mglasso: Multiscale Graphical Lasso

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>
License: MIT + file LICENSE
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:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mglasso_0.1.2.tgz, r-oldrel (arm64): 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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