Shrinkage Principal Fitted Components with Information Complexity-Based Model Selection R package spfcICOMP version 0.1.0 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>. Software Depends: R (>= 3.5.0) Imports: MASS Suggests: testthat (>= 3.0.0), knitr, rmarkdown Kabir Opeyemi Olorede <kabir.olorede@kwasu.edu.ng> Comprehensive R Archive Network (CRAN) Kabir Opeyemi Olorede [aut, cre, cph] MIT + file LICENSE (https://CRAN.R-project.org/package=spfcICOMP/LICENSE) 2026-09-03 application/tgz https://CRAN.R-project.org/package=spfcICOMP doi:10.32614/CRAN.package.spfcICOMP en-GB