CRAN submission preparations

Author: wojcieko

NEWS.md

@@ -1,33 +1,41 @@
-## BayesianMCPMod 1.2.0 (TODO: DATE)
-
-- 
+## BayesianMCPMod 1.2.0 (28-Aug-2025)
+
+* Fixed a bug in performBayesianMCPMod() where the model significance status from the MCP step was sometimes not correctly assigned to the fitted model in the Mod step
+* Fixed a bug in print.modelFit() where sometimes the coefficients for the fitted model shapes were not printed correctly
+* Fixed a bug in getMED() where quantile and evidence level could sometimes not be matched due to floating-point precision issues when using bootstrapped quantiles
+* Changed functions getPosterior(), getCritProb(), and getContr() to accept a covariance matrix instead of a standard deviation vector as argument
+* Added support for none-zero off-diagonal covariance matrices in the MCP step
+* Added bootstrapped differences to getBootstrapSamples()
+* Added average MED identification rate as attribute to assessDesign() output
+* Made the future.apply package optional
+* Re-worked vignettes and improved the output of print functions
 
 ## BayesianMCPMod 1.1.0 (07-Mar-2025)
 
-- Fixed a bug in plot.modelFits() that would plot credible bands based on incorrectly selected bootstrapped quantiles
-- Added getMED(), a function to assess the minimally efficacious dose (MED) and integrated getMED() into assessDesign() and performBayesianMCPMod
-- Added parallel processing using the future framework
-- Modified the handling of the fit of an average model: Now, getModelFits() has an argument to fit an average model and this will be carried forward for all subsequent functions
-- Re-introduced getBootstrapSamples(), a separate function for bootstrapping samples from the posterior distributions of the dose levels
-- Adapted the vignettes to new features
+* Fixed a bug in plot.modelFits() that would plot credible bands based on incorrectly selected bootstrapped quantiles
+* Added getMED(), a function to assess the minimally efficacious dose (MED) and integrated getMED() into assessDesign() and performBayesianMCPMod()
+* Added parallel processing using the future framework
+* Modified the handling of the fit of an average model: Now, getModelFits() has an argument to fit an average model and this will be carried forward for all subsequent functions
+* Re-introduced getBootstrapSamples(), a separate function for bootstrapping samples from the posterior distributions of the dose levels
+* Adapted the vignettes to new features
 
 ## BayesianMCPMod 1.0.2 (06-Feb-2025)
 
-- Addition of new vignette comparing frequentist and Bayesian MCPMod using vague priors
-- Extension of getPosterior to allow the input of a fully populated variance-covariance matrix
-- Added the non-monotonic model shapes beta and quadratic
-- New argument in assessDesign() to optionally skip the Mod part of Bayesian MCPMod
-- Additional tests 
+* Addition of new vignette comparing frequentist and Bayesian MCPMod using vague priors
+* Extension of getPosterior() to allow the input of a fully populated variance-covariance matrix
+* Added the non-monotonic model shapes beta and quadratic
+* New argument in assessDesign() to optionally skip the Mod part of Bayesian MCPMod
+* Additional tests 
 
 ## BayesianMCPMod 1.0.1 (03-Apr-2024)
 
-- Re-submission of the 'BayesianMCPMod' package
-- Removed a test that occasionally failed on the fedora CRAN test system
-- Fixed a bug that would return wrong bootstrapped quantiles in getBootstrapQuantiles()
-- Added getBootstrapSamples(), a separate function for bootstrapping samples
+* Re-submission of the 'BayesianMCPMod' package
+* Removed a test that occasionally failed on the fedora CRAN test system
+* Fixed a bug that would return wrong bootstrapped quantiles in getBootstrapQuantiles()
+* Added getBootstrapSamples(), a separate function for bootstrapping samples
 
