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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)

CRAN release: 2025-03-07

  • 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)

CRAN release: 2025-02-06

  • 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)

CRAN release: 2024-04-05

  • 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)

CRAN release: 2024-01-08

  • 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.