DPWeibull: Dirichlet Process Weibull Mixture Model for Survival Data

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.

Version: 1.2
Depends: Rcpp (≥ 0.12.4), truncdist, DPpackage, matrixStats
LinkingTo: Rcpp
Published: 2018-03-22
Author: Yushu Shi, Michael Martens
Maintainer: Yushu Shi <shiyushu2006 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: DPWeibull results


Reference manual: DPWeibull.pdf
Package source: DPWeibull_1.2.tar.gz
Windows binaries: r-devel: DPWeibull_1.2.zip, r-release: DPWeibull_1.2.zip, r-oldrel: DPWeibull_1.2.zip
OS X binaries: r-release: DPWeibull_1.2.tgz, r-oldrel: DPWeibull_1.2.tgz
Old sources: DPWeibull archive


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