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.

Creates, fits and samples Pair-Copula Bayesian networks (PCBN)
    under some restrictions on the underlying Directed Acyclic Graph (DAG),
    that is, no active cycles nor interfering v-structures, following 
    Derumigny, Horsman and Kurowicka (2025) <doi:10.48550/arXiv.2510.03518>.

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>.

An implementation of the pediatric complex chronic conditions (CCC)
    classification system using R and C++.

Implements the Principal Components Difference-in-Differences estimators as described in Chan, M. K., & Kwok, S. S. (2022) <doi:10.1080/07350015.2021.1914636>.

Implements methods to automate the Auer-Gervini graphical
  Bayesian approach for determining the number of significant
  principal components. Automation uses clustering, change points, or
  simple statistical models to distinguish "long" from "short" steps
  in a graph showing the posterior number of components as a function
  of a prior parameter. See <doi:10.1101/237883>.

Contains the functions for construction and visualization of various families 
    of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2),
    for computing the graph invariants for testing the patterns of segregation and association against complete spatial randomness (CSR)
    or uniformity in one, two and three dimensional cases.
    The package also has tools for generating points from these spatial patterns.
    The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011)
    <doi:10.1080/03610921003597211>) and arc density (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>;
    Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). The PCD families considered are Arc-Slice PCDs,
    Proportional-Edge PCDs, and Central Similarity PCDs.