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.