HeteroGGM: 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., 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.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.4) |
| Imports: |
igraph, Matrix, MASS, huge |
| Suggests: |
knitr, rmarkdown |
| Published: |
2023-10-11 |
| Author: |
Mingyang Ren 
[aut, cre],
Sanguo Zhang [aut],
Qingzhao Zhang [aut],
Shuangge Ma [aut] |
| Maintainer: |
Mingyang Ren <renmingyang17 at mails.ucas.ac.cn> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
HeteroGGM results |
Documentation:
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Reverse dependencies:
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