fitdistrBayes - Objective Bayesian Distribution Fitting

Fits common univariate distributions using registered objective Bayesian priors, including Jeffreys, reference, and maximal data information priors, and supports user-defined distributions and priors through an extensible model specification. Model-specific posterior propriety and moment conditions are checked before computation when registered or supplied. Exact simulation, marginalization, slice sampling, adaptive Metropolis, and user-supplied posterior samplers share a common interface for summaries, diagnostics, prediction, and pointwise log-likelihood evaluation. A separate interface fits independently right-censored observations using the registered complete-data priors, observed-data likelihood sampling or data augmentation, with sufficient posterior-propriety checks. Optional post-processing provides WAIC, PSIS-LOO, and DIC for observed-data likelihoods. The reference-prior framework follows Bernardo (1979) <doi:10.1111/j.2517-6161.1979.tb01066.x>.