Type: Package
Package: mglasso
Title: Multiscale Graphical Lasso
Version: 0.1.1
Authors@R: c(
    person("Edmond", "Sanou", , "doedmond.sanou@univ-evry.fr", role = c("aut", "cre")),
    person("Tung", "Le", role = "ctb"),
    person("Christophe", "Ambroise", role = "ths"),
    person("Geneviève", "Robin", role = "ths")
  )
Description: 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.
License: MIT + file LICENSE
Imports: corpcor, gridExtra, Matrix, methods, R.utils, reticulate,
        stats
Suggests: knitr, mvtnorm, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
ByteCompile: true
Config/reticulate: list( packages = list( list(package = c("")) ) )
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.1.2
NeedsCompilation: no
Packaged: 2022-02-18 15:43:51 UTC; doedmond.sanou
Author: Edmond Sanou [aut, cre],
  Tung Le [ctb],
  Christophe Ambroise [ths],
  Geneviève Robin [ths]
Maintainer: Edmond Sanou <doedmond.sanou@univ-evry.fr>
Repository: CRAN
Date/Publication: 2022-02-21 08:20:02 UTC
