updated description files and other small changes

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

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

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

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
 #' <doi:10.1002/sim.8291> and <doi:10.18637/jss.v098.i09>.
 #' \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 <doi:10.18637/jss.v098.i09> .
 #'
 #' 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}}.
+#'
 #'
 #'
 #'

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).