## BayesianMCPMod 1.3.2 (23-Mar-2026)

* Included Firth's penalized regression model for binary endpoints in case of separation.

## BayesianMCPMod 1.3.1 (25-Feb-2026)

* Fixed a newly introduced bug that would occur if the R package `future.apply` was not installed. 
* Added flexibility to bootstrapped credible bands in `plot.modelFits()`.

## BayesianMCPMod 1.3.0 (23-Feb-2026)

* Fixed a bug that would occur when predicting from the beta model shape outside of the original dose range.
* Fixed a bug in which the MED assessment could not be performed when specifying a negative direction of beneficial effect and an evidence level other than 0.5.
* Added functions and vignettes for the binary endpoint case.
* Added functionality to `assessDesign()` to provide custom simulated data and custom model estimates enabling complex data simulation and analysis methods.
* Added argument to `assessDesign()` for number of bootstrap samples in case `evidence_level` is provided.
* Added functionality to `plot.modelFits()` to plot effect sizes.
* Added calls to `set.seed()` in vignette's code blocks to facilitate individual code block reproducibility.

## 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.
