update roxygen documentation to md sytax

DESCRIPTION

@@ -38,3 +38,4 @@ RoxygenNote: 7.3.2
 Encoding: UTF-8
 URL: https://github.com/openpharma/DoseFinding, https://openpharma.github.io/DoseFinding/
 BugReports: https://github.com/openpharma/DoseFinding/issues
+Roxygen: list(markdown = TRUE)

R/DoseFinding-package.R

@@ -6,40 +6,40 @@
 #' 
 #' @details
 #' The main functions are:\cr
-#' \bold{MCTtest}: Implements a multiple contrast tests\cr
-#' \bold{powMCT}: Power calculations for multiple contrast tests\cr
-#' \bold{fitMod}: Fits non-linear dose-response models\cr
-#' \bold{optDesign}: Calculates optimal designs for dose-response models\cr
-#' \bold{MCPMod}: Performs MCPMod methodology\cr
-#' \bold{sampSize}: General function for sample size calculation\cr
+#' **MCTtest**: Implements a multiple contrast tests\cr
+#' **powMCT**: Power calculations for multiple contrast tests\cr
+#' **fitMod**: Fits non-linear dose-response models\cr
+#' **optDesign**: Calculates optimal designs for dose-response models\cr
+#' **MCPMod**: Performs MCPMod methodology\cr
+#' **sampSize**: General function for sample size calculation\cr
 #' 
 #' @references Bornkamp, B., Bretz, F., Dette, H. and Pinheiro, J. C. (2011).
-#' Response-Adaptive Dose-Finding under model uncertainty, \emph{Annals of
-#' Applied Statistics}, \bold{5}, 1611--1631
+#' Response-Adaptive Dose-Finding under model uncertainty, *Annals of
+#' Applied Statistics*, **5**, 1611--1631
 #' 
 #' Bornkamp B., Pinheiro J. C., and Bretz, F. (2009). MCPMod: An R Package for
-#' the Design and Analysis of Dose-Finding Studies, \emph{Journal of
-#' Statistical Software}, \bold{29}(7), 1--23
+#' the Design and Analysis of Dose-Finding Studies, *Journal of
+#' Statistical Software*, **29**(7), 1--23
 #' 
 #' Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple
 #' comparisons and modeling techniques in dose-response studies,
-#' \emph{Biometrics}, \bold{61}, 738--748
+#' *Biometrics*, **61**, 738--748
 #' 
 #' Dette, H., Bretz, F., Pepelyshev, A. and Pinheiro, J. C. (2008). Optimal
-#' Designs for Dose Finding Studies, \emph{Journal of the American Statisical
-#' Association}, \bold{103}, 1225--1237
+#' Designs for Dose Finding Studies, *Journal of the American Statisical
+#' Association*, **103**, 1225--1237
 #' 
 #' O'Quigley, J., Iasonos, A. and Bornkamp, B. (2017) Handbook of methods for
 #' designing, monitoring, and analyzing dose-finding trials, CRC press, Part 3:
 #' Dose-Finding Studies in Phase II
 #' 
 #' Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of
 #' dose finding studies combining multiple comparisons and modeling procedures,
-#' \emph{Journal of Biopharmaceutical Statistics}, \bold{16}, 639--656
+#' *Journal of Biopharmaceutical Statistics*, **16**, 639--656
 #' 
 #' Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based
 #' dose finding under model uncertainty using general parametric models,
-#' \emph{Statistics in Medicine}, \bold{33}, 1646--1661
+#' *Statistics in Medicine*, **33**, 1646--1661
 #' 
 #' Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley
 #' @keywords internal
@@ -82,19 +82,19 @@
 #' 
 #' Below are the definitions of the model functions:
 #' 
-#' \bold{Emax model} \deqn{}{f(d,theta)=E0+Emax d/(ED50 + d).}\deqn{
+#' **Emax model** \deqn{}{f(d,theta)=E0+Emax d/(ED50 + d).}\deqn{
 #' f(d,\theta)=E_0+E_{max}\frac{d}{ED_{50}+d}}{f(d,theta)=E0+Emax d/(ED50 +
 #' d).}
 #' 
-#' \bold{Sigmoid Emax Model} \deqn{}{f(d,theta)=E0+Emax d^h/(ED50^h +
+#' **Sigmoid Emax Model** \deqn{}{f(d,theta)=E0+Emax d^h/(ED50^h +
 #' d^h).}\deqn{
 #' f(d,\theta)=E_0+E_{max}\frac{d^h}{ED^h_{50}+d^h}}{f(d,theta)=E0+Emax
 #' d^h/(ED50^h + d^h).}
 #' 
-#' \bold{Exponential Model} \deqn{}{f(d,theta)=E0+E1 (exp(d/delta)-1).}\deqn{
+#' **Exponential Model** \deqn{}{f(d,theta)=E0+E1 (exp(d/delta)-1).}\deqn{
 #' f(d,\theta)=E_0+E_1(\exp(d/\delta)-1)}{f(d,theta)=E0+E1 (exp(d/delta)-1).}
 #' 
-#' \bold{Beta model} \deqn{}{f(d,theta)=E0+Emax
+#' **Beta model** \deqn{}{f(d,theta)=E0+Emax
 #' B(delta1,delta2)(d/scal)^delta1(1-d/scal)^delta2}\deqn{
 #' f(d,\theta)=E_0+E_{max}B(\delta_1,\delta_2)(d/scal)^{\delta_1}(1-d/scal)^{\delta_2}
 #' }{f(d,theta)=E0+Emax B(delta1,delta2)(d/scal)^delta1(1-d/scal)^delta2}
@@ -105,71 +105,71 @@
 #' \delta_2^{\delta_2})}{B(delta1,delta2)=(delta1+delta2)^(delta1+delta2)/(delta1^delta1
 #' delta2^delta2).} and \eqn{scal}{scal} is a fixed dose scaling parameter.
 #' 
-#' \bold{Linear Model} \deqn{}{f(d,theta)=E0+delta d.}\deqn{
+#' **Linear Model** \deqn{}{f(d,theta)=E0+delta d.}\deqn{
 #' f(d,\theta)=E_0+\delta d}{f(d,theta)=E0+delta d.}
 #' 
-#' \bold{Linear in log Model} \deqn{}{f(d,theta)=E0+delta log(d + off),}\deqn{
+#' **Linear in log Model** \deqn{}{f(d,theta)=E0+delta log(d + off),}\deqn{
 #' f(d,\theta)=E_0+\delta \log(d + off)}{f(d,theta)=E0+delta log(d + off),}
 #' here \eqn{off}{off} is a fixed offset parameter.
 #' 
-#' \bold{Logistic Model} \deqn{
+#' **Logistic Model** \deqn{
 #' f(d, \theta) = E_0 + E_{\max}/\left\{1 + \exp\left[ \left(ED_{50} - d
 #' \right)/\delta \right] \right\}}{f(d,theta)=E0+Emax/(1 + exp((ED50-d)/delta)).}
 #' 
-#' \bold{Quadratic Model} \deqn{}{f(d,theta)=E0+beta1 d+beta2 d^2.}\deqn{
+#' **Quadratic Model** \deqn{}{f(d,theta)=E0+beta1 d+beta2 d^2.}\deqn{
 #' f(d,\theta)=E_0+\beta_1d+\beta_2d^2}{f(d,theta)=E0+beta1 d+beta2 d^2.} The
 #' standardized model equation for the quadratic model is \eqn{d+\delta
 #' d^2}{d+delta d^2}, with \eqn{\delta=\beta_2/\beta_1}{delta=beta2/beta1}.
 #' 
-#' \bold{Linear Interpolation model}\cr The linInt model provides linear
