Calculates the number of Anti-Robinson violations within a specified window <code>w</code>,
allowing evaluation of local structural consistency in the reordered matrix.

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

GAR function

<dl><dt>mat</dt>
<dd>A symmetric numeric distance matrix.</dd>
<dt>order</dt>
<dd>A permutation vector specifying the new row/column order.</dd>
<dt>w</dt>
<dd>Window size (integer). If NULL, uses global comparisons (equivalent to AR).</dd></dl>

Arguments

Compute the Generalized Anti-Robinson (GAR) score — GAR

<dl>

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


<dt>order</dt>
<dd>A permutation vector specifying the new row/column order.</dd>


<dt>w</dt>
<dd>Window size (integer). If NULL, uses global comparisons (equivalent to AR).</dd>

</dl>

GAR: Compute the Generalized Anti-Robinson (GAR) score

Description

Usage

Value

Arguments