Matches cases to controls based on genotype principal components (PC). 
      In order to produce better results, matches are based on the weighted 
      distance of PCs where the weights are equal to the % variance explained 
      by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987)
      <DOI:10.1016/0031-3203(87)90066-5>) is used to determine matches.

Implements principal component analysis, orthogonal rotation and multiple
    factor analysis for a mixture of quantitative and qualitative variables.

Fast and Accurate Randomized Singular Value Decomposition (RSVD) methods proposed in the 'PCAone' paper by Li (2023) <https://genome.cshlp.org/content/33/9/1599>.

Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The 'PAM50' classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The 'PCAPAM50' package leverages principal component analysis and iterative 'PAM50' calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.

Provides functions for robust PCA by projection pursuit.
    The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>,
    Croux et al. (2013) <doi:10.1080/00401706.2012.727746>,
    Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.

Provides an R interface to the 'PCATS' API
    <https://pcats.research.cchmc.org/api/__docs__/>,
    allowing R users to submit tasks and retrieve results.

A system for fast, accurate, and flexible whole genome bisulfite sequencing (WGBS) data analysis of two-condition comparisons. Principal Component BiSulfite, 'PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: <https://katlande.github.io/PCBS/articles/Differential_Methylation.html>.