Type: Package
Package: mglasso
Title: Multiscale Graphical Lasso
Version: 0.1.2
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, ggplot2, ggrepel, gridExtra, Matrix, methods,
        R.utils, reticulate (>= 1.25), rstudioapi
Suggests: knitr, mvtnorm, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
ByteCompile: true
Config/reticulate: list( packages = list( list(package = "scipy",
        version = "1.7.1"), list(package = "numpy", version =
        "1.22.4"), list(package = "matplotlib"), list(package =
        "scikit-learn"), list(package = "six"), list(package =
        "pylearn-parsimony", version = "0.3.1", pip = TRUE) ) )
Encoding: UTF-8
RoxygenNote: 7.2.1
URL: https://desanou.github.io/mglasso/
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2022-09-05 22:06:02 UTC; doedm
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: 2022-09-08 13:12:55 UTC
Built: R 4.6.0; ; 2026-04-26 13:58:33 UTC; unix
