genpca: Generalized Principal Component Analysis

Generalized PCA and related matrix decompositions in weighted inner-product spaces. Methods are based on Allen, G. I., Grosenick, L., and Taylor, J. (2014) <doi:10.1080/01621459.2013.852978>, "A generalized least-square matrix decomposition", Journal of the American Statistical Association, 109(505), 145-159; and Abdi, H. (2007), "Singular value decomposition (SVD) and generalized singular value decomposition" <https://personal.utdallas.edu/~herve/Abdi-SVD2007-pretty.pdf>, in "Encyclopedia of Measurement and Statistics", 907-912.

Version:	0.2.0
Depends:	R (≥ 4.1.0)
Imports:	Rcpp, eigencore (≥ 1.0.3), FNN, Matrix, multivarious (≥ 0.3.0), assertthat, methods, digest
LinkingTo:	Rcpp, RcppArmadillo, RcppEigen
Suggests:	testthat (≥ 3.0.0), adjoin, irlba, matrixStats, clue, knitr, rmarkdown, ggplot2, albersdown, ragg, systemfonts
Published:	2026-09-15
DOI:	10.32614/CRAN.package.genpca (may not be active yet)
Author:	Brad Buchsbaum [aut, cre, cph]
Maintainer:	Brad Buchsbaum <brad.buchsbaum at gmail.com>
BugReports:	https://github.com/bbuchsbaum/genpca/issues
License:	MIT + file LICENSE
URL:	https://bbuchsbaum.github.io/genpca/, https://github.com/bbuchsbaum/genpca
NeedsCompilation:	yes
Language:	en-US
Materials:	README, NEWS
CRAN checks:	genpca results

Documentation:

Reference manual:	genpca.html , genpca.pdf
Vignettes:	Getting Started with genpca (source, R code) GPCA Metrics: Building M and A (source, R code) GPCA at Scale and Special Cases (source, R code) Generalized PLS-SVD: Explicit Whitening Reference (source, R code) Sparse and Functional PCA with sfpca() (source, R code) Modelling Structured Noise (source, R code)

Downloads:

Package source:	genpca_0.2.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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