+#' **Linear Interpolation model**\cr The linInt model provides linear
 #' interpolation at the values defined by the nodes vector. In virtually all
 #' situations the nodes vector is equal to the doses used in the analysis. For
-#' example the \code{\link{Mods}} and the \code{\link{fitMod}} function
+#' example the [Mods()] and the [fitMod()] function
 #' automatically use the doses that are used in the context of the function
-#' call as nodes. The guesstimates specified in the \code{\link{Mods}} function
+#' call as nodes. The guesstimates specified in the [Mods()] function
 #' need to be the treatment effects at the active doses standardized to the
-#' interval [0,1] (see the examples in the \code{\link{Mods}} function).
+#' interval \[0,1\] (see the examples in the [Mods()] function).
 #' 
 #' @details
-#' The \bold{Emax model} is used to represent monotone, concave dose-response
+#' The **Emax model** is used to represent monotone, concave dose-response
 #' shapes.  To distinguish it from the more general sigmoid emax model it is
 #' sometimes also called hyperbolic emax model.
 #' 
-#' The \bold{sigmoid Emax} model is an extension of the (hyperbolic) Emax model
+#' The **sigmoid Emax** model is an extension of the (hyperbolic) Emax model
 #' by introducing an additional parameter h, that determines the steepness of
 #' the curve at the ed50 value. The sigmoid Emax model describes monotonic,
 #' sigmoid dose-response relationships. In the toxicology literature this model
 #' is also called four-parameter log-logistic (4pLL) model.
 #' 
-#' The \bold{quadratic} model is intended to capture a possible non-monotonic
+#' The **quadratic** model is intended to capture a possible non-monotonic
 #' dose-response relationship.
 #' 
-#' The \bold{exponential model} is intended to capture a possible sub-linear or
+#' The **exponential model** is intended to capture a possible sub-linear or
 #' a convex dose-response relationship.
 #' 
-#' The \bold{beta model} is intended to capture non-monotone dose-response
+#' The **beta model** is intended to capture non-monotone dose-response
 #' relationships and is more flexible than the quadratic model.  The kernel of
 #' the beta model function consists of the kernel of the density function of a
-#' beta distribution on the interval [0,scal]. The parameter scal is not
+#' beta distribution on the interval \[0,scal\]. The parameter scal is not
 #' estimated but needs to be set to a value larger than the maximum dose. It
 #' can be set in most functions (\samp{fitMod}, \samp{Mods}) via the
 #' \samp{addArgs} argument, when omitted a value of \samp{1.2*(maximum dose)}
 #' is used as default, where the maximum dose is inferred from other input to
 #' the respective function.
 #' 
-#' The \bold{linear in log-dose} model is intended to capture concave shapes.
-#' The parameter \code{off} is not estimated in the code but set to a
+#' The **linear in log-dose** model is intended to capture concave shapes.
+#' The parameter `off` is not estimated in the code but set to a
 #' pre-specified value. It can be set in most functions (\samp{fitMod},
 #' \samp{Mods}) via the \samp{addArgs} argument, when omitted a value of
 #' \samp{0.01*(maximum dose)} is used as default, where the maximum dose is
 #' inferred from other input to the respective function.
 #' 
-#' The \bold{logistic model} is intended to capture general monotone, sigmoid
+#' The **logistic model** is intended to capture general monotone, sigmoid
 #' dose-response relationships. The logistic model and the sigmoid Emax model
 #' are closely related: The sigmoid Emax model is a logistic model in
 #' log(dose).
 #' 
-#' The \bold{linInt model} provids linear interpolation of the means at the
+#' The **linInt model** provids linear interpolation of the means at the
 #' doses. This can be used as a "nonparametric" estimate of the dose-response
 #' curve, but is probably most interesting for specifying a "nonparametric"
 #' truth during planning and assess how well parametric models work under a
@@ -210,14 +210,14 @@
 #' linIntGrad(dose, resp, nodes, ...)
 #' @return Response value for model functions or matrix containing the gradient
 #' evaluations.
-#' @seealso \code{\link{fitMod}}
+#' @seealso [fitMod()]
 #' @references MacDougall, J. (2006). Analysis of dose-response studies - Emax
-#' model,\emph{in} N. Ting (ed.), \emph{Dose Finding in Drug Development},
+#' model,*in* N. Ting (ed.), *Dose Finding in Drug Development*,
 #' Springer, New York, pp. 127--145
 #' 
 #' Pinheiro, J. C., Bretz, F. and Branson, M. (2006). Analysis of dose-response
-#' studies - modeling approaches, \emph{in} N. Ting (ed.). \emph{Dose Finding
-#' in Drug Development}, Springer, New York, pp. 146--171
+#' studies - modeling approaches, *in* N. Ting (ed.). *Dose Finding
+#' in Drug Development*, Springer, New York, pp. 146--171
 #' @examples
 #' 
 #' ## some quadratic example shapes
@@ -275,12 +275,12 @@ NULL
 #' @usage data(biom)
 #' @format A data frame with 100 observations on the following 2 variables.
 #'   \describe{
-#'   \item{\code{resp}}{a numeric vector containing the response values}
-#'   \item{\code{dose}}{a numeric vector containing the dose values}
+#'   \item{`resp`}{a numeric vector containing the response values}
+#'   \item{`dose`}{a numeric vector containing the dose values}
 #' }
 #' @source Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining
 #' multiple comparisons and modeling techniques in dose-response studies,
-#' \emph{Biometrics}, \bold{61}, 738--748
+#' *Biometrics*, **61**, 738--748
 #' @keywords datasets
 NULL
 
