Package: clusterGGM 0.2.0

clusterGGM: Sparse Gaussian Graphical Modeling with Variable Clustering

Perform sparse estimation of a Gaussian graphical model (GGM) with node aggregation through variable clustering. Currently, the package implements the clusterpath estimator of the Gaussian graphical model (CGGM) (Touw, Alfons, Groenen & Wilms, 2025; <doi:10.48550/arXiv.2407.00644>).

Authors:Daniel J.W. Touw [aut], Andreas Alfons [aut, cre], Ines Wilms [aut], Patrick J.F. Groenen [ths]

clusterGGM_0.2.0.tar.gz
clusterGGM_0.2.0.zip(r-4.7-x86_64)clusterGGM_0.2.0.zip(r-4.7-arm64)clusterGGM_0.2.0.zip(r-4.6-x86_64)clusterGGM_0.2.0.zip(r-4.6-arm64)clusterGGM_0.2.0.zip(r-4.5-x86_64)
clusterGGM_0.2.0.tgz(r-4.6-x86_64)clusterGGM_0.2.0.tgz(r-4.6-arm64)clusterGGM_0.2.0.tgz(r-4.5-x86_64)clusterGGM_0.2.0.tgz(r-4.5-arm64)
clusterGGM_0.2.0.tar.gz(r-4.7-arm64)clusterGGM_0.2.0.tar.gz(r-4.7-x86_64)clusterGGM_0.2.0.tar.gz(r-4.6-arm64)clusterGGM_0.2.0.tar.gz(r-4.6-x86_64)
clusterGGM_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
clusterGGM/json (API)

# Install 'clusterGGM' in R:

install.packages('clusterGGM', repos = c('https://aalfons.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/aalfons/clusterggm/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

openblas cpp

3.78 score 1 stars 263 downloads 12 exports 2 dependencies

Last updated from:521ac18826. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	128
linux-devel-x86_64	OK	155
source / vignettes	OK	312
linux-release-arm64	OK	125
linux-release-x86_64	OK	149
macos-release-arm64	OK	170
macos-release-x86_64	OK	373
macos-oldrel-arm64	OK	111
macos-oldrel-x86_64	OK	334
windows-devel-arm64	OK	255
windows-devel-x86_64	OK	214
windows-release-arm64	OK	264
windows-release-x86_64	OK	182
windows-oldrel-x86_64	OK	161
wasm-release	OK	133

Exports:cggm cggm_cov cggm_cov_cv cggm_cv cggm_refit clusterpath_weights cv_folds get_clusters get_covariance get_precision lasso_weights min_clusters

Dependencies:Rcpp RcppArmadillo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Sparse Gaussian Graphical Modeling with Variable Clustering	clusterGGM-package clusterGGM
Clusterpath Estimator of the Gaussian Graphical Model	cggm cggm_cov
Cross Validation for the Clusterpath Estimator of the Gaussian Graphical Model	cggm_cov_cv cggm_cv
Refit the Gaussian Graphical Model for a Given Aggregation and Sparsity Structure	cggm_refit
Compute the Weight Matrix for the Clusterpath Penalty	clusterpath_weights
Create Cross-Validation Folds	cv_folds
Extract the Cluster Assignment	get_clusters get_clusters.CGGM_CV get_clusters.CGGM_path
Extract the Estimated Precision or Covariance Matrix	get_covariance get_covariance.CGGM_CV get_covariance.CGGM_path get_precision get_precision.CGGM_CV get_precision.CGGM_path
Compute the Weight Matrix for the Lasso Penalty	lasso_weights
Calculate the Minimum Number of Clusters	min_clusters