matrixCorr - Collection of Correlation, Agreement, and Reliability Estimators
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)
<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>; Hayes and
Krippendorff (2007) <doi:10.1080/19312450709336664>; weighted
repeated-measures correlation by Kondo et al. (2025)
<doi:10.1002/sim.70046>.
