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Changelog

Source: NEWS.md

BetaDanish 0.2.0

Major new functionality

  • Bayesian inference: bayes_betadanish() provides random-walk Metropolis sampling for the Exponentiated Danish submodel and the full four-parameter Beta-Danish model with vague Gamma priors.
  • Competing risks rewrite: fit_bd_competing() now uses bound-constrained multi-start L-BFGS-B optimization. New cif_compare() overlays fitted cumulative incidence functions against the Aalen-Johansen estimator and reports Gray’s test.
  • Structural properties: closed-form Shannon entropy (bd_entropy_shannon()), order-statistic densities (bd_order_stat_pdf()), mean residual life, hazard-shape classification, and stress-strength reliability.
  • Diagnostics: Cox-Snell residual plots for both AFT (plot.bd_aft()) and cure (plot.bd_cure()) fits.
  • Bootstrap confidence intervals for AFT and cure models.
  • Finite-sample simulation-study runner for Table 5.5 of the underlying thesis.

Vignettes

Three new vignettes have been added:

  • “Bayesian Estimation with BetaDanish”
  • “Competing Risks with the Beta-Danish Distribution”
  • “Cure Models with the Beta-Danish Distribution”

Bug fixes

  • summary.bd_aft() and summary.bd_cure() now apply the delta-method back-transform so that reported standard errors are on the natural parameter scale, not the log scale.
  • report_betadanish() no longer prints NULL for AIC and BIC.
  • dbetadanish() log-pdf is now numerically stable in the right tail.
  • qbetadanish() clamps p to the unit interval.

Infrastructure

  • Continuous integration via GitHub Actions on four OS/R configurations: ubuntu-release, ubuntu-devel, macOS-release, and windows-release.
  • Test coverage reporting via Codecov.
  • Online package website built with pkgdown.
  • All Suggests packages used via requireNamespace() guards at the call sites.

BetaDanish 0.1.0

CRAN release: 2026-05-20

  • First public release.
  • Implements the four-parameter Beta-Danish distribution and its three-parameter Exponentiated Danish submodel for survival and reliability analysis.
  • Maximum-likelihood estimation, goodness-of-fit, model comparison, and visualization.
  • Built-in datasets: remission, carbon_fibres, transplant, aarset, leukemia, melanoma, brain_cancer.