 ## BayesianMCPMod 1.0.0 (31-Dec-2023)
 
-- Initial release of the 'BayesianMCPMod' package
-- Special thanks to Jana Gierse, Bjoern Bornkamp, Chen Yao, Marius Thomas & Mitchell Thomann for their review and valuable comments
-- Thanks to Kevin Kunzmann for R infrastructure support and to Frank Fleischer for methodological support
+* Initial release of the 'BayesianMCPMod' package
+* Special thanks to Jana Gierse, Bjoern Bornkamp, Chen Yao, Marius Thomas & Mitchell Thomann for their review and valuable comments
+* Thanks to Kevin Kunzmann for R infrastructure support and to Frank Fleischer for methodological support

cran-comments.md

@@ -16,7 +16,7 @@
 
 ## R CMD check results
 
-### Local aarch64-apple-darwin20, Windows Server, R 4.4.2
+### Local aarch64-apple-darwin20, R 4.4.2
 0 errors √ | 0 warnings √ | 0 notes √
 
 ### Winbuilder x86_64-w64-mingw32, macOS Ventura 13.3.1, R under development (unstable) (2025-02-05 r87692 ucrt)
@@ -41,21 +41,19 @@ Status: OK
 
 ### Rhub Windows / windows
 
-* checking for detritus in the temp directory ... NOTE
-Found the following files/directories:
-  'Rscript420e8038' 'Rscripta4ce8028'
 * DONE
-Status: 1 NOTE
-
--> This note seems is related with the parallelization on the github server and does not occur on the Winbuilder server.
+Status: OK
 
 ## From NEWS.md
 
-## BayesianMCPMod 1.1.0 (07-Mar-2025)
-
-- Fixed a bug in plot.modelFits() that would plot credible bands based on incorrectly selected bootstrapped quantiles
-- Added getMED(), a function to assess the minimally efficacious dose (MED) and integrated getMED() into assessDesign() and performBayesianMCPMod
-- Added parallel processing using the future framework
-- Modified the handling of the fit of an average model: Now, getModelFits() has an argument to fit an average model and this will be carried forward for all subsequent functions
-- Re-introduced getBootstrapSamples(), a separate function for bootstrapping samples from the posterior distributions of the dose levels
-- Adapted the vignettes to new features
+### BayesianMCPMod 1.2.0 (28-Aug-2025)
+
+* Fixed a bug in performBayesianMCPMod() where the model significance status from the MCP step was sometimes not correctly assigned to the fitted model in the Mod step
+* Fixed a bug in print.modelFit() where sometimes the coefficients for the fitted model shapes were not printed correctly
+* Fixed a bug in getMED() where quantile and evidence level could sometimes not be matched due to floating-point precision issues when using bootstrapped quantiles
+* Changed functions getPosterior(), getCritProb(), and getContr() to accept a covariance matrix instead of a standard deviation vector as argument
+* Added support for none-zero off-diagonal covariance matrices in the MCP step
+* Added bootstrapped differences to getBootstrapSamples()
+* Added average MED identification rate as attribute to assessDesign() output
+* Made the future.apply package optional
+* Re-worked vignettes and improved the output of print functions

cran_submission_script.R

@@ -33,7 +33,7 @@ urlchecker::url_update()
 
 # check on other distributions
 # _rhub
-rhub::rhub_check(platforms = c("ubuntu-next", "macos-arm64", "windows"))
+# rhub::rhub_check(platforms = c("ubuntu-next", "macos-arm64", "windows"))
 # _win devel CRAN
 devtools::check_win_devel()
 # _macos CRAN

vignettes/analysis_normal.Rmd

@@ -395,7 +395,7 @@ The actual fit (on this approximated posterior distribution) is then performed u
 
 The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights.
 
-For the considered case, the simplified and the full fit are very similar, so wepresent the full fit.
+For the considered case, the simplified and the full fit are very similar, so we present the full fit.
 
 ```{r}
 # If simple = TRUE, uses approx posterior
@@ -444,7 +444,7 @@ bootstrap_quantiles <- getBootstrapQuantiles(
   doses      = c(0, 2.5, 4, 5, 7, 10),
   n_samples  = 10)
 ```
-The boostrap quantiles include information about the absolute quantiles (sample_type=abs) and also about the placebo-adjusted (resp. control-adjusted) quantiles (sample_type=diff). 
+The bootstrap quantiles include information about the absolute quantiles (sample_type=abs) and also about the placebo-adjusted (resp. control-adjusted) quantiles (sample_type=diff). 
 
 ```{r, collapse = TRUE}
 #| code-fold: true