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 


Author  Patrick Danaher 
Date of publication  20130416 21:27:09 
Maintainer  Patrick Danaher <pdanaher@uw.edu> 
License  GPL2 
Version  2.3 
Package repository  View on CRAN 
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