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 univ-evry.fr>
License:	MIT + file LICENSE
URL:	https://desanou.github.io/mglasso/
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	mglasso results

Documentation:

Reference manual:	mglasso.pdf
Vignettes:	Multiscale GLasso

Downloads:

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-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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