gcpca: Generalized Contrastive Principal Component Analysis

Implements dense and sparse generalized contrastive principal component analysis (gcPCA) with S3 fit objects and methods for prediction, summaries, and plotting. The gcPCA is a hyperparameter-free method for comparing high-dimensional datasets collected under different experimental conditions to reveal low-dimensional patterns enriched in one condition compared to the other. Method details are described in de Oliveira, Garg, Hjerling-Leffler, Batista-Brito, and Sjulson (2025) <doi:10.1371/journal.pcbi.1012747>.

Version:	0.0.1
Imports:	graphics, stats
Suggests:	testthat (≥ 3.0.0)
Published:	2026-04-01
DOI:	10.32614/CRAN.package.gcpca (may not be active yet)
Author:	Eliezyer de Oliveira [aut, cre]
Maintainer:	Eliezyer de Oliveira <eliezyer.deoliveira at gmail.com>
BugReports:	https://github.com/SjulsonLab/generalized_contrastive_PCA/issues
License:	MIT + file LICENSE
URL:	https://github.com/SjulsonLab/generalized_contrastive_PCA
NeedsCompilation:	no
CRAN checks:	gcpca results

Documentation:

Reference manual:

gcpca.html , gcpca.pdf

Downloads:

Package source:	gcpca_0.0.1.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:

Please use the canonical form https://CRAN.R-project.org/package=gcpca to link to this page.