.compute_gar_core

This function is not exported. It serves as the core computational engine
for evaluating Anti-Robinson (AR), Generalized Anti-Robinson (GAR), and
Relative GAR (RGAR) scores.

internal

Provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) <https://www3.stat.sinica.edu.tw/statistica/J12N1/J12N11/J12N11.html> and further extended by Wu, Tien, and Chen (2010) <doi:10.1016/j.csda.2008.09.029>.

Shu-Yu Lin

GAPR

Generalized Association Plots

.compute_gar_core function

<dl><dt>mat</dt>
<dd>A numeric, symmetric distance matrix.</dd>
<dt>order</dt>
<dd>A permutation vector indicating the new ordering of rows/columns.</dd>
<dt>w</dt>
<dd>An integer window size (if NULL, evaluates all triplets globally).</dd>
<dt>normalize</dt>
<dd>Logical. If TRUE, returns a proportion; otherwise returns the raw count.</dd></dl>

Arguments

Internal function to compute Anti-Robinson or GAR/ RGAR score — .compute_gar_core

<dl>

<dt>mat</dt>
<dd>A numeric, symmetric distance matrix.</dd>


<dt>order</dt>
<dd>A permutation vector indicating the new ordering of rows/columns.</dd>


<dt>w</dt>
<dd>An integer window size (if NULL, evaluates all triplets globally).</dd>


<dt>normalize</dt>
<dd>Logical. If TRUE, returns a proportion; otherwise returns the raw count.</dd>

</dl>

.compute_gar_core: Internal function to compute Anti-Robinson or GAR/ RGAR score

Description

Usage

Value

Arguments