print.jgl

plot.jgl

internal

JGL-internal

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>.

Patrick Danaher

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

JGL-internal function

<p>Patrick Danaher, Pei Wang and Daniela Witten</p>

Author

Internal JGL functions — JGL-internal

Internal JGL functions

JGL-internal: Internal JGL functions

Description

Usage

Arguments

Author