Changes in version 1.4.0                        

Released 2026-09-12

New features

  - pooling_surv() is the new general entry point for fitting Bayesian
    pooling models for right-censored log-normal survival data. It runs
    the same data-augmented Gibbs sampler under three pooling modes,
    selected with the new pooling argument:
      - pooling = "exnex" — the EXNEX hierarchy (default, as in previous
        releases);
      - pooling = "complete" — a single shared basket effect with prior
        N(m_mu, v_mu) (complete pooling);
      - pooling = "none" — independent basket effects with priors
        N(m_nex_j, v_nex_j) (no pooling).

Deprecation (soft)

  - exnex_surv() is deprecated in favour of pooling_surv(); it now emits
    a one-line deprecation message and forces pooling = "exnex". All
    existing calls continue to work.
  - Fitted objects are of class "pooling_surv"; all methods (summary(),
    print(), plot(), survival_curves(), median_survival(), rmst(),
    probability_superiority(), compute_waic(), compare_waic()) accept
    them.

Other

  - Priors that are irrelevant for the chosen pooling mode (e.g. p_mix
    under "complete" or "none") now trigger a warning naming the ignored
    fields.
  - print() output now labels the fitted pooling variant.
  - New thin argument in pooling_surv(): keep every thin-th post-warmup
    draw (default 1, keep all). Thinning reduces memory and posterior
    autocorrelation without changing the sampler.
  - New verbose argument in pooling_surv() (default TRUE): a live
    progress bar (progressr) reports progress while the chains run;
    silenced with verbose = FALSE.
  - compare_waic() now names rows with the argument names used in the
    call (e.g. fit_exnex) instead of the anonymous fit1/fit2 labels;
    when no informative name is available it falls back to the fitted
    pooling variant.
  - parallel_chains in pooling_surv() is now a logical flag: TRUE runs
    all chains concurrently on background sessions through the future
    framework (progress from workers is relayed to your console, so it
    works in RStudio too); FALSE (default) runs chains sequentially.
  - With verbose = TRUE, a live progress bar (progressr) now updates
    while the chains run, in both sequential and parallel execution; the
    per-chain text lines were removed.
  - New convergence_diagnostics() computes split R-hat, bulk ESS and
    tail ESS per parameter; summary()/print() now include these columns
    and a one-line convergence verdict automatically.
  - New rank_probabilities() gives the posterior probability of each
    rank position per basket (median survival, S(t_0), or RMST
    summaries).
  - New posterior_predictive_check() overlays the posterior predictive
    mean survival curve with the observed Kaplan-Meier curve per basket.
  - New shrinkage_plot() contrasts EXNEX posterior estimates against
    unpooled Kaplan-Meier medians to visualise partial pooling.

                        Changes in version 1.3.1                        

Breaking changes

  - compute_waic() and compare_waic() now return se_elpd_waic instead of
    se_waic, matching the convention of the loo package.
  - simulate_data() replaces argument n_each with n (scalar or
    per-basket vector) and adds argument K (number of baskets).

Bug fixes

  - simulate_data() now actually uses the n argument; previously n_each
    was accepted but ignored in favour of a hard-coded size sequence.
  - survival_curves() now emits a warning when more than one group
    exists in the fitted model but newdata is not supplied.
  - rmst() now correctly defaults tmax to max(fit$data$time) instead of
    requiring it.
  - probability_superiority() no longer returns a useless level = NULL
    element; its newdata handling is now consistent and better
    documented.

                 Changes in version 1.3.0 (2026-09-10)                  

  - New inference and model-selection tools (all in pure R, no changes
    to the validated C++ sampler):
      - survival_curves() computes posterior survival curves S(t) (with
        plot.survival_exnex() for plotting);
      - median_survival() returns posterior median survival times;
      - rmst() returns the restricted mean survival time up to a
        horizon;
      - compute_waic()/compare_waic() provide WAIC model comparison;
      - probability_superiority() estimates the posterior probability
        that one group beats another on median, survival probability, or
        RMST;
      - simulate_data() generates log-normal basket-trial datasets (with
        resistant baskets) for studies and examples.
  - New vignette Tools for Experimenters covering these utilities.