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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | spfcICOMP_0.1.0.tar.gz |
290.4 KiB |
0.1.0 |
rolling linux/noble R-4.5 | spfcICOMP_0.1.0.tar.gz |
290.9 KiB |
0.1.0 |
rolling source/ R- | spfcICOMP_0.1.0.tar.gz |
76.6 KiB |
0.1.0 |
latest linux/jammy R-4.5 | spfcICOMP_0.1.0.tar.gz |
290.4 KiB |
0.1.0 |
latest linux/noble R-4.5 | spfcICOMP_0.1.0.tar.gz |
290.9 KiB |
0.1.0 |
latest source/ R- | spfcICOMP_0.1.0.tar.gz |
76.6 KiB |
0.1.0 |
2026-04-23 source/ R- | spfcICOMP_0.1.0.tar.gz |
0 B |