Changelog
Source:NEWS.md
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()
, andgetContr()
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 integratedgetMED()
intoassessDesign()
andperformBayesianMCPMod()
. - 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.