Copula Graphical Models for Heterogeneous Mixed Data
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- WARNING12 OK, 1 WARNING2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 57%4 run in a plain check · 3 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Dependencies
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2023-02-27
- Total releases
- 5 / 3 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.10
- Bundled data
- 2.4 KB / 1 file
- Download size
- 19 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("heteromixgm")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.