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HeteroGGM  

Gaussian Graphical Model-Based Heterogeneity Analysis
View on CRAN: Click here


Download and install HeteroGGM package within the R console
Install from CRAN:
install.packages("HeteroGGM")

Install from Github:
library("remotes")
install_github("cran/HeteroGGM")

Install by package version:
library("remotes")
install_version("HeteroGGM", "1.0.1")



Attach the package and use:
library("HeteroGGM")
Maintained by
Mingyang Ren
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-11
Latest Update: 2023-10-11
Description:
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.
How to cite:
Mingyang Ren (2021). HeteroGGM: Gaussian Graphical Model-Based Heterogeneity Analysis. R package version 1.0.1, https://cran.r-project.org/web/packages/HeteroGGM. Accessed 05 Jun. 2026.
Previous versions and publish date:
0.1.0 (2021-02-11 10:30), 1.0.1 (2023-10-11 15:10)
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Complete documentation for HeteroGGM
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