Datasets associated with the 'gap' package. Currently,
             it includes an example data for regional association
             plot (CDKN), an example data for a genomewide association
             meta-analysis (OPG), data in studies of Parkinson's diease (PD),
             ALHD2 markers and alcoholism (aldh2), APOE/APOC1 markers
             and Schizophrenia (apoeapoc), cystic fibrosis (cf), a
             Olink/INF panel (inf1), Manhattan plots with (hr1420, mhtdata)
             and without (w4) gene annotations.

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>.

Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.