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 |
| Reference manual: | mglasso.pdf |
| Vignettes: |
Multiscale GLasso |
| Package source: | mglasso_0.1.2.tar.gz |
| Windows binaries: | r-devel: mglasso_0.1.2.zip, r-release: mglasso_0.1.2.zip, r-oldrel: mglasso_0.1.2.zip |
| macOS binaries: | r-devel (arm64): mglasso_0.1.2.tgz, r-release (arm64): mglasso_0.1.2.tgz, r-oldrel (arm64): mglasso_0.1.2.tgz, r-devel (x86_64): mglasso_0.1.2.tgz, r-release (x86_64): mglasso_0.1.2.tgz, r-oldrel (x86_64): mglasso_0.1.2.tgz |
| Old sources: | mglasso archive |
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