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: 0.1.1
Imports: corpcor, gridExtra, Matrix, methods, R.utils, reticulate, stats
Suggests: knitr, mvtnorm, rmarkdown, testthat (≥ 3.0.0)
Published: 2022-02-21
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
NeedsCompilation: no
Language: en-US
CRAN checks: mglasso results

Documentation:

Reference manual: mglasso.pdf
Vignettes: Multiscale GLasso

Downloads:

Package source: mglasso_0.1.1.tar.gz
Windows binaries: r-devel: mglasso_0.1.1.zip, r-release: mglasso_0.1.1.zip, r-oldrel: mglasso_0.1.1.zip
macOS binaries: r-release (arm64): mglasso_0.1.1.tgz, r-oldrel (arm64): mglasso_0.1.1.tgz, r-release (x86_64): mglasso_0.1.1.tgz, r-oldrel (x86_64): mglasso_0.1.1.tgz

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