adding Rdpack for references

DESCRIPTION

@@ -13,7 +13,7 @@ Authors@R: c(
    person("Daniel", "Sabanes Bove", role = "aut", email = "[email protected]",
            comment = c(ORCID = "0000-0002-0176-9239")),
    person(given = "Novartis Pharma AG", role = c("cph", "fnd")))
-Imports: ggplot2, lattice, mvtnorm
+Imports: ggplot2, lattice, mvtnorm, Rdpack
 Suggests: 
     emmeans,
     numDeriv,
@@ -48,3 +48,4 @@ URL: https://github.com/openpharma/DoseFinding, https://openpharma.github.io/Dos
 BugReports: https://github.com/openpharma/DoseFinding/issues
 Roxygen: list(markdown = TRUE)
 Config/testthat/edition: 3
+RdMacros: Rdpack

NAMESPACE

@@ -7,7 +7,7 @@ importFrom("stats", "AIC", "IQR", "acf", "approx", "as.formula",
            "pnorm", "predict", "qnorm", "qt", "quantile", "quasi",
            "sd", "terms", "uniroot", "var", "vcov")
 importFrom("utils", "setTxtProgressBar", "txtProgressBar", "browseURL")
-
+importFrom(Rdpack,reprompt)
 export(fitMod, defBnds, bFitMod, maFitMod, MCTtest, bMCTtest, MCPMod,
        betaMod, quadratic, emax, exponential,linear, linlog, logistic, sigEmax, linInt,
        betaModGrad, quadraticGrad, emaxGrad, exponentialGrad,

R/DesignMCPModApp.R

@@ -2,8 +2,8 @@
 #' 
 #' 
 #' This function starts the externally hosted DesignMCPMod Shiny App in a
-#' browser window. The app was developed by Sophie Sun [aut, cre], Danyi Xiong
-#' [aut], Bjoern Bornkamp [ctb], Frank Bretz [ctb], Ardalan Mirshani [ctb].
+#' browser window. The app was developed by Sophie Sun \[aut, cre\], Danyi Xiong
+#' \[aut\], Bjoern Bornkamp \[ctb\], Frank Bretz \[ctb\], Ardalan Mirshani \[ctb\].
 #' This app performs power and sample size calculations for a multiple contrast
 #' test for normal, binary and negative binomial outcomes. The app uses the
 #' DoseFinding package as calculation backend and the R code underlying the

R/DoseFinding-package.R

@@ -13,36 +13,23 @@
 #' **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, *Annals of
-#' Applied Statistics*, **5**, 1611--1631
+#' @references 
+#' \insertRef{bornkamp2011}{DoseFinding}
 #' 
-#' Bornkamp B., Pinheiro J. C., and Bretz, F. (2009). MCPMod: An R Package for
-#' the Design and Analysis of Dose-Finding Studies, *Journal of
-#' Statistical Software*, **29**(7), 1--23
+#' \insertRef{bornkamp2009}{DoseFinding}
 #' 
-#' Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple
-#' comparisons and modeling techniques in dose-response studies,
-#' *Biometrics*, **61**, 738--748
+#' \insertRef{bretz2005}{DoseFinding}
 #' 
-#' Dette, H., Bretz, F., Pepelyshev, A. and Pinheiro, J. C. (2008). Optimal
-#' Designs for Dose Finding Studies, *Journal of the American Statisical
-#' Association*, **103**, 1225--1237
+#' \insertRef{dette2008}{DoseFinding}
 #' 
-#' 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
+#' \insertRef{oquigley2017}{DoseFinding}
 #' 
-#' Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of
-#' dose finding studies combining multiple comparisons and modeling procedures,
-#' *Journal of Biopharmaceutical Statistics*, **16**, 639--656
+#' \insertRef{pinheiro2006}{DoseFinding}
 #' 
-#' Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based
-#' dose finding under model uncertainty using general parametric models,
-#' *Statistics in Medicine*, **33**, 1646--1661
+#' \insertRef{pinheiro2014}{DoseFinding}
+#' 
+#' \insertRef{seber2003}{DoseFinding}
 #' 
-#' Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley
-#' @keywords internal
 #' @examples
 #' 
 #' data(IBScovars)
@@ -211,13 +198,11 @@
 #' @return Response value for model functions or matrix containing the gradient
 #' evaluations.
 #' @seealso [fitMod()]
-#' @references MacDougall, J. (2006). Analysis of dose-response studies - Emax
-#' model,*in* N. Ting (ed.), *Dose Finding in Drug Development*,
-#' Springer, New York, pp. 127--145
+#' @references 
+#' \insertRef{macdougall2006}{DoseFinding}
+#' 
+#' \insertRef{pinheiro2006}{DoseFinding}
 #' 
-#' Pinheiro, J. C., Bretz, F. and Branson, M. (2006). Analysis of dose-response
-#' studies - modeling approaches, *in* N. Ting (ed.). *Dose Finding
-#' in Drug Development*, Springer, New York, pp. 146--171
 #' @examples
 #' 
 #' ## some quadratic example shapes
@@ -268,7 +253,7 @@ NULL
 #' Biometrics Dose Response data
 #' 
 #' An example data set for dose response studies. This data set was used in
-#' Bretz et al. (2005) to illustrate the MCPMod methodology.
+#' \insertCite{bretz2005;textual}{DoseFinding} to illustrate the MCPMod methodology.
 #' 
 #' @name biom
 #' @docType data
@@ -278,9 +263,7 @@ NULL
 #'   \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,
-#' *Biometrics*, **61**, 738--748
+#' @source \insertRef{bretz2005}{DoseFinding}
 #' @keywords datasets
 NULL
 
