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.