@@ -303,12 +303,12 @@ NULL
 #' @format A data frame with 5 summary estimates (one per dose). Variables:
 #'   A data frame with 5 summary estimates (one per dose). Variables:
 #'  \describe{
-#'    \item{\code{dose}}{a numeric vector containing the dose values}
-#'    \item{\code{fev1}}{a numeric vector containing the least square
+#'    \item{`dose`}{a numeric vector containing the dose values}
+#'    \item{`fev1`}{a numeric vector containing the least square
 #'      mean per dose}
-#'    \item{\code{sdev}}{a numeric vector containing the standard errors
+#'    \item{`sdev`}{a numeric vector containing the standard errors
 #'      of the least square means per dose}
-#'    \item{\code{n}}{Number of participants analyzed per treatment group}
+#'    \item{`n`}{Number of participants analyzed per treatment group}
 #'  }
 #' @source http://clinicaltrials.gov/ct2/show/results/NCT00501852
 #' @keywords datasets
@@ -356,12 +356,12 @@ NULL
 #' @format 
 #'   A data frame with 369 observations on the following 2 variables.
 #'   \describe{
-#'     \item{\code{gender}}{a factor specifying the gender}
-#'     \item{\code{dose}}{a numeric vector}
-#'     \item{\code{resp}}{a numeric vector}
+#'     \item{`gender`}{a factor specifying the gender}
+#'     \item{`dose`}{a numeric vector}
+#'     \item{`resp`}{a numeric vector}
 #'   }
 #' @source Biesheuvel, E. and Hothorn, L. A. (2002). Many-to-one comparisons in
-#' stratified designs, \emph{Biometrical Journal}, \bold{44}, 101--116
+#' stratified designs, *Biometrical Journal*, **44**, 101--116
 #' @keywords datasets
 NULL
 
