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risdr - Regularised and Information-Theoretic Sufficient Dimension Reduction

Implements covariance-stabilised sufficient dimension reduction for continuous responses, with an additional sparse engine for continuous, categorical, and right-censored survival responses and information-theoretic structural dimension selection. Supported methods include sliced inverse regression, sliced average variance estimation, directional regression, and principal Hessian directions. Sample, ridge, Oracle Approximating Shrinkage, Ledoit-Wolf, and Maximum Entropy Covariance (MEC) estimators are provided alongside prediction, resampling, simulation, and diagnostic utilities. The sufficient dimension reduction methods build on Li (1991) <doi:10.1080/01621459.1991.10475035>, Li (1992) <doi:10.1080/01621459.1992.10476258>, and Li and Wang (2007) <doi:10.1198/016214507000000536>. Covariance shrinkage follows Olorede and Yahya (2019) <doi:10.48550/arXiv.1909.13017>, Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4> and Chen et al. (2010) <doi:10.1109/TSP.2010.2053029>.

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5.08 score 205 downloads

spfcICOMP - Shrinkage Principal Fitted Components with Information Complexity-Based Model Selection

Implements shrinkage principal fitted components for sufficient dimension reduction in high-dimensional regression and classification. Provides regularised covariance estimation using Oracle Approximating Shrinkage and Maximum Entropy Covariance, structural-dimension selection using conventional and information-complexity criteria, response-guided feature screening, reduced-space prediction, and simulation utilities. Methodological foundations include Cook and Forzani (2008) <doi:10.1214/08-STS275>, Chen et al. (2010) <doi:10.1109/TSP.2010.2053029>, Bozdogan (2000) <doi:10.1006/jmps.1999.1277>, and Olorede and Yahya (2019) <doi:10.48550/arXiv.1909.13017>.

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4.18 score 263 downloads