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  "Title": "Copula Graphical Models for Heterogeneous Mixed Data",
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  "Description": "A multi-core R package that allows for the statistical\nmodeling of multi-group multivariate mixed data using Gaussian\ngraphical models. Combining the Gaussian copula framework with\nthe fused graphical lasso penalty, the 'heteromixgm' package\ncan handle a wide variety of datasets found in various\nsciences. The package also includes an option to perform model\nselection using the AIC, BIC and EBIC information criteria, a\nfunction that plots partial correlation graphs based on the\nselected precision matrices, as well as simulate mixed\nheterogeneous data for exploratory or simulation purposes and\none multi-group multivariate mixed agricultural dataset\npertaining to maize yields. The package implements the\nmethodological developments found in Hermes et al. (2024)\n<doi:10.1080/10618600.2023.2289545>.",
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