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. 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>.
Last updated
agreementconcordancecorrelationcorrelation-analysiscorrelation-coefficientcppdistance-measuresintraclass-correlationopenblascppopenmp
6.29 score 2 stars 13 scripts 178 downloads
lcc - Advanced Analysis of Longitudinal Data Using the Concordance Correlation Coefficient
Methods for assessing agreement between repeated measurements obtained by two or more methods using the longitudinal concordance correlation coefficient (LCC). Polynomial mixed-effects models (via 'nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) <doi:10.1007/s13253-018-0321-1>.
Last updated
agreementconcordanceconcordance-analysislongitudinal
3.00 score 8 scripts 188 downloads