The gap encodes the distance between clusters and improves
    interpretation of cluster heatmaps. The gaps can be of the same
    distance based on a height threshold to cut the dendrogram. Another
    option is to vary the size of gaps based on the distance between
    clusters.

An excerpt of the data available at Gapminder.org. For each
    of 142 countries, the package provides values for life expectancy, GDP
    per capita, and population, every five years, from 1952 to 2007.

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>.

Implements genetic algorithm-based variable selection for building 
    quantitative structure-activity relationship (QSAR) models. The package provides 
    a workflow for selecting optimal predictor subsets from large 
    descriptor spaces using leave-one-out cross-validation (LOOCV) with Q2 as the 
    fitness criterion. Features include automatic handling of multicollinearity via 
    variance inflation factor (VIF) thresholding, customizable genetic algorithm 
    operators, and diagnostic tools for model evaluation. Supports both training set 
    optimization and external validation, plus nested (double) cross-validation for 
    unbiased performance estimation and predictor stability diagnostics. Built-in 
    visualization functions include Q2 curves and Williams plots to assess model 
    applicability domain. The method is demonstrated in papers predicting 
    antibacterial activity by Araya-Cloutier et al. (2018) 
    <doi:10.1038/s41598-018-27545-4> and Kalli et al. (2021) 
    <doi:10.1038/s41598-021-92964-9>.