1:nrow(pred)).","code":""},{"path":[]},{"path":"/reference/plot.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create plots of abundance uncertainty — plot.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/plot_pred_by_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially plot predictions per model term — plot_pred_by_term","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"Plot effect smooth model spatially. term model, plot effect space. Plots made scale, relative influence smooth can seen.","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"plot_pred_by_term(dsm.obj, data, location.cov = c(\"x\", \"y\"))"},{"path":"/reference/plot_pred_by_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"dsm.obj fitted dsm object data data use plot (often prediction grid), data also include width height columns plotting location.cov deprecated, use location.cov","code":""},{"path":"/reference/plot_pred_by_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"ggplot2 plot","code":""},{"path":"/reference/plot_pred_by_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"David L Miller (idea taken inlabru)","code":""},{"path":"/reference/plot_pred_by_term.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially plot predictions per model term — plot_pred_by_term","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data and fit a model data(mexdolphins) hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) mod1 <- dsm(count~s(x,y) + s(depth), hr.model, segdata, obsdata)  preddata$width <- preddata$height <- sqrt(preddata$area)  # make the plot plot_pred_by_term(mod1, preddata, c(\"x\",\"y\"))  # better plot would be # library(viridis) # plot_pred_by_term(mod1, preddata, c(\"x\",\"y\")) + scale_fill_viridis() } # }"},{"path":"/reference/predict.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from a fitted density surface model — predict.dsm","title":"Predict from a fitted density surface model — predict.dsm","text":"Make predictions density abundance outside (inside) covered area.","code":""},{"path":"/reference/predict.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from a fitted density surface model — predict.dsm","text":"","code":"# S3 method for class 'dsm' predict(object, newdata = NULL, off.set = NULL, type = \"response\", ...)"},{"path":"/reference/predict.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from a fitted density surface model — predict.dsm","text":"object fitted dsm object newdata spatially referenced covariates e.g. altitude, depth, distance shore, etc. Covariates data.frame must names identical variable names used fitting model .set area cells prediction grid. units segments/distances given dsm. Replaces column newdata called .set supplied. Ignored newdata supplied type scale results . default \"response\", see predict.gam explanation options (usually necessary) ... arguments passed predict.gam","code":""},{"path":"/reference/predict.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from a fitted density surface model — predict.dsm","text":"predicted values response scale default (unless type specified, case see predict.gam).","code":""},{"path":"/reference/predict.dsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict from a fitted density surface model — predict.dsm","text":"newdata supplied, predictions made data built model. Note order results necessarily segdata (segment data) data.frame supplied dsm. area .set used argument supplied, otherwise look areas column named .set newdata. Either way link function (usually log) applied offsets, need log passing function.","code":""},{"path":[]},{"path":"/reference/predict.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict from a fitted density surface model — predict.dsm","text":"David L. Miller","code":""},{"path":"/reference/predict.fake_ddf.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for fake detection functions — predict.fake_ddf","title":"Prediction for fake detection functions — predict.fake_ddf","text":"Prediction function dummy detection functions. function returns many 1s rows newdata. esw=TRUE strip width returned.","code":""},{"path":"/reference/predict.fake_ddf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for fake detection functions — predict.fake_ddf","text":"","code":"# S3 method for class 'fake_ddf' predict(   object,   newdata = NULL,   compute = FALSE,   int.range = NULL,   esw = FALSE,   ... )"},{"path":"/reference/predict.fake_ddf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for fake detection functions — predict.fake_ddf","text":"object model object newdata many 1s return? compute unused, compatibility mrds::predict int.range unused, compatibility mrds::predict esw strip width returned? ... S3 consistency","code":""},{"path":"/reference/predict.fake_ddf.