doi:10.1016/S0047-259X(03)00096-4>; Schäfer and Strimmer (2005) <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>.">

matrixCorr: Collection of Correlation and Association Estimators (original) (raw)

Compute correlation and other association matrices from small to high-dimensional datasets with relative simple functions and sensible defaults. Includes options for shrinkage and robustness to improve results in noisy or high-dimensional settings (p >= n), plus convenient print/plot methods for inspection. Implemented with optimised C++ backends using BLAS/OpenMP and memory-aware symmetric updates. Works with base matrices and data frames, returning standard R objects via a consistent S3 interface. Useful across genomics, agriculture, and machine-learning workflows. Supports Pearson, Spearman, Kendall, distance correlation, partial correlation, and robust biweight mid-correlation; Bland–Altman analyses and Lin's concordance correlation coefficient (including repeated-measures extensions). Methods based on Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>; Schäfer and Strimmer (2005) <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>.

Version: 0.6.2
Imports: Rcpp (≥ 1.1.0), ggplot2 (≥ 3.5.2), Matrix (≥ 1.7.2)
LinkingTo: cpp11, Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, MASS, viridisLite
Published: 2025-11-15
DOI: 10.32614/CRAN.package.matrixCorr
Author: Thiago de Paula OliveiraORCID iD [aut, cre]
Maintainer: Thiago de Paula Oliveira <thiago.paula.oliveira at gmail.com>
BugReports: https://github.com/Prof-ThiagoOliveira/matrixCorr/issues
License: MIT + file
URL: https://github.com/Prof-ThiagoOliveira/matrixCorr
NeedsCompilation: yes
Materials: README
CRAN checks: matrixCorr results

Documentation:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=matrixCorrto link to this page.