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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 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

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.