Package: JGL 2.3.2
Patrick Danaher
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>.
Authors:
JGL_2.3.2.tar.gz
JGL_2.3.2.tar.gz(r-4.5-noble)JGL_2.3.2.tar.gz(r-4.4-noble)
JGL_2.3.2.tgz(r-4.4-emscripten)JGL_2.3.2.tgz(r-4.3-emscripten)
JGL.pdf |JGL.html✨
JGL/json (API)
| # Install 'JGL' in R: |
| install.packages('JGL', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- example.data - Toy two-class gene expression dataset.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 11 months agofrom:52fc1717ad. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 14 2024 |
| R-4.5-linux | OK | Nov 14 2024 |
Exports:JGLnet.degreenet.edgesnet.hubsnet.neighborsplot.jglprint.jglscreen.fglscreen.gglsubnetworks
Dependencies:clicpp11glueigraphlatticelifecyclemagrittrMatrixpkgconfigrlangvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Joint Graphical Lasso | JGL-package |
| Calculate the critical value of the FGL objective funciton. | crit |
| Toy two-class gene expression dataset. | example.data |
| Calculate the critical value of the GGL objective funciton. | gcrit |
| Joint Graphical Lasso | JGL |
| List the degree of every node in all classes. | net.degree |
| List the edges in a network | net.edges |
| Get degrees of most connected nodes. | net.hubs |
| Get network neighbors of a node | net.neighbors |
| Quickly identify connected features in the solution to FGL | screen.fgl |
| Quickly identify connected features in the solution to GGL | screen.ggl |
| Identify subnetwork membership | subnetworks |