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)