diff --git a/DESCRIPTION b/DESCRIPTION index 54b048f0..e2fb2b4f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: adoptr Type: Package Title: Adaptive Optimal Two-Stage Designs in R -Version: 1.0.1 +Version: 1.1.0 Authors@R: c( person("Kevin", "Kunzmann", role = c("aut", "cre"), @@ -65,5 +65,5 @@ Collate: 'minimize.R' 'regularization.R' RoxygenNote: 7.2.3 -BugReports: https://github.com/kkmann/adoptr/issues -URL: https://github.com/kkmann/adoptr +BugReports: https://github.com/imbi-heidelberg/adoptr/issues +URL: https://github.com/imbi-heidelberg/adoptr diff --git a/NEWS.md b/NEWS.md index f9b897aa..ef065d00 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,13 @@ +# adoptr 1.1.0 + +* added log-rank test for time-to-event-endpoints +* added chi-squared distribution for contingency tables and two-sided tests +* added f-distribution for ANOVA +* improved get_initial_design to find better initial designs +* added automatic constraint checks +* bug-fix for binomial pdf/cdf +* bug-fix in design2str function + # adoptr 1.0.0 * added references to article in journal of statistical software diff --git a/R/FDistribution.R b/R/FDistribution.R index 0346e7cd..3336bf9f 100644 --- a/R/FDistribution.R +++ b/R/FDistribution.R @@ -37,9 +37,10 @@ setClass("ANOVA", contains = "NestedModels") #' @examples #' model <- NestedModels(2, 4) #' -#' @seealso see \code{\link{probability_density_function}} and +#' @seealso See \code{\link{probability_density_function}} and #' \code{\link{cumulative_distribution_function}} to evaluate the pdf -#' and the cdf, respectively. +#' and the cdf, respectively. Use \code{\link{ANOVA}} for detailed information +#' of ANOVA. #' #' @rdname NestedModels-class #' @export @@ -68,7 +69,8 @@ NestedModels <- function(p_inner, p_outer) { #' #' @seealso see \code{\link{probability_density_function}} and #' \code{\link{cumulative_distribution_function}} to evaluate the pdf -#' and the cdf, respectively. +#' and the cdf, respectively. Use \code{\link{NestedModels}} to get insights +#' in the implementation of \code{ANOVA}. #' #' @rdname ANOVA-class #' @export diff --git a/R/adoptr.R b/R/adoptr.R index 9c580a96..0d8e8e95 100644 --- a/R/adoptr.R +++ b/R/adoptr.R @@ -1,7 +1,8 @@ #' Adaptive Optimal Two-Stage Designs #' #' The \pkg{adoptr} package provides functionality to explore custom optimal -#' two-stage designs for one- or two-arm superiority tests. +#' two-stage designs for one- or two-arm superiority tests. More than two arms +#' can be compared via chi-squared tests or ANOVA. #' For more details on the theoretical background see #' and . #' \pkg{adoptr} makes heavy use of the S4 class system. @@ -13,14 +14,14 @@ #' @section Quickstart: #' #' For a sample workflow and a quick demo of the capabilities, see -#' \href{https://kkmann.github.io/adoptr/articles/adoptr.html}{here}. +#' \href{https://imbi-heidelberg.github.io/adoptr/articles/adoptr.html}{here}. #' #' A more detailed description of the background and the usage of \pkg{adoptr} -#' can be found \href{https://kkmann.github.io/adoptr/articles/adoptr_jss.html}{here} +#' can be found \href{https://imbi-heidelberg.github.io/adoptr/articles/adoptr_jss.html}{here} #' or here . #' #' A variety of examples is presented in the validation report hosted -#' \href{https://kkmann.github.io/adoptr-validation-report/}{here}. +#' \href{https://imbi-heidelberg.github.io/adoptr-validation-report/}{here}. #' #' #' @@ -33,8 +34,10 @@ #' #' @section Data distributions: #' -#' Currently, the only implemented data distribution is \code{\link{Normal}} -#' (one or two arms). +#' The implemented data distributions are \code{\link{Normal}}, \code{\link{Binomial}}, +#' \code{\link{Student}}, \code{\link{Survival}}, \code{\link{ChiSquared}} (including +#' \code{\link{Pearson2xK}} and \code{\link{ZSquared}}) and \code{\link{ANOVA}}. +#' #' #' #' diff --git a/man/ANOVA-class.Rd b/man/ANOVA-class.Rd index 820bdb16..fb9f653f 100644 --- a/man/ANOVA-class.Rd +++ b/man/ANOVA-class.Rd @@ -34,5 +34,6 @@ H1 <- PointMassPrior(get_tau_ANOVA(c(0.4, 0.8, 0.5)), 1) \seealso{ see \code{\link{probability_density_function}} and \code{\link{cumulative_distribution_function}} to evaluate the pdf - and the cdf, respectively. + and the cdf, respectively. Use \code{\link{NestedModels}} to get insights + in the implementation of \code{ANOVA}. } diff --git a/man/NestedModels-class.Rd b/man/NestedModels-class.Rd index 812bc2df..c3d2fe92 100644 --- a/man/NestedModels-class.Rd +++ b/man/NestedModels-class.Rd @@ -57,7 +57,8 @@ model <- NestedModels(2, 4) } \seealso{ -see \code{\link{probability_density_function}} and +See \code{\link{probability_density_function}} and \code{\link{cumulative_distribution_function}} to evaluate the pdf - and the cdf, respectively. + and the cdf, respectively. Use \code{\link{ANOVA}} for detailed information + of ANOVA. } diff --git a/man/adoptr.Rd b/man/adoptr.Rd index b6e5a3f9..f7849382 100644 --- a/man/adoptr.Rd +++ b/man/adoptr.Rd @@ -6,7 +6,8 @@ \title{Adaptive Optimal Two-Stage Designs} \description{ The \pkg{adoptr} package provides functionality to explore custom optimal -two-stage designs for one- or two-arm superiority tests. +two-stage designs for one- or two-arm superiority tests. More than two arms +can be compared via chi-squared tests or ANOVA. For more details on the theoretical background see and . \pkg{adoptr} makes heavy use of the S4 class system. @@ -17,14 +18,14 @@ A good place to start learning about it can be found For a sample workflow and a quick demo of the capabilities, see -\href{https://kkmann.github.io/adoptr/articles/adoptr.html}{here}. +\href{https://imbi-heidelberg.github.io/adoptr/articles/adoptr.html}{here}. A more detailed description of the background and the usage of \pkg{adoptr} -can be found \href{https://kkmann.github.io/adoptr/articles/adoptr_jss.html}{here} +can be found \href{https://imbi-heidelberg.github.io/adoptr/articles/adoptr_jss.html}{here} or here . A variety of examples is presented in the validation report hosted -\href{https://kkmann.github.io/adoptr-validation-report/}{here}. +\href{https://imbi-heidelberg.github.io/adoptr-validation-report/}{here}. } \section{Designs}{ @@ -37,8 +38,9 @@ A variety of examples is presented in the validation report hosted \section{Data distributions}{ -Currently, the only implemented data distribution is \code{\link{Normal}} -(one or two arms). +The implemented data distributions are \code{\link{Normal}}, \code{\link{Binomial}}, +\code{\link{Student}}, \code{\link{Survival}}, \code{\link{ChiSquared}} (including +\code{\link{Pearson2xK}} and \code{\link{ZSquared}}) and \code{\link{ANOVA}}. } \section{Priors}{ diff --git a/vignettes/adoptr.Rmd b/vignettes/adoptr.Rmd index 47505ead..c91ffd97 100644 --- a/vignettes/adoptr.Rmd +++ b/vignettes/adoptr.Rmd @@ -231,14 +231,14 @@ In particular, **adoptr** supports: * Important predefined scores as (conditional) power or sample size functions as well as a framework for constraints on scores -(cf. [conditional scores](https://kkmann.github.io/adoptr/articles/conditional-scores.html)). +(cf. [conditional scores](https://imbi-heidelberg.github.io/adoptr/articles/conditional-scores.html)). * Arbitrary continuous or discrete -[prior distributions](https://kkmann.github.io/adoptr/articles/working-with-priors.html) +[prior distributions](https://imbi-heidelberg.github.io/adoptr/articles/working-with-priors.html) for the single location parameter $\theta$ of the test statistic. * Support for simple arithmetic operations on scores to quickly define weighted -sums etc. (cf. [composite scores](https://kkmann.github.io/adoptr/articles/composite-scores.html)). +sums etc. (cf. [composite scores](https://imbi-heidelberg.github.io/adoptr/articles/composite-scores.html)). * Relatively flexible framework for -[defining new scores](https://kkmann.github.io/adoptr/articles/defining-new-scores.html). +[defining new scores](https://imbi-heidelberg.github.io/adoptr/articles/defining-new-scores.html).