BayesPIM 2.0

Breaking changes

• Version 2.0 is a substantial rewrite: the user-facing API, the sampler, and the post-estimation tooling have all changed, and code written for 1.0.1 will not run unchanged.
• All exported functions and arguments were renamed to snake_case. bayes.2S() is now bayespim(), gen.dat() is gen_data(), get.IC_2S() is get_ic(), trim.mcmc() is trim_mcmc(), and search.prop.sd() is search_prop_sd(). Arguments follow the same convention (Vobs -> v_obs, Z.X -> x_t, Z.W -> x_g, dist.X -> dist, tau.w -> tau_g).
• bayes.2S_seq() and search.prop.sd_seq() are removed; the parallel and sequential code paths are unified.
• get.ppd.2S() is replaced by ppCIF(), which computes the mixture and the non-prevalent cumulative incidence function in a single call and has a plot() method.
• The thining, conv.crit, parallel, vanilla, and ndraws.naive arguments are gone. Draw storage is controlled by save_every and the warm-up cutoff by warmup.
• In gen_data(), the covariate-correlation argument is renamed from r to rho, so that r unambiguously denotes the baseline-test indicator, as it does in bayespim() and in the returned $r.
• Two defaults changed: kappa no longer defaults to 0.5 and must be given explicitly when update_kappa = FALSE, and the effective-sample-size target min_effss rose from chains * 10 to chains * 100.
• The generalized gamma is reparameterized to the Prentice form (location, scale, signed shape Q) using flexsurv, replacing the ggamma parameterisation of 1.0.1. Fitted shape values are not comparable across versions, and the model is now available only with the collapsed sampler.

New features

• Two slice samplers. sampler = "slice_collapsed", the new default, augments only the latent screening interval and updates the incidence parameters from the interval-censored likelihood; sampler = "slice" augments exact event times. Both show lower autocorrelation and faster convergence than the Metropolis-Hastings sampler of 1.0.1, which remains available as sampler = "mh".
• Gamma incidence times (dist = "gamma"), parameterized through the conditional mean and coefficient of variation.
• summary() and plot() methods for fitted models, reporting posterior quantiles and convergence diagnostics for each parameter block.
• Revised convergence assessment using rank-normalized split R-hat and effective sample size from the posterior package, replacing coda::gelman.diag(). update_till_converge = TRUE extends sampling automatically until max_rhat and min_effss are met.
• Reproducibility. seed_chains sets one seed per chain and the end-of-chain RNG state is stored, so a run continued through prev_run is identical to an uninterrupted run of the same length.
• Covariate standardization (standardize_covariates) and internal time rescaling (rescale_times), both enabled by default, with returned coefficients on the original scale.
• User-supplied priors through log_prior_fun; the default is exported as log_aft_prior().
• Input validation. Malformed inputs produce a single, specific error before sampling begins.
• Further additions: save_every to limit memory, silent to suppress progress output, and fix_q to hold the generalized-gamma shape fixed.
• Example fit. data(mod) provides a converged model so post-estimation examples run without refitting.
• Rewritten vignette (vignette("BayesPIM_intro")): a user guide covering estimation, convergence, model comparison, and posterior CIFs.
• Test suite. The package now ships contract tests covering the samplers, distributions, draw storage, covariate scaling, and post-estimation functions.

Other

• Internal code was consolidated and reduced: duplicate parameterisations, unreachable branches, and redundant state were removed, and naming is consistently snake_case throughout.
• Dependencies: posterior, survival, and flexsurv added; ggamma and mvtnorm removed; coda moved to Depends.
• Smaller bug fixes and behind-the-scenes efficiency updates and leaner code.