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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