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Prediction for fake detection functions — predict.fake_ddf","text":"David L Miller","code":""},{"path":"/reference/print.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model object — print.dsm","title":"Print a description of a density surface model object — print.dsm","text":"method just gives short description fitted model. Use summary.dsm method information.","code":""},{"path":"/reference/print.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model object — print.dsm","text":"","code":"# S3 method for class 'dsm' print(x, ...)"},{"path":"/reference/print.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model object — print.dsm","text":"x model fitted dsm ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model object — print.dsm","text":"NULL","code":""},{"path":"/reference/print.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model object — print.dsm","text":"David L. Miller","code":""},{"path":"/reference/print.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm.var","title":"Print a description of a density surface model variance object — print.dsm.var","text":"method provides short summary, use summary.dsm.var method information.","code":""},{"path":"/reference/print.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm.var","text":"","code":"# S3 method for class 'dsm.var' print(x, ...)"},{"path":"/reference/print.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm.var","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a description of a density surface model variance object — print.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a description of a density surface model variance object — print.dsm_varprop","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"method provides short summary, see summary.dsm_varprop.","code":""},{"path":"/reference/print.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"","code":"# S3 method for class 'dsm_varprop' print(x, ...)"},{"path":"/reference/print.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"x dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":[]},{"path":"/reference/print.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print a description of a density surface model variance object — print.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm.var","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"See summary.dsm.var information.","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"","code":"# S3 method for class 'summary.dsm.var' print(x, ...)"},{"path":"/reference/print.summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of density surface model variance object — print.summary.dsm_varprop","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"See summary.dsm_varprop information.","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"","code":"# S3 method for class 'summary.dsm_varprop' print(x, ...)"},{"path":"/reference/print.summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"x summary dsm variance object ... unspecified unused arguments S3 consistency","code":""},{"path":"/reference/print.summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"NULL","code":""},{"path":[]},{"path":"/reference/print.summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Print summary of density surface model variance object — print.summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/rqgam.check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"function deprecated, use rqgam_check.","code":""},{"path":"/reference/rqgam.check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"","code":"rqgam.check(gam.obj, ...)"},{"path":"/reference/rqgam.check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam.check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Reproduces \"Resids vs. linear pred\" plot gam.check using randomised quantile residuals, la Dunn Smyth (1996). Checks heteroskedasticity usual, looking \"funnel\"-type structures points, much easier randomised quantile residuals deviance residuals, model uses count distribution response.","code":""},{"path":"/reference/rqgam_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"rqgam_check(gam.obj, ...)"},{"path":"/reference/rqgam_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"gam.obj gam, glm dsm object. ... arguments passed plotting functions","code":""},{"path":"/reference/rqgam_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"just plots!","code":""},{"path":"/reference/rqgam_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Note function works negative binomial Tweedie response distributions. Earlier versions function produced full gam.check output, confusing one plots really useful.  