robust-statistics-toolkit

# Robust Statistics Toolkit ## Purpose Provides robust statistical methods resistant to outliers and model violations for reliable inference. ## Capabilities - M-estimators (Huber, Tukey) - Trimmed and winsorized estimators - Robust regression (MM-estimation) - Breakdown point analysis - Influence function computation - Robust covariance estimation ## Usage Guidelines 1. **Outlier Detection**: Identify potential outliers first 2. **Estimator Selection**: Choose based on expected contamination 3. **Breakdown Point**: Consider required breakdown point 4. **Efficiency**: Balance robustness and efficiency ## Tools/Libraries - robustbase (R) - scikit-learn - statsmodels