CRAN
glassomix 1.2
High dimensional Mixture Graph Models selection
Released Nov 5, 2013 by Anani Lotsi
Dependencies
mvtnorm 1.0-8 glasso 1.10 huge 1.2.7
The package glassomix provides a general framework for network recovering through a model-based soft clustering. It provides functions for parameter estimation via the EM algorithm for Gaussian graphical mixture models in high dimensional setting. The main function is ``glasso.mix'' upon which a model selection is performed. The package estimates the optimum number of mixture components, K and the tuning parameter, lambda, based on the Extended Bayesian Information Criteria (EBIC) via ``select.gm'' function. The graphical structural of the K-networks are also plotted through the function ``gm.plot''
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>glassomix</artifactId>
<version>1.2-b41</version>
</dependency>
</dependencies>
<repositories>
<repository>
<id>bedatadriven</id>
<name>bedatadriven public repo</name>
<url>https://nexus.bedatadriven.com/content/groups/public/</url>
</repository>
</repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:glassomix')
Test Results
This package was last tested against Renjin 0.9.2689 on Aug 27, 2018.