print.summary_latent_corr

Prints compact summary statistics returned by
<code>summary.tetrachoric_corr()</code>, <code>summary.polychoric_corr()</code>,
<code>summary.polyserial_corr()</code>, and <code>summary.biserial_corr()</code>.

Compute correlation, association, and agreement measures for
small to high-dimensional datasets through a consistent matrix-oriented
interface. Supports classical correlations (Pearson, Spearman, Kendall),
distance correlation, partial correlation with regularised estimators,
shrinkage correlation for p >= n settings, robust correlations including
biweight mid-correlation, percentage-bend, and skipped correlation,
latent-variable methods for binary and ordinal data, repeated-measures
correlation, and agreement analyses based on Bland-Altman methods and
Lin's concordance correlation coefficient, including repeated-measures
extensions. 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>.

Thiago de Paula Oliveira

matrixCorr

Collection of Correlation and Association Estimators

print.summary_latent_corr function

<dl><dt>x</dt>
<dd>An object of class <code>summary_latent_corr</code>.</dd>
<dt>digits</dt>
<dd>Integer; number of decimal places to print.</dd>
<dt>...</dt>
<dd>Unused.</dd></dl>

Arguments

Summary Method for Latent Correlation Matrices — print.summary_latent_corr

<dl>

<dt>x</dt>
<dd>An object of class <code>summary_latent_corr</code>.</dd>


<dt>digits</dt>
<dd>Integer; number of decimal places to print.</dd>


<dt>...</dt>
<dd>Unused.</dd>

</dl>

print.summary_latent_corr: Summary Method for Latent Correlation Matrices

Description

Usage

Value

Arguments