JGL: Performs the Joint Graphical Lasso for sparse inverse covariance estimation on multiple classes
Version 2.3

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.

Package details

AuthorPatrick Danaher
Date of publication2013-04-16 21:27:09
MaintainerPatrick Danaher <pdanaher@uw.edu>
Package repositoryView on CRAN
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JGL documentation built on May 29, 2017, 9:05 p.m.