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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.0.1 |
rolling source/ R- | gcpca_0.0.1.tar.gz |
12.7 KiB |
0.0.1 |
rolling linux/jammy R-4.5 | gcpca_0.0.1.tar.gz |
68.5 KiB |
0.0.1 |
latest source/ R- | gcpca_0.0.1.tar.gz |
12.7 KiB |
0.0.1 |
latest linux/jammy R-4.5 | gcpca_0.0.1.tar.gz |
68.5 KiB |
0.0.1 |
2026-04-23 source/ R- | gcpca_0.0.1.tar.gz |
12.7 KiB |
0.0.1 |
2026-04-09 windows/windows R-4.5 | gcpca_0.0.1.zip |
71.4 KiB |