CRAN/E | mglasso

mglasso

Multiscale Graphical Lasso

Installation

About

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.

desanou.github.io/mglasso/

Key Metrics

Version 0.1.2
Published 2022-09-08 736 days ago
Needs compilation? no
License MIT
License File
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Downloads

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Maintainer

Maintainer

Edmond Sanou

Authors

Edmond Sanou

aut / cre

Tung Le

ctb

Christophe Ambroise

ths

Geneviève Robin

ths

Material

NEWS
Reference manual
Package source

Vignettes

Multiscale GLasso

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Old Sources

mglasso archive

Imports

corpcor
ggplot2
ggrepel
gridExtra
Matrix
methods
R.utils
reticulate ≥ 1.25
rstudioapi

Suggests

knitr
mvtnorm
rmarkdown
testthat ≥ 3.0.0