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JGL package

This package runs the Joint Graphical Lasso (JGL) method for estimating sparse inverse covariance matrices across multiple similar datasets.

Reference:

Danaher P, Wang P, Witten DM. The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2014 Mar 1;76(2):373-97.

Install the package from CRAN with install.packages("JGL")

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Version

Install

install.packages('JGL')

Monthly Downloads

265

Version

2.3.2

License

MIT + file LICENSE

Maintainer

Patrick Danaher

Last Published

December 19th, 2023

Functions in JGL (2.3.2)

screen.ggl

Quickly identify connected features in the solution to GGL
screen.fgl

Quickly identify connected features in the solution to FGL
gcrit

Calculate the critical value of the GGL objective funciton.
JGL-internal

Internal JGL functions
net.hubs

Get degrees of most connected nodes.
net.degree

List the degree of every node in all classes.
net.neighbors

Get network neighbors of a node
crit

Calculate the critical value of the FGL objective funciton.
net.edges

List the edges in a network
JGL

Joint Graphical Lasso
subnetworks

Identify subnetwork membership
JGL-package

Joint Graphical Lasso
example.data

Toy two-class gene expression dataset.