Changes in version 1.3.2 (2026-05-14)                  

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

                 Changes in version 1.3.1 (2026-02-25)                  

  - 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().

                 Changes in version 1.3.0 (2026-02-23)                  

  - 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.

                 Changes in version 1.2.0 (2025-08-28)                  

  - 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.

                 Changes in version 1.1.0 (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.

                 Changes in version 1.0.2 (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.

                 Changes in version 1.0.1 (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.

                 Changes in version 1.0.0 (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.