@@ -381,9 +381,9 @@ NULL
 #'  A data frame with 517 columns corresponding to the patients that
 #'  completed the trial
 #'  \describe{
-#'    \item{\code{dose}}{a numeric vector containing the dose values}
-#'    \item{\code{painfree}}{number of treatment responders}
-#'    \item{\code{ntrt}}{number of subject per treatment group}
+#'    \item{`dose`}{a numeric vector containing the dose values}
+#'    \item{`painfree`}{number of treatment responders}
+#'    \item{`ntrt`}{number of subject per treatment group}
 #'  }
 #' @source http://clinicaltrials.gov/ct2/show/results/NCT00712725
 #' @keywords datasets
@@ -421,14 +421,14 @@ NULL
 #' @format 
 #'  A data frame with 100 observations on the following 2 variables.
 #'  \describe{
-#'    \item{\code{resp}}{a numeric vector containing the response values}
-#'    \item{\code{dose}}{a numeric vector containing the dose values}
-#'    \item{\code{id}}{Patient ID}    
-#'    \item{\code{time}}{time of measurement}
+#'    \item{`resp`}{a numeric vector containing the response values}
+#'    \item{`dose`}{a numeric vector containing the dose values}
+#'    \item{`id`}{Patient ID}    
+#'    \item{`time`}{time of measurement}
 #'  }
 #' @source Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014)
 #' Model-based dose finding under model uncertainty using general parametric
-#' models, \emph{Statistics in Medicine}, \bold{33}, 1646--1661
+#' models, *Statistics in Medicine*, **33**, 1646--1661
 #' @keywords datasets
 #' @examples
 #' 

