Package: heteromixgm
Type: Package
Title: Copula Graphical Models for Heterogeneous Mixed Data
Imports: Matrix, igraph, parallel, tmvtnorm, glasso, BDgraph, methods,
        stats, utils, MASS
Authors@R: c(person(given = "Sjoerd",
           family = "Hermes",
           role = c("aut", "cre"),
           email = "sjoerd.hermes@wur.nl"), 
           person(given = "Joost", family = "van Heerwaarden", role = "ctb"),
           person(given = "Pariya", family = "Behrouzi", role = "ctb"))
Version: 2.0.2
Maintainer: Sjoerd Hermes <sjoerd.hermes@wur.nl>
Description: 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>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.10)
NeedsCompilation: no
Packaged: 2026-07-18 04:41:26 UTC; root
Author: Sjoerd Hermes [aut, cre], Joost van Heerwaarden [ctb], Pariya
        Behrouzi [ctb]
Repository: https://cran.r-universe.dev
Date/Publication: 2024-08-20 02:52:12 UTC
RemoteUrl: https://github.com/cran/heteromixgm
RemoteRef: HEAD
RemoteSha: 10d12c3706c6bf8757d52b35278233cfa4952f0c