Gaussian Graphical Model-Based Heterogeneity Analysis

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., 2021), 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. (2021), 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. (2021). Gaussian Graphical Model-based Heterogeneity Analysis via Penalized Fusion. Biometrics, .


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install.packages("HeteroGGM")

0.1.0 by Mingyang Ren, 3 months ago


Browse source code at https://github.com/cran/HeteroGGM


Authors: Mingyang Ren [aut, cre] , Sanguo Zhang [aut] , Qingzhao Zhang [aut] , Shuangge Ma [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports igraph, Matrix, MASS

Suggests knitr, rmarkdown


See at CRAN