[ilovemaths] risdr 0.4.0

kabirolorede@gmail.com (Kabir Olorede) — Mon, 12 Oct 2026 18:23:20 GMT

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) , Li (1992) , and Li and Wang (2007) . Covariance shrinkage follows Olorede and Yahya (2019) , Ledoit and Wolf (2004) and Chen et al. (2010) .

[ilovemaths] spfcICOMP 0.1.0.9000

kabir.olorede@kwasu.edu.ng (Kabir Opeyemi Olorede) — Sun, 06 Sep 2026 14:22:24 GMT

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) , Chen et al. (2010) , Bozdogan (2000) , and Olorede and Yahya (2019) .

ilovemaths.r-universe.dev

[ilovemaths] risdr 0.4.0

[ilovemaths] spfcICOMP 0.1.0.9000