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, 2026) and the tree-aggregated graphical lasso (tag-lasso) (Wilms & Bien, 2022).
More information on this method can be found in the following article:
D.J.W. Touw, A. Alfons, P.J.F. Groenen, and I. Wilms (2026). Clusterpath Gaussian Graphical Modeling. Journal of Computational and Graphical Statistics. doi: 10.1080/10618600.2026.2653764
I. Wilms and J. Bien (2022) Tree-based Node Aggregation in Sparse Graphical Models. Journal of Machine Learning Research, 23(243), 1--36. https://jmlr.org/papers/v23/21-0105.html
Package clusterGGM is on CRAN (The Comprehensive R Archive Network),
hence the latest release can be easily installed from the R command
line via
install.packages("clusterGGM")
To install the latest (possibly unstable) development version from
GitHub, you can pull this repository and install it from the R command
line via
install.packages("remotes")
remotes::install_github("aalfons/clusterGGM")
If you already have package remotes installed, you can skip the first line. Moreover, package clusterGGM contains C++ code that needs to be compiled, so you may need to download and install the necessary tools for MacOS or the necessary tools for Windows.
If you experience any bugs or issues or if you have any suggestions for additional features, please submit an issue via the Issues tab of this repository. Please have a look at existing issues first to see if your problem or feature request has already been discussed.
If you want to contribute to the package, you can fork this repository and create a pull request after implementing the desired functionality.
If you need help using the package, or if you are interested in collaborations related to this project, please get in touch with the package maintainer.