A comprehensive suite of spatial 
  functions created to analyze and assess data heterogeneity and climate variability 
  in spatial datasets. This package is specifically designed to address the challenges associated
  with characterizing and understanding complex spatial patterns in environmental and climate-related data.

The goal of this package is to user-friendly realizing Gaussian 
             graphical model-based heterogeneity analysis.
             Recently, several Gaussian graphical model-based heterogeneity 
             analysis techniques have been developed. A common methodological limitation 
             is that the number of subgroups is assumed to be known a priori, which 
             is not realistic. In a very recent study (Ren et al., 2022), a novel approach 
             based on the penalized fusion technique is developed to fully 
             data-dependently determine the number and structure of subgroups in 
             Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing 
             the Gaussian graphical model technique in more practical settings. Beyond 
             Ren et al. (2022), more estimations and functions are added, so 
             that the package is self-contained and more comprehensive and can 
             provide ``more direct'' insights to practitioners (with the 
             visualization function). Reference: 
             Ren, M., Zhang S., Zhang Q. and Ma S. (2022). Gaussian Graphical 
             Model-based Heterogeneity Analysis via Penalized Fusion. 
             Biometrics, 78 (2), 524-535.

Published meta-analyses routinely present one of the
   measures of heterogeneity introduced in Higgins and Thompson
   (2002) <doi:10.1002/sim.1186>. For critiquing articles
   it is often better to convert to another of
   those measures. Some
   conversions are provided here and confidence intervals are also
   available.

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>.