matrixCorr - Collection of Correlation and Association Estimators

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.

- R
Published by Prof-ThiagoOliveira 6 months ago