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.
- Version2.3.2
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Last release12/19/2023
Documentation
Team
Patrick Danaher
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Reverse Imports1 package
- Reverse Suggests1 package