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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 |
| 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 |
| Reference manual: | gcpca.html , gcpca.pdf |
| Package source: | gcpca_0.0.1.tar.gz |
| Windows binaries: | r-devel: gcpca_0.0.1.zip, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): gcpca_0.0.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): gcpca_0.0.1.tgz, r-oldrel (x86_64): gcpca_0.0.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.