Checks k computed, need done using gam.check.","code":""},{"path":"/reference/rqgam_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"Based code Natalie Kelly, bugs added Dave Miller","code":""},{"path":"/reference/rqgam_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randomised quantile residuals check plot for GAMs/DSMs — rqgam_check","text":"","code":"# \\donttest{ library(Distance) library(dsm) library(tweedie)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL) #> Fitting hazard-rate key function #> AIC= 677.433 #> No survey area information supplied, only estimating detection function.  # fit a simple smooth of x and y with a Tweedie response with estimated #  p parameter mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw()) #> Warning: Some observations are outside of detection function 1 truncation! rqgam_check(mod1)  # }"},{"path":"/reference/summary.dsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted density surface model — summary.dsm","title":"Summarize a fitted density surface model — summary.dsm","text":"Gives brief summary fitted dsm object.","code":""},{"path":"/reference/summary.dsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted density surface model — summary.dsm","text":"","code":"# S3 method for class 'dsm' summary(object, ...)"},{"path":"/reference/summary.dsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted density surface model — summary.dsm","text":"object model fitted dsm ... arguments passed summary.gam","code":""},{"path":"/reference/summary.dsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted density surface model — summary.dsm","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize a fitted density surface model — summary.dsm","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm.var","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"Gives brief summary fitted dsm variance object.","code":""},{"path":"/reference/summary.dsm.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"","code":"# S3 method for class 'dsm.var' summary(   object,   alpha = 0.05,   boxplot.coef = 1.5,   bootstrap.subregions = NULL,   ... )"},{"path":"/reference/summary.dsm.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95\\ confidence intervals) boxplot.coef value coef used calculate outliers see boxplot. bootstrap.subregions list vectors logicals indices subregions variances need calculated (bootstraps (see dsm.var.prop use subregions variance propagation). ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm.var","text":"David L. Miller","code":""},{"path":"/reference/summary.dsm_varprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the variance of a density surface model — summary.dsm_varprop","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"Gives brief summary fitted dsm_varprop variance object.","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"","code":"# S3 method for class 'dsm_varprop' summary(object, alpha = 0.05, ...)"},{"path":"/reference/summary.dsm_varprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"object dsm.var object alpha alpha level confidence intervals (default 0.05 give 95% confidence internal) ... unused arguments S3 compatibility","code":""},{"path":"/reference/summary.dsm_varprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"summary object","code":""},{"path":[]},{"path":"/reference/summary.dsm_varprop.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summarize the variance of a density surface model — summary.dsm_varprop","text":"David L. Miller","code":""},{"path":"/reference/trim.var.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimmed variance — trim.var","title":"Trimmed variance — trim.var","text":"Trim variance estimates bootstrap. defined percentage defined amount necessary bring median trimmed mean within 8% defined 'outliers'.","code":""},{"path":"/reference/trim.var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimmed variance — trim.var","text":"","code":"trim.var(untrimmed.bootstraps, boxplot.coef = 1.5)"},{"path":"/reference/trim.var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimmed variance — trim.var","text":"untrimmed.bootstraps (usually $study.area.total element returned dsm.var.movblk bootstrap object. boxplot.coef value coef used calculate outliers see boxplot.","code":""},{"path":"/reference/trim.var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimmed variance — trim.var","text":"trimmed variance","code":""},{"path":"/reference/trim.var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trimmed variance — trim.var","text":"Louise