JGL: Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes

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>.

Version:	2.3.1
Depends:	igraph
Published:	2018-11-30
Author:	Patrick Danaher
Maintainer:	Patrick Danaher <pdanaher at uw.edu>
License:	GPL-2
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	JGL results

Documentation:

Reference manual:

JGL.pdf

Downloads:

Package source:	JGL_2.3.1.tar.gz
Windows binaries:	r-devel: JGL_2.3.1.zip, r-release: JGL_2.3.1.zip, r-oldrel: JGL_2.3.1.zip
macOS binaries:	r-release (arm64): JGL_2.3.1.tgz, r-oldrel (arm64): JGL_2.3.1.tgz, r-release (x86_64): JGL_2.3.1.tgz, r-oldrel (x86_64): JGL_2.3.1.tgz
Old sources:	JGL archive

Reverse dependencies:

Reverse depends:	JointNets, pGMGM
Reverse imports:	fgm
Reverse suggests:	EstimateGroupNetwork

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