CRAN

multiPIM 1.4-3

Variable Importance Analysis with Population Intervention Models

Released Feb 25, 2015 by Stephan Ritter

This package can be loaded by Renjin but 1 out 3 tests failed.

Dependencies

polspline 1.1.12 lars 1.2 penalized 0.9-50 rpart 4.1-13

Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.

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>multiPIM</artifactId>
    <version>1.4-3-b240</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:multiPIM')

Test Results

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

Source

R

View GitHub Mirror

Release History