CRAN

KODAMA 1.5

Knowledge Discovery by Accuracy Maximization

Released Oct 18, 2018 by Stefano Cacciatore

This package is available for Renjin and there are no known compatibility issues.

Dependencies

Rcpp RcppArmadillo 0.9.100.5.0

KODAMA algorithm is an unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. The algorithm was published by Cacciatore et al. 2014 . Addition functions was introduced by Cacciatore et al. 2017 to facilitate the identification of key features associated with the generated output and are easily interpretable for the user. Cross-validated techniques are also included in this package.

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>KODAMA</artifactId>
    <version>1.5-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:KODAMA')

Test Results

This package was last tested against Renjin 0.9.2692 on Oct 21, 2018.

Source

R
C
C++

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Release History