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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.

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 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 in getBootstrapQuantiles() that would return wrong bootstrapped quantiles.
• 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.