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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=mglasso to link to this page.
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