@@ -360,8 +343,7 @@ NULL
 #'     \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, *Biometrical Journal*, **44**, 101--116
+#' @source \insertRef{biesheuvel2002}{DoseFinding}
 #' @keywords datasets
 NULL
 
@@ -426,9 +408,7 @@ NULL
 #'    \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, *Statistics in Medicine*, **33**, 1646--1661
+#' @source \insertRef{pinheiro2014}{DoseFinding}
 #' @keywords datasets
 #' @examples
 #' 

R/MCPMod.R

@@ -5,7 +5,7 @@
 #'
 #' 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).
+#' see \insertCite{bretz2005;textual}{DoseFinding}.
 #'
 #'
 #' @aliases MCPMod predict.MCPMod plot.MCPMod
@@ -17,7 +17,7 @@
 #'   \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.
+#'   exp(-0.5AIC_i)/sum(exp(-0.5AIC_i))} See \insertCite{buckland1997;textual}{DoseFinding} 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
 #'   [MCTtest()] and [fitMod()]. Infinite degrees of (\samp{df=Inf}) correspond to the multivariate
@@ -44,30 +44,24 @@
 #'   objects and additional information (model selection criteria, dose estimates, selected models).
 #' @author Bjoern Bornkamp
 #' @seealso [MCTtest()], [fitMod()], [drmodels()]
-#' @references Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling
-#'   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, *Journal of Biopharmaceutical Statistics*, **16**, 639--656
-#'
-#'   Pinheiro, J. C., Bretz, F., and Branson, M. (2006). Analysis of dose-response studies - modeling approaches,
-#'   *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, *Statistics in Medicine*, **33**, 1646--1661
-#'
-#'   Schorning, K., Bornkamp, B., Bretz, F., & Dette, H. (2016). Model selection
-#' 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,
-#'   *Biometrics*, **53**, 603--618
-#'
-#'   Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley.
+#' @references 
+#' 
+#' \insertRef{buckland1997}{DoseFinding}
+#' 
+#' \insertRef{bretz2005}{DoseFinding}
+#' 
+#' \insertRef{pinheiro2006b}{DoseFinding}
+#' 
+#' \insertRef{pinheiro2006}{DoseFinding}
+#' 
+#' \insertRef{pinheiro2014}{DoseFinding}
+#' 
+#' \insertRef{schorning2016}{DoseFinding}
+#' 
+#' \insertRef{seber2003}{DoseFinding}
+#' 
+#' \insertRef{xun2017}{DoseFinding}
+#' 
 #' @examples
 #'
 #' data(biom)

