Inference of Multiscale graphical models with neighborhood selection approach. The method is based on solving a convex optimization problem combining a Lasso and fused-group Lasso penalties. This allows to infer simultaneously a conditional independence graph and a clustering partition. The optimization is based on the Continuation with Nesterov smoothing in a Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018) <doi:10.1109/TMI.2018.2829802> implemented in python.
| Version: | 0.1.2 |
| Imports: | corpcor,ggplot2,ggrepel,gridExtra,Matrix, methods,R.utils,reticulate (≥ 1.25),rstudioapi |
| Suggests: | knitr,mvtnorm,rmarkdown,testthat (≥ 3.0.0) |
| Published: | 2022-09-08 |
| DOI: | 10.32614/CRAN.package.mglasso |
| Author: | Edmond Sanou [aut, cre], Tung Le [ctb], Christophe Ambroise [ths], Geneviève Robin [ths] |
| Maintainer: | Edmond Sanou <doedmond.sanou at univ-evry.fr> |
| License: | MIT + fileLICENSE |
| URL: | https://desanou.github.io/mglasso/ |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | mglasso results |