CRAN

glmmLasso 1.5.1

Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation

Released May 6, 2017 by Andreas Groll

Dependencies

minqa 1.2.4 Matrix 1.2-14

A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.

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>glmmLasso</artifactId>
    <version>1.5.1-b16</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:glmmLasso')

Test Results

This package was last tested against Renjin 0.9.2689 on Aug 26, 2018.

• glmmLassoControl.rd

Source

View GitHub Mirror

Release History

• 1.5.1
• 1.5.0
• 1.4.4
• 1.4.3
• 1.4.1
• 1.3.7
• 1.3.6
• 1.3.5
• 1.3.4
• 1.3.3
• 1.3.2
• 1.3.1
• 1.3.0
• 1.2.2
• 1.2.1
• 1.1.1
• 1.1.0
• 1.0.6
• 1.0.5
• 1.0.4
• 1.0.3
• 1.0.2
• 1.0.1
• 1.0.0