Changes in version 0.1.0 (2026-08-07)                  

Initial Release

Features

  - Distribution-agnostic Bayesian estimation framework using
    Tierney-Kadane approximation
  - Support for arbitrary univariate probability distributions
    (continuous or discrete)
  - Multiple Bayesian loss functions:
      - Squared Error Loss (SEL) - posterior mean
      - LINEX Loss - asymmetric linear-exponential loss
      - General Entropy Loss (GEL) - generalized entropy loss
      - Precautionary Loss - conservative estimation
      - Weighted Squared Error Loss - inverse-variance weighting
      - Custom loss functions - user-defined

Censoring Support

  - Complete (uncensored) data
  - Right-censored data
  - Left-censored data
  - Interval-censored data
  - Type-I censoring
  - Type-II censoring
  - Progressive Type-II censoring
  - Hybrid censoring
  - Doubly censored data

Prior Distributions

  - Gamma prior
  - Normal prior
  - Beta prior
  - Uniform prior
  - Exponential prior
  - Log-Normal prior
  - Weibull prior
  - Inverse Gamma prior
  - User-defined priors (via custom functions)
  - Independent priors for multiple parameters

Optimization

  - Multiple backends: BFGS, L-BFGS-B, Nelder-Mead, nlminb, maxLik,
    trust
  - Automatic fallback between optimization methods
  - Convergence diagnostics and iteration tracking
  - Support for parameter bounds

Numerical Differentiation

  - Numerical gradients and Hessians via numDeriv
  - Optional analytic derivative support with validation

Model Comparison

  - AIC (Akaike Information Criterion)
  - BIC (Bayesian Information Criterion)
  - CAIC (Consistent AIC)
  - HQIC (Hannan-Quinn Information Criterion)
  - DIC (Deviance Information Criterion) approximation
  - Expected log-posterior
  - Negative log-likelihood

Visualization

  - Posterior approximation plots (per parameter)
  - Likelihood surface and profile likelihood plots
  - Contour plots for two-parameter models
  - Prior vs posterior comparison plots
  - Convergence diagnostics
  - Credible interval forest plots
  - Residuals plots (Pearson and deviance)

Prior Sensitivity Analysis

  - Systematic variation of prior hyperparameters
  - Impact on estimates, bias, variance, and risk
  - Sensitivity plots and tables

S3 Methods

  - summary() - comprehensive model summary
  - print() - concise output
  - coef() - extract Bayes estimates
  - vcov() - extract posterior covariance matrix
  - logLik() - extract log-likelihood
  - AIC() - compute AIC
  - BIC() - compute BIC
  - plot() - diagnostic plots
  - predict() - generate predictions
  - residuals() - compute residuals

Testing

  - Comprehensive testthat test suite
  - Tests for all censoring schemes
  - Tests for multiple prior families
  - Tests for loss functions
  - Tests for optimization scenarios
  - GitHub Actions CI/CD for cross-platform checking

Documentation

  - roxygen2 documentation for all exported functions
  - Runnable examples in documentation
  - Comprehensive vignettes (planned)