CRAN

QICD 1.2.0

Estimate the Coefficients for Non-Convex Penalized Quantile Regression Model by using QICD Algorithm

Released Apr 18, 2017 by Bo Peng

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

Extremely fast algorithm "QICD", Iterative Coordinate Descent Algorithm for High-dimensional Nonconvex Penalized Quantile Regression. This algorithm combines the coordinate descent algorithm in the inner iteration with the majorization minimization step in the outside step. For each inner univariate minimization problem, we only need to compute a one-dimensional weighted median, which ensures fast computation. Tuning parameter selection is based on two different method: the cross validation and BIC for quantile regression model. Details are described in Peng,B and Wang,L. (2015) .

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>QICD</artifactId>
    <version>1.2.0-b16</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:QICD')

Test Results

This package was last tested against Renjin 0.9.2644 on Jun 1, 2018.

Source

R
C
C++
Fortran

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Release History