R/MCTtest.R

@@ -25,7 +25,7 @@
 #' @param S The covariance matrix of \samp{resp} when \samp{type = "general"}, see Description.
 #' @param type Determines whether inference is based on an ANCOVA model under a homoscedastic normality assumption (when
 #'   \samp{type = "normal"}), or estimates at the doses and their covariance matrix and degrees of freedom are specified
-#'   directly in \samp{resp}, \samp{S} and \samp{df}. See also [fitMod()] and Pinheiro et al. (2014).
+#'   directly in \samp{resp}, \samp{S} and \samp{df}. See also [fitMod()] and \insertCite{pinheiro2014}{DoseFinding}.
 #' @param addCovars Formula specifying additive linear covariates (for \samp{type = "normal"})
 #' @param placAdj Logical, if true, it is assumed that placebo-adjusted
 #'   estimates are specified in \samp{resp} (only possible for \samp{type =
@@ -46,11 +46,11 @@
 #' @return An object of class MCTtest, a list containing the output.
 #' @author Bjoern Bornkamp
 #' @seealso [powMCT()], [optContr()]
-#' @references Hothorn, T., Bretz, F., and Westfall, P. (2008). Simultaneous Inference in General Parametric Models,
-#'   *Biometrical Journal*, **50**, 346--363
-#'
-#'   Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty
-#'   using general parametric models, *Statistics in Medicine*, **33**, 1646--1661
+#' @references 
+#' \insertRef{hothorn2008}{DoseFinding}
+#'   
+#' \insertRef{pinheiro2014}{DoseFinding}
+#' 
 #' @examples
 #'
 #' ## example without covariates
@@ -278,9 +278,7 @@ critVal <- function(corMat, alpha = 0.025, df = NULL,
 #' @return Numeric containing the calculated p-values.
 #' @author Bjoern Bornkamp
 #' @seealso [MCTtest()], [optContr()]
-#' @references Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies
-#'   combining multiple comparisons and modeling procedures, *Journal of Biopharmaceutical Statistics*, **16**,
-#'   639--656
+#' @references \insertRef{pinheiro2006b}{DoseFinding}
 #' @examples
 #' data(biom)
 #' ## define shapes for which to calculate optimal contrasts

R/Mods.R

@@ -50,9 +50,7 @@
 #'   derived linear parameters (based on \samp{"placEff"} and \samp{"maxEff"}) in a list.
 #' @author Bjoern Bornkamp
 #' @seealso [Mods()], [drmodels()], [optDesign()], [powMCT()]
-#' @references Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies
-#'   combining multiple comparisons and modeling procedures, *Journal of Biopharmaceutical Statistics*, **16**,
-#'   639--656
+#' @references \insertRef{pinheiro2006b}{DoseFinding}
 #' @examples
 #'
 #' ## Example on how to specify candidate models

R/bFitMod.R

@@ -11,7 +11,8 @@
 #' assumed, and a large number of samples is drawn from this distribution. For each draw the fitMod function with
 #' \samp{type = "general"} is used to fit the draws from the multivariate normal distribution.
 #'
-#' Componentwise univariate slice samplers are implemented (see Neal, 2003) to sample from the posterior distribution.
+#' Componentwise univariate slice samplers are implemented \insertCite{@see @neal2003}{DoseFinding}
+#' to sample from the posterior distribution.
 #'
 #' @aliases bFitMod coef.bFitMod predict.bFitMod plot.bFitMod
 #' @param dose Numeric specifying the dose variable.
@@ -54,7 +55,7 @@
 #'   additional information on the fitted model.
 #' @author Bjoern Bornkamp
 #' @seealso [fitMod()]
-#' @references Neal, R. M. (2003), Slice sampling, Annals of Statistics, 31, 705-767
+#' @references \insertAllCited{}
 #' @examples
 #' data(biom)
 #  ## produce first stage fit (using dose as factor)

R/bMCTtest.R

@@ -1,14 +1,14 @@
 #' Performs Bayesian multiple contrast test
 #'
 #' This function performs a Bayesian multiple contrast test using normal mixture priors for the response on each dose,
-#' as proposed in Fleischer et al. (2022). For a general description of the multiple contrast test see
+#' as proposed in \insertCite{fleischer2022;textual}{DoseFinding}. For a general description of the multiple contrast test see
 #' [MCTtest()].
 #'
 #' If \samp{type = "normal"}, an ANCOVA model based on a homoscedastic normality assumption is fitted and posteriors for
 #' dose-response and contrast vectors are obtained assuming a known variance.
 #'
 #' For \samp{type = "general"} it is assumed multivariate normally distributed estimates are specified in \samp{resp}
-#' with covariance given by \samp{S}, which define the likelihood.  Posteriors for dose-response and contrast vectors
+#' with covariance given by \samp{S}, which define the likelihood. Posteriors for dose-response and contrast vectors
 #' are then obtained assuming a known covariance matrix S
 #'
 #' The multiple contrast test decision is based on the maximum posterior probability of a contrast being greater than
@@ -17,7 +17,7 @@
 #' contrast test if uninformative priors are used.
 #'
 #' For the default calculation of optimal contrasts the prior information is ignored (i.e. contrasts are calculated in
-#' the same way as in [MCTtest()]).  Fleischer et al. (2022) discuss using contrasts that take the prior
+#' the same way as in [MCTtest()]). \insertCite{fleischer2022;textual}{DoseFinding} discuss using contrasts that take the prior
 #' effective sample sizes into account, which can be slightly more favourable for the Bayesian MCT test. Such
 #' alternative contrasts can be directly handed over via the \samp{contMat} argument.
 #'
@@ -45,8 +45,7 @@
 #' @author Marius Thomas
 #' @export
 #' @seealso [MCTtest()], [optContr()]
-#' @references Fleischer, F., Bossert, S., Deng, Q., Loley, C. and Gierse, J. (2022).  Bayesian MCP-Mod,
-#'   *Pharmaceutical Statistics*, **21**, 654--670
+#' @references \insertAllCited{}
 #' @examples
 #'
 #'
@@ -277,7 +276,7 @@ print.bMCTtest <- function(x, digits = 3, eps = 1e-3, ...){
 #' @return Returns a posterior multivariate normal mixture as a list of length 3, containing mixture weights, mean
 #'   vectors and covariance matrices.
 #' @author Marius Thomas
-#' @references Bernardo, J. M., and Smith, A. F. (1994). Bayesian theory. John Wiley & Sons.
+#' @references \insertRef{bernardo1994}{DoseFinding}
 #' @export
 mvpostmix <- function(priormix, mu_hat, S_hat)
 {

