inform_if_verbose

internal

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>.

inform_if_verbose: Inform only when verbose

Description

Usage

Arguments