Burt","code":""},{"path":"/reference/vis.concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis.concurvity","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"function deprecated, use vis_concurvity.","code":""},{"path":"/reference/vis.concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"","code":"vis.concurvity(model, type = \"estimate\")"},{"path":"/reference/vis.concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis.concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualise concurvity between terms in a GAM — vis_concurvity","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"Plot measures much one term model explained another. values high, one consider re-running variable selection one offending variables removed check stability term selection.","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"vis_concurvity(model, type = \"estimate\")"},{"path":"/reference/vis_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"model fitted model type concurvity measure plot, see concurvity","code":""},{"path":"/reference/vis_concurvity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"methods considered somewhat experimental time. Consult concurvity information concurvity measures calculated.","code":""},{"path":"/reference/vis_concurvity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"David L Miller","code":""},{"path":"/reference/vis_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualise concurvity between terms in a GAM — vis_concurvity","text":"","code":"if (FALSE) { # \\dontrun{ library(Distance) library(dsm)  # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins)  # fit a detection function and look at the summary hr.model <- ds(distdata, truncation=6000,                key = \"hr\", adjustment = NULL)  # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata)  # visualise concurvity using the \"estimate\" metric vis_concurvity(mod1) } # }"},{"path":"/news/index.html","id":"dsm-234","dir":"Changelog","previous_headings":"","what":"dsm 2.3.4","title":"dsm 2.3.4","text":"fix bug predictions NA (e.g., predictions outside soap boundary), variances NA due propagation (#4)","code":""},{"path":"/news/index.html","id":"dsm-233","dir":"Changelog","previous_headings":"","what":"dsm 2.3.3","title":"dsm 2.3.3","text":"CRAN release: 2022-08-20 dsm.var.prop -> dsm_var_prop dsm.var.gam -> dsm_var_gam dsm.var.movblk -> dsm_var_movblk dsm.cor -> dsm_cor rqgam.check -> rqgam_check vis.concurvity -> vis_concurvity var_type -> var.type","code":""},{"path":"/news/index.html","id":"dsm-232","dir":"Changelog","previous_headings":"","what":"dsm 2.3.2","title":"dsm 2.3.2","text":"CRAN release: 2022-03-17 fixed bug offset calculation dsm_varprop se estimation offsets logged twice. Thanks Megan Ferguson spotting . documentation now rmarkdown format dsm 2.3.1 transect type (line point) now determined detection function rather using transect= argument multiple detection functions now possible, use list() fitted detection functions supplied ddf.obj= argument dsm(). dsm.var.gam, dsm.var.prop dsm_varprop compatible. Vignette coming soon. Thanks Dave Fifield Ewan Wakefield extensive testing suggestions. See ?“dsm-data” information set data. strip transects now handled using dummy_ddf() function. presence~ response type removed Response options “D”, “density”, “Dhat”, “N”, “Nhat” “n” deprecated. Now “count”, “abundance.est” “density.est” allowed. Make predict.dsm work tibbles .set column newdata (error thrown previous code’s check ). Thanks Dave Fifield reporting issue. Fix bug inconsistent predictions density.est used response. Code predict.dsm simplified documentation updated. Thanks Dave Fifield reporting issue. mrds independent observer (io) trial models allowed detection function.Variance can estimated via dsm_varprop (usual limitation covariates may vary segment level) dsm.var.gam. feature currently experimental. Thanks Doug Sigourney Megan Ferguson help suggestions. See ?“dsm-data” information set data. example multiddf mrds analysis dsm https://github.com/densitymodelling/nefsc_fin_mrds_dsm availability= argument now works differently count models. availability must now length number segments (nrow(segment.data)) multiply offset (effective area) rather number animals observed coherent detectability handled case. dsm_varprop longer requies newdata supplied. newdata isn’t present just refitted original models returned, entries NA.","code":""},{"path":"/news/index.html","id":"dsm-230","dir":"Changelog","previous_headings":"","what":"dsm 2.3.0","title":"dsm 2.3.0","text":"CRAN release: 2020-04-22 fixed dsm_varprop (gam.fixed.priors) response distributions fixed scale parameters. Negative binomial results