[prof-thiagooliveira] matrixCorr 0.12.3

thiago.paula.oliveira@gmail.com (Thiago de Paula Oliveira) — Mon, 28 Sep 2026 07:17:23 GMT

Compute correlation, association, agreement, and reliability measures for small to high-dimensional datasets through a consistent matrix-oriented interface. Supports classical correlations (Pearson, Spearman, Kendall, Chatterjee's rank correlation), distance correlation, partial correlation with regularised estimators, shrinkage correlation for p >= n settings, robust correlations including biweight mid-correlation, percentage-bend, Winsorized, and skipped correlation, latent-variable methods for binary and ordinal data, pairwise and overall intraclass correlation for wide data, repeated-measures correlation, and agreement/reliability analyses based on Cohen's kappa, weighted kappa, multi-rater kappa, Gwet's AC1/AC2, Krippendorff's alpha, Bland-Altman methods, Lin's concordance correlation coefficient, Poisson GLMM concordance for count data, and repeated-measures intraclass/concordance correlation, including robust concordance based on minimum covariance determinant estimates. 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) ; high-dimensional shrinkage covariance estimation ; Lin (1989) ; Wilcox (1994) ; Wilcox (2004) ; Hayes and Krippendorff (2007) ; weighted repeated-measures correlation by Kondo et al. (2025) .

oliveiratp.r-universe.dev

[prof-thiagooliveira] matrixCorr 0.12.3