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JGL
View on CRAN: Click
here
Download and install JGL package within the R console
Install from CRAN:
install.packages("JGL")
Install from Github:
library("remotes")
install_github("cran/JGL")
Install by package version:
library("remotes")
install_version("JGL", "2.3.2")
Attach the package and use:
library("JGL")
Maintained by
Patrick Danaher
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-07-27
Latest Update: 2023-12-19
Description:
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)
.
How to cite:
Patrick Danaher (2012). JGL: Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes. R package version 2.3.2, https://cran.r-project.org/web/packages/JGL. Accessed 07 Nov. 2024.
Previous versions and publish date:
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Other R packages that JGL depends,
imports, suggests or enhances
Complete documentation for JGL
Functions, R codes and Examples using
the JGL R package
Some associated functions: JGL-internal . JGL-package . JGL . crit . example.data . gcrit . net.degree . net.edges . net.hubs . net.neighbors . screen.fgl . screen.ggl . subnetworks .
Some associated R codes: crit.R . dsgl.R . flsa.general.R . flsa2.R . gcrit.R . make.adj.matrix.R . net.degree.R . net.edges.R . net.hubs.R . net.neighbors.R . penalty.as.matrix.R . screen.fgl.R . screen.ggl.R . soft.R . subnetworks.R . Full JGL package functions and examples
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