heteromixgm: Copula Graphical Models for Heterogeneous Mixed Data

A multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the 'heteromixgm' package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, a function that plots partial correlation graphs based on the selected precision matrices, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2024) <doi:10.1080/10618600.2023.2289545>.

Version: 2.0.2
Depends: R (≥ 3.10)
Imports: Matrix, igraph, parallel, tmvtnorm, glasso, BDgraph, methods, stats, utils, MASS
Published: 2024-08-19
DOI: 10.32614/CRAN.package.heteromixgm
Author: Sjoerd Hermes [aut, cre], Joost van Heerwaarden [ctb], Pariya Behrouzi [ctb]
Maintainer: Sjoerd Hermes <sjoerd.hermes at wur.nl>
License: GPL-3
NeedsCompilation: no
CRAN checks: heteromixgm results

Documentation:

Reference manual: heteromixgm.pdf

Downloads:

Package source: heteromixgm_2.0.2.tar.gz
Windows binaries: r-devel: heteromixgm_2.0.2.zip, r-release: heteromixgm_2.0.2.zip, r-oldrel: heteromixgm_2.0.2.zip
macOS binaries: r-release (arm64): heteromixgm_2.0.2.tgz, r-oldrel (arm64): heteromixgm_2.0.2.tgz, r-release (x86_64): heteromixgm_2.0.2.tgz, r-oldrel (x86_64): heteromixgm_2.0.2.tgz
Old sources: heteromixgm archive

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