CRAN

TVsMiss 0.1.1

Variable Selection for Missing Data

Released Apr 5, 2018 by Yang Yang

This package can be loaded by Renjin but 3 out 4 tests failed.

Dependencies

Rcpp glmnet 2.0-16

Use a regularization likelihood method to achieve variable selection purpose. Likelihood can be worked with penalty lasso, smoothly clipped absolute deviations (SCAD), and minimax concave penalty (MCP). Tuning parameter selection techniques include cross validation (CV), Bayesian information criterion (BIC) (low and high), stability of variable selection (sVS), stability of BIC (sBIC), and stability of estimation (sEST). More details see Jiwei Zhao, Yang Yang, and Yang Ning (2018) "Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data." Statistica Sinica.

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>TVsMiss</artifactId>
    <version>0.1.1-b4</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:TVsMiss')

Test Results

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

Source

R
C

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Release History