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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>.
| Version: | 2.3.2 |
| Depends: | igraph |
| Published: | 2023-12-19 |
| DOI: | 10.32614/CRAN.package.JGL |
| Author: | Patrick Danaher |
| Maintainer: | Patrick Danaher <pdanaher at uw.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| In views: | GraphicalModels |
| CRAN checks: | JGL results |
| Reference manual: | JGL.pdf |
| Package source: | JGL_2.3.2.tar.gz |
| Windows binaries: | r-devel: JGL_2.3.2.zip, r-release: JGL_2.3.2.zip, r-oldrel: JGL_2.3.2.zip |
| macOS binaries: | r-release (arm64): JGL_2.3.2.tgz, r-oldrel (arm64): JGL_2.3.2.tgz, r-release (x86_64): JGL_2.3.2.tgz, r-oldrel (x86_64): JGL_2.3.2.tgz |
| Old sources: | JGL archive |
| Reverse imports: | fgm |
| Reverse suggests: | EstimateGroupNetwork |
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