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sorocs: A Bayesian Semiparametric Approach to Correlated ROC Surfaces

A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.

Version:0.1.0
Imports:MASS,MCMCpack,mvtnorm
Suggests:knitr,rmarkdown
Published:2020-03-13
DOI:10.32614/CRAN.package.sorocs
Author:Zhen Chen [aut], Beom Seuk Hwang [aut], Weimin Zhang [cre]
Maintainer:Weimin Zhang <zhangwm at hotmail.com>
BugReports:http://github.com/wzhang17/sorocs/issues
License:GPL-3
URL:http://github.com/wzhang17/sorocs.git
NeedsCompilation:no
CRAN checks:sorocs results

Documentation:

Reference manual:sorocs.html ,sorocs.pdf
Vignettes:Package sorocs (source,R code)

Downloads:

Package source: sorocs_0.1.0.tar.gz
Windows binaries: r-devel:sorocs_0.1.0.zip, r-release:sorocs_0.1.0.zip, r-oldrel:sorocs_0.1.0.zip
macOS binaries: r-release (arm64):sorocs_0.1.0.tgz, r-oldrel (arm64):sorocs_0.1.0.tgz, r-release (x86_64):sorocs_0.1.0.tgz, r-oldrel (x86_64):sorocs_0.1.0.tgz

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=sorocsto link to this page.


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