Package: HeteroGGM 1.0.1
Mingyang Ren
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.
Authors:
HeteroGGM_1.0.1.tar.gz
HeteroGGM_1.0.1.tar.gz(r-4.5-noble)HeteroGGM_1.0.1.tar.gz(r-4.4-noble)
HeteroGGM_1.0.1.tgz(r-4.4-emscripten)HeteroGGM_1.0.1.tgz(r-4.3-emscripten)
HeteroGGM.pdf |HeteroGGM.html✨
HeteroGGM/json (API)
# Install 'HeteroGGM' in R: |
install.packages('HeteroGGM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- example.data - Some example data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:adddaf0eb0. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 04 2024 |
R-4.5-linux | OK | Dec 04 2024 |
Exports:FGGMFGGM.refitgenelambda.obogenerate.dataGGMPFlinked_node_namesPGGMBCplot_networkPower.law.networksummary_network
Dependencies:clicpp11gluehugeigraphlatticelifecyclemagrittrMASSMatrixpkgconfigRcppRcppEigenrlangvctrs
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Some example data | example.data |
Fused Gaussian graphical model. | FGGM |
Refitting of FGGM | FGGM.refit |
Generate tuning parameters | genelambda.obo |
Data Generation | generate.data |
GGM-based heterogeneity analysis. | GGMPF |
Indexes the names of all nodes connected to some particular nodes in a subgroup. | linked_node_names |
Penalized GGM-based clustering. | PGGMBC |
Visualization of network structures. | plot_network |
Power law network | Power.law.network |
The summary of the resulting network structures. | summary_network |