CRAN

MIRL 1.0

Multiple Imputation Random Lasso for Variable Selection with Missing Entries

Released Apr 11, 2018 by Ying Liu

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

Dependencies

mice 2.46.0 boot 1.3-20 MASS 7.3-49 glmnet 2.0-16

Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) . It deals with missingness by multiple imputation and produces a selection probability for each variable following stability selection. The user can further choose a threshold for the selection probability to select a final set of variables. The threshold can be picked by cross validation or the user can define a practical threshold for selection probability. If you find this work useful for your application, please cite the method paper.

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>MIRL</artifactId>
    <version>1.0-b2</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:MIRL')

Test Results

This package was last tested against Renjin 0.9.2635 on Apr 26, 2018.

Source

R

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Release History