Changelog ========= For a detailed changelog, see `CHANGELOG.md `_ in the repository. Version 0.1.0 ------------- **Initial Release** Core Features ~~~~~~~~~~~~~ - Complete MICE implementation with convergence tracking - Five imputation methods: PMM, CART, Random Forest, MIDAS, Sample - Rubin's rules pooling with fraction of missing information (FMI) - Formula-based model fitting and analysis - Comprehensive input validation - Professional logging system Imputation Methods ~~~~~~~~~~~~~~~~~~ - **PMM**: Predictive Mean Matching with Bayesian bootstrap - **CART**: Classification and Regression Trees - **Random Forest**: Ensemble method with configurable parameters - **MIDAS**: Distance-aided substitution for small samples - **Sample**: Simple random sampling Configuration Options ~~~~~~~~~~~~~~~~~~~~~ - Customizable predictor matrices - Multiple visit sequence strategies - Method-specific parameter tuning - Initial imputation methods - Flexible method assignment per variable Diagnostic Tools ~~~~~~~~~~~~~~~~ - Convergence diagnostics (chain statistics) - Stripplots for observed vs imputed comparison - Density plots for distribution comparison - Box plots for distribution visualization - Missing data pattern visualization - XY plots for bivariate relationships Statistical Analysis ~~~~~~~~~~~~~~~~~~~~ - Formula-based model specification (Patsy syntax) - Automatic pooling using Rubin's rules - Fraction of Missing Information (FMI) calculation - Confidence intervals and p-values - Degrees of freedom adjustment Documentation ~~~~~~~~~~~~~ - Comprehensive Sphinx documentation - User guide with detailed explanations - Theory section with mathematical background - API reference for all modules - Jupyter notebook examples - Best practices guide Testing ~~~~~~~ - Extensive test suite with pytest - Unit tests for all core functions - Integration tests for workflows - Coverage tracking Development ~~~~~~~~~~~ - MIT License - GitHub repository with CI/CD - ReadTheDocs integration - Development, testing, and documentation dependencies Contributors ~~~~~~~~~~~~ - Anna-Carolina Haensch - The Anh Vu - Zhanna Lopuliak Future Plans ------------ Potential future enhancements (not yet implemented): - Additional imputation methods (e.g., lasso, ridge) - Parallel processing for large datasets - GPU acceleration for random forest - More sophisticated predictor matrix algorithms - Additional diagnostic plots - Integration with scikit-learn pipelines - Categorical variable handling improvements - Time series imputation methods Stay tuned for updates! Reporting Issues ---------------- Found a bug or have a feature request? Open an issue on `GitHub `_.