Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes
Calculate the critical value of the FGL objective funciton.
Calculate the critical value of the GGL objective funciton.
Internal JGL functions
Joint Graphical Lasso
Joint Graphical Lasso
List the degree of every node in all classes.
List the edges in a network
Get degrees of most connected nodes.
Get network neighbors of a node
Quickly identify connected features in the solution to FGL
Quickly identify connected features in the solution to GGL
Identify subnetwork membership
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>.