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 + file LICENSE |
| URL: |
https://desanou.github.io/mglasso/ |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
mglasso results |