Generalized Principal Component Analysis

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Documentation for package ‘genpca’ version 0.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

geigen_cov	Generalized eigenproblem on a covariance matrix
genpca	Generalised Principal Components Analysis (GPCA)
genpca_cov	Generalized PCA on a covariance matrix (GMD form)
genpls	Generalized PLS via Implicit Operator (PLS-SVD / GPLSSVD)
genplsc	Canonical Generalized PLS (alias)
gmd_clear_cache	Clear internal cache for matrix decompositions
gpca_mle	Experimental penalized-ML estimation of GPCA metrics
gplssvd_op	Generalized PLS-SVD via Implicit Operator (memory-safe)
mnpca_mrl	Matrix-Normal PCA via Maximum Regularized Likelihood
print.sfpca	Print an sfpca fit
reconstruct.genpca	Reconstruct data from a genpca fit
reconstruct.sfpca	Reconstruct data from an sfpca fit
repair_metric	Repair a metric matrix explicitly
rpls	Regularised / Generalised Partial Least Squares (RPLS / GPLS)
sfpca	Sparse and Functional Principal Components Analysis (SFPCA) with Spatial Coordinates
transfer.cross_projector	Compatibility wrapper for multivarious::transfer
truncate	Truncate a projection to fewer components
truncate.genpca	Truncate a genpca fit to fewer components