Changes in version 0.1.0 (2026-07-28)                  

Initial Release

New Features

  - Generalized EM algorithm for parameter estimation under censoring
  - Support for 23 censoring schemes:
      - Complete data
      - Right censoring
      - Left censoring
      - Interval censoring
      - Random censoring
      - Block random censoring
      - Type-I censoring
      - Type-II censoring
      - Progressive Type-II censoring
      - Progressive first failure censoring
      - Joint Type-I censoring
      - Joint Type-II censoring
      - Balanced joint progressive Type-II censoring
      - Hybrid censoring
      - Hybrid Type-I censoring
      - Hybrid Type-II censoring
      - Type-I hybrid censoring
      - Type-II progressively hybrid censoring
      - Doubly Type-II censoring
      - Middle censoring
      - Right truncation
      - Left truncation

Core Functionality

  - em_fit(): Main function for EM parameter estimation
  - em.control(): Control parameters for EM algorithm
  - dist_spec_em(): Define distribution for EM estimation
  - Support for arbitrary user-supplied PDF, CDF, and survival functions
  - Automatic construction of observed likelihood, complete likelihood,
    and Q-function
  - E-step with conditional expectations for censored data
  - M-step with multiple optimization methods (BFGS, L-BFGS-B,
    Nelder-Mead, CG, SANN, Brent, nlminb)

Statistical Inference

  - Numerical Hessian computation for standard errors
  - Bootstrap standard errors (parametric and nonparametric)
  - Multiple confidence interval types (normal, percentile, BCa, basic)
  - Louis method support (framework in place)

Model Selection

  - AIC, AICc, BIC, HQIC, CAIC, KIC
  - Takeuchi Information Criterion (TIC)
  - model_selection() function for comprehensive criteria

Goodness-of-Fit

  - KS statistic
  - Anderson-Darling statistic
  - Cramer-von Mises statistic
  - Watson statistic
  - RMSE, MAE, MSE
  - R-squared and adjusted R-squared
  - gof_stats() function

Diagnostics

  - EM convergence diagnostics
  - Parameter convergence tracking
  - Log-likelihood convergence tracking
  - Q-function convergence tracking
  - Aitken acceleration support
  - em_diagnostics() function

S3 Methods

  - print.emfit(): Print fit summary
  - summary.emfit(): Comprehensive summary
  - plot.emfit(): Diagnostic plots (6 plot types)
  - coef.emfit(): Extract parameter estimates
  - vcov.emfit(): Variance-covariance matrix
  - confint.emfit(): Confidence intervals
  - logLik.emfit(): Log-likelihood
  - AIC.emfit(): Akaike Information Criterion
  - BIC.emfit(): Bayesian Information Criterion
  - residuals.emfit(): Residuals
  - fitted.emfit(): Fitted values
  - predict.emfit(): Predictions

Residual Analysis

  - Cox-Snell residuals
  - Martingale residuals
  - Deviance residuals
  - Pearson residuals
  - Score residuals
  - Schoenfeld residuals
  - Randomized quantile residuals
  - Generalized residuals
  - residual_analysis() function
  - plot_residuals() function

Bootstrap

  - Parametric bootstrap
  - Nonparametric bootstrap
  - Bias-corrected bootstrap
  - Percentile bootstrap
  - BCa intervals
  - bootstrap_se() function

Visualization

  - Parameter convergence plots
  - Log-likelihood plots
  - Q-function plots
  - Histogram with fitted density
  - QQ plots
  - Residual plots
  - Residuals vs fitted plots
  - Residuals vs order plots

Parallel Computing

  - Support for parallel package
  - Support for future/furrr packages
  - Parallel bootstrap
  - get_ncores() function

Utilities

  - Comprehensive error handling and validation
  - Numerical integration utilities
  - Automatic differentiation support (numDeriv)
  - Safe numerical operations

Documentation

  - Comprehensive roxygen2 documentation
  - Benchmark examples vignette
  - Examples for Exponential, Weibull, Gamma, Log-Normal, Normal
    distributions
  - Unit tests using testthat

Performance

  - Efficient numerical integration
  - Vectorized operations
  - Caching support
  - Optional parallelization

Extensibility

  - Modular censoring scheme architecture
  - Easy to add new censoring schemes
  - Distribution-agnostic core algorithm

Bug Fixes

  - None (initial release)

Known Issues

  - Some advanced censoring schemes use placeholder implementations
  - Louis method requires further development
  - Profile likelihood intervals not yet implemented

Deprecated

  - None

Notes

  - This package is designed as the estimation counterpart to UniCensor
  - Together, they provide a complete framework for censored data
    analysis
  - Compatible with R >= 4.0.0