CRAN

JGL 2.3.1

Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes

Released Nov 30, 2018 by Patrick Danaher

Dependencies

igraph 1.2.2

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) .

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>JGL</artifactId>
    <version>2.3.1-b1</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

View build log

Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:JGL')

Test Results

This package was last tested against Renjin 0.9.2716 on Dec 19, 2018.

• JGL-examples
• JGL-package-examples
• example.data-examples
• net.degree-examples
• net.edges-examples
• net.hubs-examples
• net.neighbors-examples
• screen.fgl-examples
• screen.ggl-examples
• subnetworks-examples

Source

View GitHub Mirror

Release History

• 2.3.1
• 2.3
• 2.2
• 2.1