now may incorrect!","code":""},{"path":"/news/index.html","id":"dsm-2218","dir":"Changelog","previous_headings":"","what":"dsm 2.2.18","title":"dsm 2.2.18","text":"Clarification predict.dsm return documentation. Thanks Dave Fifield suggestion. .set=NULL lead predictions 0 density used. Now just returns density. Thanks Dave Fifield catch.","code":""},{"path":"/news/index.html","id":"dsm-2217","dir":"Changelog","previous_headings":"","what":"dsm 2.2.17","title":"dsm 2.2.17","text":"CRAN release: 2019-01-19 Now Suggests package “sp” (needed parts mexdolphin example data)","code":""},{"path":"/news/index.html","id":"dsm-2216","dir":"Changelog","previous_headings":"","what":"dsm 2.2.16","title":"dsm 2.2.16","text":"CRAN release: 2018-06-27 check Sample.Labels match , none throw error. Thanks Madhura Davate bringing issue attention. gamma option removed, since now ignored gam(). using method=“GCV.Cp” gamma=1.4 still used (generally advise using REML rather GCV smoothing parameter selection) dsm now requires mgcv version 1.8-23 higher. dsm.var.prop (dsm_varprop) now throw error covariates detection function can’t anything useful new function obs_exp diagnostic function compare observed expected counts different covariate aggregations","code":""},{"path":"/news/index.html","id":"dsm-2215","dir":"Changelog","previous_headings":"","what":"dsm 2.2.15","title":"dsm 2.2.15","text":"CRAN release: 2017-07-03 mexdolphins loaded using data(), data longer need attach()ed dsm.var.prop throws error count N used response. dsm.var.prop now calls dsm_varprop, keeps scale parameter constant refitting models variance propagation. dsm_varprop provides lower-level interface variance propagation probably faster flexible. add vis.concurvity visually assess concurvity (colinearity extended smooth terms) model new model diagnostic function: plot_pred_by_term – plots effect smooth model spatially. remove “abundance” option response, use “count” dsm.var.prop dsm_varprop better diagnostics checking variance model hasn’t done something weird dsm.var.movblk discards replicates model didn’t converge fix potential issue negative binomial response used variance propagation (due scale parameterisations). dsm_varprop now refits nb() models negbin() advise users look deeply. Thanks Eric Rexstad spotting . fix bug predict.dsm() predictions “density” response including offset. Thanks Len Thomas spotting .","code":""},{"path":"/news/index.html","id":"dsm-2214","dir":"Changelog","previous_headings":"","what":"dsm 2.2.14","title":"dsm 2.2.14","text":"CRAN release: 2017-01-26 nasty bug predict.dsm fixed: logging offset twice newdata provided, leading odd checking plots. also effected results predict() newdata= specified. Thanks Megan Ferguson alerting us . add support point transects(!) rqgam.check() now produced linear predictor vs. randomised quantile residuals plot avoid confusion interpreting plots REML now default method smoothing parameter selection","code":""},{"path":"/news/index.html","id":"dsm-2213","dir":"Changelog","previous_headings":"","what":"dsm 2.2.13","title":"dsm 2.2.13","text":"CRAN release: 2016-10-03 dsm.var.gam now example dsm.var.prop now checks detection function model covariates","code":""},{"path":"/news/index.html","id":"dsm-2212","dir":"Changelog","previous_headings":"","what":"dsm 2.2.12","title":"dsm 2.2.12","text":"CRAN release: 2016-08-10 alpha argument now works analytical variance reported using dsm.var.prop dsm.var.gam print.summary summary methods. Thanks Jason Roberts Eric Rexstad spotting issue. dsm now uses numDeriv::grad calculate derivatives variance estimation – much faster. Thanks Jason Roberts fix. dsm now requires mrds version 2.1.15 higher, due internal changes fixed issue Effort column required even segment.area specified. Thanks Greg Lollback spotting issue. ensured ordering segments matched ordering observations aggregation observations correct. Thanks Dave Fifield reporting bug suggesting fix. check.cols longer checks see column called “distance” strip transects used","code":""},{"path":"/news/index.html","id":"dsm-2211","dir":"Changelog","previous_headings":"","what":"dsm 2.2.11","title":"dsm 2.2.11","text":"dsm.var.prop uses update() rather messing around eval() print.summary.dsm.var didn’t work GAM variance detection function specified, now fixed (thanks Natalia Dellabianca spotting ) Removed non-working plot() code variance examples – see Mexico pantropical spotted dolphins proper treatment plotting using projections (http://distancesampling.org/R/vignettes/mexico-analysis.html) plot.var.gam longer crashes default distance data doesn’t columns “x” “y”","code":""},{"path":"/news/index.html","id":"dsm-2210","dir":"Changelog","previous_headings":"","what":"dsm 