num_or_na_vec

<p>Display variance component estimation details to the console.</p>

internal

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>.

num_or_na_vec: num_or_na_vec

Description

Usage

Arguments