resources.Rmd

---
title: "Additional Resources"
---

### Useful Books
James B. Grace.  2006.  Structural Equation Modeling and Natural Systems. Cambridge University Press. [<a href="http://www.amazon.com/Structural-Equation-Modeling-Natural-Systems/dp/0521546532/">amazon</a>]<br />
<br />
Ken A. Bollen. 1989. Structural Equations with Latent Variables. Wiley Press.[<a href="http://www.amazon.com/Structural-Equations-Latent-Variables-Kenneth/dp/0471011711/">amazon</a>]<br />
<br />
Rex B. Kline.  2010.  Principles and Practice of Structural Equation Modeling.  The Guilford Press. [<a href="http://www.amazon.com/Principles-Practice-Structural-Equation-Methodology/dp/1606238760/">amazon</a>]<br />
<br />
Bill Shipley.  2000. Cause and Correlation in Biology. Cambridge University Press. [<a href="http://www.amazon.com/Cause-Correlation-Biology-Structural-Equations/dp/0521529212/">amazon</a>]<br /><br />

Rick H. Holyle, ed. 2012.  Handbook of Structural Equation Modeling. The Guilford Press. [<a href="http://www.amazon.com/Handbook-Structural-Equation-Modeling-Hoyle/dp/1606230778/ref=sr_1_2?ie=UTF8&qid=1332966925&sr=8-2">amazon</a>]


### Packages We Use
[piecewiseSEM](http://jslefche.github.io/piecewiseSEM/)  
[lavaan](http://lavaan.ugent.be)  
[brms](https://github.com/paul-buerkner/brms)  
[DiagrammeR](http://visualizers.co/diagrammer/)  

### Mailing Lists
[sem for biology](https://groups.google.com/forum/#!forum/sem_for_biology) (for this class)  
[lavaan google group](https://groups.google.com/forum/#!forum/lavaan)  
[semnet](https://listserv.ua.edu/archives/semnet.html)  

### R
[R for Data Science](https://r4ds.had.co.nz) Wickham and Gromelund. Essential reading.  
[Getting used to R, RStudio, and R Markdown](https://ismayc.github.io/rbasics-book/index.html) 2017. Chester Ismay. The basics.  
[R Programming for Data Science](https://bookdown.org/rdpeng/rprogdatascience/). 2016. Roger D. Peng. Provides a more detailed intro to basic R programming.  
[Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/). 2016. Roger D. Peng. Uses the tidyverse and ggplot2 for data exploration. Great introduction to these packages and how they can be made to sing together.  
[Efficient R Programming](https://bookdown.org/csgillespie/efficientR/). 2016.  Colin Gillespe and Robin Lovelace\
[Statistical Inference for Data Science](https://leanpub.com/LittleInferenceBook/read). 2018. Brian Caffo. A wonderful book that is a companion to his Coursera course, but is open, and full of gret concepts and R examples.\
[Regression Models for Data Science in R](https://leanpub.com/regmods/read). 2018. Brian Caffo. A wonderful primer on regression models. Incredibly thorough.\
[Advanced R](http://adv-r.had.co.nz/). 2014. Great walkthrough of the details and guts of R. From novices to R wizards, you will learn things you never thought possible (or the actual reasoning behind that hacky stuff you've been doing for years).\
[Principles of Econometrics with  R](https://bookdown.org/ccolonescu/RPoE4/) 2016. Constantin Colonescu. Yes, it's econometrics, but there's a lot here that's very generalizable to biological data analysis in R as well.\
[A Tour of Time Series Analysis with R](http://tts.smac-group.com/)  
[Fundamentals of Data Visualization](http://serialmentor.com/dataviz/). 2018. Claus Wilke. A wonderful online collection of best principles and practices for data viz.  
[Forecasting: Principles and Practice](http://otexts.org/fpp2/). 2018. Rob Hyndman and George Athanasaopoules. A great intro to timeseries and forecasting in R.