2.2.10","title":"dsm 2.2.10","text":"corrected offsets examples dsm, dsm.var.prop, dsm.var.movblk updated mexdolphins data lat/long reversal prediction data non-list storage stopped use rqgam.check used non-negbin/Tweedie responses (thanks Phil Bouchet highlighting issue).","code":""},{"path":"/news/index.html","id":"dsm-229","dir":"Changelog","previous_headings":"","what":"dsm 2.2.9","title":"dsm 2.2.9","text":"CRAN release: 2015-07-29 Moving block bootstrap now works detection function (suggested Laura Williamson) Mexico dolphin data areas prediction grids segments re-projected. Thanks Phil Bouchet pointing error. Context: https://groups.google.com/d/msg/distance-sampling/oj1j8mSmnTc/3cOAAJgBw9MJ","code":""},{"path":"/news/index.html","id":"dsm-228","dir":"Changelog","previous_headings":"","what":"dsm 2.2.8","title":"dsm 2.2.8","text":"Speed reduced memory usage variance calculations, thanks Filipe Dias asking . Better error observations matched detection function. Thanks Ricardo Lima highlighting . Check missing columns incorrect logic, now corrected tests. Thanks Julia Migné spotting .","code":""},{"path":"/news/index.html","id":"dsm-227","dir":"Changelog","previous_headings":"","what":"dsm 2.2.7","title":"dsm 2.2.7","text":"Plotting check plots using random quantile residuals (rqgam.check) didn’t work Tweedie tw family models, now fixed. Thanks Laura Mannocci Jason Roberts pointing . Plotting CV used matrix() coerce data data.frame – thus converting elements simplest type. Errors ensued. Thanks Adrian Schiavini finding bug!","code":""},{"path":"/news/index.html","id":"dsm-226","dir":"Changelog","previous_headings":"","what":"dsm 2.2.6","title":"dsm 2.2.6","text":"dsm.var.prop dsm.var.gam now allow supply single number offset, copy many times need. Thanks Filipe Dias suggesting .","code":""},{"path":"/news/index.html","id":"dsm-225","dir":"Changelog","previous_headings":"","what":"dsm 2.2.5","title":"dsm 2.2.5","text":"CRAN release: 2014-09-16 dsm.var.prop now throw error detection function dsm ’s trying calculate variance (thanks Adrián Schiavini finding ) variance/CV plotting threw error detection function supplied observation=TRUE. Now error sidestepped (, thanks Adrián Schiavini finding ).","code":""},{"path":"/news/index.html","id":"dsm-224","dir":"Changelog","previous_headings":"","what":"dsm 2.2.4","title":"dsm 2.2.4","text":"CRAN release: 2014-06-17 changed stop() warning() data detection function included observation data frame, automatically excluded later, bug found Eric Rexstad Anna Cucknell","code":""},{"path":"/news/index.html","id":"dsm-223","dir":"Changelog","previous_headings":"","what":"dsm 2.2.3","title":"dsm 2.2.3","text":"CRAN release: 2014-04-23 moving block boostrap using observed abundances residuals therefore gave wrong answers (Eric Rexstad found bug)","code":""},{"path":"/news/index.html","id":"dsm-222","dir":"Changelog","previous_headings":"","what":"dsm 2.2.2","title":"dsm 2.2.2","text":"version number reporting now works (Len Thomas found bug) documentation default density presence models now weighted area segment","code":""},{"path":"/news/index.html","id":"dsm-221","dir":"Changelog","previous_headings":"","what":"dsm 2.2.1","title":"dsm 2.2.1","text":"switch new testthat spec added new response variables (Dhat, density.est, count, n), suggestion Len Thomas","code":""},{"path":"/news/index.html","id":"dsm-214","dir":"Changelog","previous_headings":"","what":"dsm 2.1.4","title":"dsm 2.1.4","text":"check observed distances within width now throw error","code":""},{"path":"/news/index.html","id":"dsm-213","dir":"Changelog","previous_headings":"","what":"dsm 2.1.3","title":"dsm 2.1.3","text":"CRAN release: 2013-08-20 CRAN-compatibility fixes","code":""},{"path":"/news/index.html","id":"dsm-212","dir":"Changelog","previous_headings":"","what":"dsm 2.1.2","title":"dsm 2.1.2","text":"updates dsm.cor unified plotting variance estimation tweaks non-dsm models","code":""},{"path":"/news/index.html","id":"dsm-206","dir":"Changelog","previous_headings":"","what":"dsm 2.0.6","title":"dsm 2.0.6","text":"removed warning () comparison length make.soapgrid need pass bnd list() dsm.cor now handles gamm objects dsm.cor allows one check residual autocorrelation models presence/absence data now longer needs dummy detection function object","code":""},{"path":"/news/index.html","id":"dsm-205","dir":"Changelog","previous_headings":"","what":"dsm 2.0.5","title":"dsm 2.0.5","text":"started NEWS file fixed bug dsm.var.gam() didn’t return predictions plotting result fail silly typo docs check ’re using mgcv version >= 1.7-24 GAMM fitting warn ","code":""}]