CRAN

DPWeibull 1.3

Dirichlet Process Weibull Mixture Model for Survival Data

Released Feb 1, 2019 by Yushu Shi

This package can be loaded by Renjin but no tests could be found for the package.

Dependencies

truncdist 1.0-2 DPpackage 1.1-7.4 matrixStats 0.54.0 Rcpp

Use Dirichlet process Weibull mixture model and dependent Dirichlet process Weibull mixture model for survival data with and without competing risks. Dirichlet process Weibull mixture model is used for data without covariates and dependent Dirichlet process model is used for regression data. The package is designed to handle exact/right-censored/ interval-censored observations without competing risks and exact/right-censored observations for data with competing risks. Inside each cluster of Dirichlet process, we assume a multiplicative effect of covariates as in Cox model and Fine and Gray model. In addition, we provide a wrapper for DPdensity() function from the R package 'DPpackage'. This wrapper automatically uses Low Information Omnibus prior and can model one and two dimensional data with Dirichlet mixture of Gaussian distributions.

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>DPWeibull</artifactId>
    <version>1.3-b1</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:DPWeibull')

Source

C++

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