R/MCPMod.R

@@ -3,31 +3,31 @@
 
 #' MCPMod - Multiple Comparisons and Modeling
 #'
-#' Tests for a dose-response effect using a model-based multiple contrast test (see \code{\link{MCTtest}}), selects one
-#' (or several) model(s) from the significant shapes, fits them using \code{\link{fitMod}}.  For details on the method
+#' Tests for a dose-response effect using a model-based multiple contrast test (see [MCTtest()]), selects one
+#' (or several) model(s) from the significant shapes, fits them using [fitMod()].  For details on the method
 #' see Bretz et al. (2005).
 #'
 #'
 #' @aliases MCPMod predict.MCPMod plot.MCPMod
 #' @inheritParams MCTtest
 #' @param selModel Optional character vector specifying the model selection
 #'   criterion for dose estimation.  Possible values are \itemize{ \item
-#'   \code{AIC}: Selects model with smallest AIC (this is the default) \item
-#'   \code{maxT}: Selects the model corresponding to the largest t-statistic.
-#'   \item \code{aveAIC}: Uses a weighted average of the models corresponding to
+#'   `AIC`: Selects model with smallest AIC (this is the default) \item
+#'   `maxT`: Selects the model corresponding to the largest t-statistic.
+#'   \item `aveAIC`: Uses a weighted average of the models corresponding to
 #'   the significant contrasts.  The model weights are chosen by the formula:
 #'   \eqn{w_i = \exp(-0.5AIC_i)/\sum_i(\exp(-0.5AIC_i))}{w_i =
 #'   exp(-0.5AIC_i)/sum(exp(-0.5AIC_i))} See Buckland et al. (1997) for details.
 #'   } For \samp{type = "general"} the "gAIC" is used.
 #' @param df Specify the degrees of freedom to use in case \samp{type = "general"}, for the call to
-#'   \code{\link{MCTtest}} and \code{\link{fitMod}}. Infinite degrees of (\samp{df=Inf}) correspond to the multivariate
+#'   [MCTtest()] and [fitMod()]. Infinite degrees of (\samp{df=Inf}) correspond to the multivariate
 #'   normal distribution.  For type = "normal" the degrees of freedom deduced from the AN(C)OVA fit are used and this
 #'   argument is ignored.
-#' @param doseType,Delta,p \samp{doseType} determines the dose to estimate, ED or TD (see also \code{\link{Mods}}), and
+#' @param doseType,Delta,p \samp{doseType} determines the dose to estimate, ED or TD (see also [Mods()]), and
 #'   \samp{Delta} and \samp{p} need to be specified depending on whether TD or ED is to be estimated.  See
-#'   \code{\link{TD}} and \code{\link{ED}} for details.
+#'   [TD()] and [ED()] for details.
 #' @param bnds Bounds for non-linear parameters. This needs to be a list with list entries corresponding to the selected
-#'   bounds. The names of the list entries need to correspond to the model names. The \code{\link{defBnds}} function
+#'   bounds. The names of the list entries need to correspond to the model names. The [defBnds()] function
 #'   provides the default selection.
 #' @param control Control list for the optimization.\cr A list with entries: "nlminbcontrol", "optimizetol" and
 #'   "gridSize".
@@ -43,29 +43,29 @@
 #' @return An object of class \samp{MCPMod}, which contains the fitted \samp{MCTtest} object as well as the \samp{DRMod}
 #'   objects and additional information (model selection criteria, dose estimates, selected models).
 #' @author Bjoern Bornkamp
-#' @seealso \code{\link{MCTtest}}, \code{\link{fitMod}}, \code{\link{drmodels}}
+#' @seealso [MCTtest()], [fitMod()], [drmodels()]
 #' @references Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling
-#'   techniques in dose-response studies, \emph{Biometrics}, \bold{61}, 738--748
+#'   techniques in dose-response studies, *Biometrics*, **61**, 738--748
 #'
 #'   Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple
-#'   comparisons and modeling procedures, \emph{Journal of Biopharmaceutical Statistics}, \bold{16}, 639--656
+#'   comparisons and modeling procedures, *Journal of Biopharmaceutical Statistics*, **16**, 639--656
 #'
 #'   Pinheiro, J. C., Bretz, F., and Branson, M. (2006). Analysis of dose-response studies - modeling approaches,
-#'   \emph{in} N. Ting (ed.). \emph{Dose Finding in Drug Development}, Springer, New York, pp. 146--171
+#'   *in* N. Ting (ed.). *Dose Finding in Drug Development*, Springer, New York, pp. 146--171
 #'
 #'   Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty
-#'   using general parametric models, \emph{Statistics in Medicine}, \bold{33}, 1646--1661
+#'   using general parametric models, *Statistics in Medicine*, **33**, 1646--1661
 #'
 #'   Schorning, K., Bornkamp, B., Bretz, F., & Dette, H. (2016). Model selection
-#' versus model averaging in dose finding studies. \emph{Statistics in
-#' Medicine}, \bold{35}, 4021--4040
+#' versus model averaging in dose finding studies. *Statistics in
+#' Medicine*, **35**, 4021--4040
 #'
 #'   Xun, X. and Bretz, F. (2017) The MCP-Mod methodology: Practical Considerations and The DoseFinding R package, in
 #'   O'Quigley, J., Iasonos, A. and Bornkamp, B. (eds) Handbook of methods for designing, monitoring, and analyzing
 #'   dose-finding trials, CRC press
 #'
 #'   Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997). Model selection an integral part of inference,
-#'   \emph{Biometrics}, \bold{53}, 603--618
+#'   *Biometrics*, **53**, 603--618
 #'
 #'   Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley.
 #' @examples