CRAN

deepboost 0.1.6

Deep Boosting Ensemble Modeling

Released Nov 8, 2017 by Daniel Marcous

This package can be loaded by Renjin but 9 out 10 tests failed.

Dependencies

Rcpp

Provides deep boosting models training, evaluation, predicting and hyper parameter optimising using grid search and cross validation. Based on Google's Deep Boosting algorithm, and Google's C++ implementation. Cortes, C., Mohri, M., & Syed, U. (2014) .

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>deepboost</artifactId>
    <version>0.1.6-b9</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

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Renjin CLI

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

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

Test Results

This package was last tested against Renjin 0.9.2644 on Jun 2, 2018.

Source

R
C
C++

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Release History