fitdistrBayes 0.2.3

• Reject tied observations for the Student-t model with unknown degrees of freedom under the independence Jeffreys prior. Such samples have an improper posterior because the prior gives positive mass to degrees of freedom for which the location-scale posterior is not integrable near zero scale.
• Document the pairwise-distinct sample requirement in the help page and public catalogue, and add regression tests for tied and distinct observations.
• The fixed-degree-of-freedom Student-t routes retain their existing, multiplicity-dependent propriety conditions. Sampling algorithms and the remaining model-prior routes are unchanged.

fitdistrBayes 0.2.2

• Added the weighted Lindley distribution with Fisher-information Jeffreys, one-group reference, Jeffreys’ first-rule, independence-Jeffreys, and exact ordered reference priors for lambda and phi. All six routes enforce the proved condition that the sample is positive, nonconstant, and has at least two observations.
• Disabled weighted Lindley MDI fitting because the corresponding posterior is improper for every finite sample.
• Added the closed-form weighted Lindley likelihood initialization with a numerical maximum-likelihood fallback, stable log-prior evaluation, a Fisher-orthogonal mean/shape Metropolis parameterization, mixture-based posterior prediction, and pointwise log-likelihood evaluation.
• Expanded the public catalogue to 20 distributions and 56 enabled model–prior routes, with analytic, initialization, predictive, guard, and registry tests for the new family.

fitdistrBayes 0.2.1

• Added fitdistrBayes_model(), a public model specification for combining a user-defined density and prior with parameter support, starting values, validation, posterior-propriety and moment declarations, posterior prediction, and an adaptive Metropolis, slice, or user-supplied sampler.
• Extended fitdistrBayes() so the distribution argument can be a fitdistrBayes_model object. The fitted object records which extension components and computational engine were used.
• Added executable tests for a Laplace model with a proper non-objective prior, a slice-sampled Exponential model, and an exact Poisson–Gamma posterior simulator.

fitdistrBayes 0.2.0

• Expanded the help pages to document the class, structure, and statistical meaning of every exported function and S3 method return value.
• Replaced the non-executable tutorial wrapper with an interactive file-view example and left all fitting examples unwrapped after verifying that the complete help example runs in less than five seconds.
• Removed explicit access to .GlobalEnv and all superassignment operators from the package code and validation scripts.
• Added Gumbel, Frechet, Lomax, Nakagami-m, Exponential-Logarithmic, and Rician models with documented propriety conditions enforced on the supported data domain.
• Added closed-form moment starts for Gumbel, Nakagami-m, and Rician, closed-form L-moment starts for Frechet and Lomax, and a stable one-dimensional moment start for the Exponential-Logarithmic model.
• Added marginal/conditional samplers for Frechet and Nakagami-m, stable transformed Metropolis samplers for the other new models, and an accelerated Gauss-Laguerre evaluation of the Rician Jeffreys factor.
• Reparameterized the Lomax Metropolis sampler by shape and scale/shape to reduce posterior correlation while retaining the documented output scale.
• Added explicit failures for the improper Lomax reference posterior and Nakagami-m MDI posterior, as well as for unproved Rician reference/MDI routes.
• Expanded the analytic, numerical, initialization, route, and stress tests to 19 built-in distributions and 50 enabled model-prior routes.
• Added fitdistrBayes_routes() as a machine-readable catalogue of all 50 enabled routes and made the route test execute directly from this catalogue.
• Expanded help("fitdistrBayes") with all built-in models, parameterizations, priors, fixed-parameter requirements, propriety guidance, and one example for every family; the English-language 50-route console tutorial is installed as examples/tutorial_fitdistrBayes_all_models.R.
• Tightened the Nakagami-m propriety check to reject constant positive samples.
• Made the custom density/prior log-mode contract explicit and reject undefined or positive-infinite log values; custom RNG outputs can now be validated.
• Reported post-warmup, warmup, and all-iteration acceptance rates separately, included tail ESS in the convergence decision, and corrected constant-chain and per-chain ACF diagnostics.
• Removed silent floating-point clipping: unrepresentable natural-scale draws now produce transparent numerical-range errors.

fitdistrBayes 0.1.2

• Added classical automatic initialization, including the closed-form Weibull L-moment estimator.
• Made non-constant-sample validation invariant to translation.
• Replaced costly truncated negative-binomial entropy sums with an exact numerical integral for MDI fitting.
• Stabilized natural-scale Gamma and Weibull draws at floating-point limits.
• Preserved the matrix shape of log_lik() for one-observation samples.
• Scaled Metropolis location proposals to the model’s initial scale, fixing poor mixing for very small- and very large-scale data.
• Added broad stress, entropy, initialization, edge-case, and difficult-chain regression tests.