CRAN

randnet 0.2

Random Network Model Selection and Parameter Tuning

Released Feb 12, 2019 by Tianxi Li

This package can be loaded by Renjin but 1 out 15 tests failed.

Dependencies

Matrix 1.2-15 irlba 2.3.3 entropy 1.2.1 poweRlaw 0.70.2 RSpectra 0.13-1 AUC 0.3.0

Model selection and parameter tuning procedures for a class of random network models. The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) , likelihood ratio method from Wang and Bickel (2015) , spectral methods from Le and Levina (2015) . Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 ) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 ).

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>randnet</artifactId>
    <version>0.2-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:randnet')

Test Results

This package was last tested against Renjin 0.9.2724 on Feb 14, 2019.

Source

R

View GitHub Mirror

Release History