Package: JGL
Type: Package
Title: Performs the Joint Graphical Lasso for Sparse Inverse Covariance
        Estimation on Multiple Classes
Version: 2.3.2
Date: 2023-12-04
Author: Patrick Danaher
Maintainer: Patrick Danaher <pdanaher@uw.edu>
Description: The Joint Graphical Lasso is a generalized method for
        estimating Gaussian graphical models/ sparse inverse covariance
        matrices/ biological networks on multiple classes of data.  We
        solve JGL under two penalty functions: The Fused Graphical
        Lasso (FGL), which employs a fused penalty to encourage inverse
        covariance matrices to be similar across classes, and the Group
        Graphical Lasso (GGL), which encourages similar network
        structure between classes.  FGL is recommended over GGL for
        most applications. Reference: Danaher P, Wang P, Witten DM. (2013) 
        <doi:10.1111/rssb.12033>.
Depends: igraph
License: MIT + file LICENSE
LazyLoad: yes
Packaged: 2023-12-19 00:02:36 UTC; pdanaher
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2023-12-19 05:50:06 UTC
RoxygenNote: 7.2.3
Encoding: UTF-8