R/fitMod.R

@@ -47,8 +47,8 @@ defBnds <- function(mD, emax = c(0.001, 1.5)*mD,
 #' estimates obtained in a first stage fit, then \samp{resp} contains the estimates and \samp{S} is the estimated
 #' covariance matrix for the estimates in \samp{resp}. Statistical inference (e.g. confidence intervals) rely on
 #' asymptotic normality of the first stage estimates, which makes this method of interest only for sufficiently large
-#' sample size for the first stage fit. A modified model-selection criterion can be applied to these model fits (see
-#' also Pinheiro et al. 2014 for details).
+#' sample size for the first stage fit. A modified model-selection criterion can be applied to these model fits 
+#' \insertCite{@see also @pinheiro2014 for details}{DoseFinding}.
 #'
 #' For details on the implemented numerical optimizer see the Details section below.
 #'
@@ -60,9 +60,8 @@ defBnds <- function(mD, emax = c(0.001, 1.5)*mD,
 #' grid-search (with the grid size specified via \samp{control$gridSize}), or can directly be handed over via
 #' \samp{start}.
 #'
-#' For details on the asymptotic approximation used for \samp{type = "normal"}, see Seber and Wild (2003, chapter 5).
-#' For details on the asymptotic approximation used for \samp{type = "general"}, and the gAIC, see Pinheiro et al.
-#' (2014).
+#' For details on the asymptotic approximation used for \samp{type = "normal"}, see \insertCite{seber2003;textual}{DoseFinding}, Chapter 5.
+#' For details on the asymptotic approximation used for \samp{type = "general"}, and the gAIC, see \insertCite{pinheiro2014;textual}{DoseFinding}.
 #'
 #' @aliases fitMod coef.DRMod vcov.DRMod predict.DRMod plot.DRMod logLik.DRMod AIC.DRMod gAIC gAIC.DRMod
 #' @param dose,resp Either vectors of equal length specifying dose and response values, or names of variables in the
@@ -76,7 +75,7 @@ defBnds <- function(mD, emax = c(0.001, 1.5)*mD,
 #'   the first stage fit.
 #' @param type Determines whether inference is based on an ANCOVA model under a homoscedastic normality assumption (when
 #'   \samp{type = "normal"}), or estimates at the doses and their covariance matrix and degrees of freedom are specified
-#'   directly in \samp{resp}, \samp{S} and \samp{df}. See also the Description above and Pinheiro et al. (2014).
+#'   directly in \samp{resp}, \samp{S} and \samp{df}. See also the Description above and \insertCite{pinheiro2014;textual}{DoseFinding}.
 #' @param addCovars Formula specifying additional additive linear covariates (only for \samp{type = "normal"})
 #' @param placAdj Logical, if true, it is assumed that placebo-adjusted
 #'   estimates are specified in \samp{resp} (only possible for \samp{type =
@@ -109,10 +108,9 @@ defBnds <- function(mD, emax = c(0.001, 1.5)*mD,
 #'   residual sum of squares (or generalized residual sum of squares),
 #' @author Bjoern Bornkamp
 #' @seealso [defBnds()], [drmodels()]
-#' @references Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model
-#'   uncertainty using general parametric models, *Statistics in Medicine*, **33**, 1646--1661
+#' @references 
+#' \insertAllCited{}
 #'
-#'   Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley.
 #' @examples
 #'
 #' ## Fit the emax model to the IBScovars data set