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: |
2.0.1 |
| Depends: |
R (≥ 3.10) |
| Imports: |
Matrix, igraph, parallel, tmvtnorm, glasso, BDgraph, methods, stats, utils, MASS |
| Published: |
2024-08-01 |
| 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 |
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