matrixCorr: Collection of Correlation and Association Estimators

Compute correlation, association, and agreement measures for small to high-dimensional datasets through a consistent matrix-oriented interface. Supports classical correlations (Pearson, Spearman, Kendall), distance correlation, partial correlation with regularised estimators, shrinkage correlation for p >= n settings, robust correlations including biweight mid-correlation, percentage-bend, and skipped correlation, latent-variable methods for binary and ordinal data, repeated-measures correlation, and agreement analyses based on Bland-Altman methods and Lin's concordance correlation coefficient, including repeated-measures extensions. Implemented with optimized C++ backends using BLAS/OpenMP and memory-aware symmetric updates, and returns standard R objects with print/summary/plot methods plus optional Shiny viewers for matrix inspection. Methods based on Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>; high-dimensional shrinkage covariance estimation <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>; Wilcox (1994) <doi:10.1007/BF02294395>; Wilcox (2004) <doi:10.1080/0266476032000148821>.

Version:	0.10.0
Imports:	Rcpp (≥ 1.1.0), ggplot2 (≥ 3.5.2), Matrix (≥ 1.7.2), cli, rlang
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown, MASS, mnormt, shiny, shinyWidgets, viridisLite, testthat (≥ 3.0.0)
Enhances:	plotly
Published:	2026-04-03
DOI:	10.32614/CRAN.package.matrixCorr
Author:	Thiago de Paula Oliveira [aut, cre]
Maintainer:	Thiago de Paula Oliveira <thiago.paula.oliveira at gmail.com>
BugReports:	https://github.com/Prof-ThiagoOliveira/matrixCorr/issues
License:	MIT + file LICENSE
URL:	https://github.com/Prof-ThiagoOliveira/matrixCorr
NeedsCompilation:	yes
Materials:	README
CRAN checks:	matrixCorr results

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