NEWS
BetaDanish 0.2.0 (2026-06-04)
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 (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.