swMATH ID: 27159
Software Authors: Patrick Danaher
Description: R package JGL: Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes. 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>.
Homepage: https://cran.r-project.org/web/packages/JGL/index.html
Source Code: https://github.com/cran/JGL
Dependencies: R
Related Software: RidgeFusion; RSpectra; Rcpp; Rbgl; gRbase; sfsmisc; snowfall; fdrtool; Hmisc; ggplot2; reshape; expm; Pgmpy; Skggm; Python; igraph; pGMGM; beam; GGMridge; GeneNet
Cited in: 1 Publication

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