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
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

Downloads:

Reference manual: sorocs.pdf
Vignettes: Package sorocs
Package source: sorocs_0.1.0.tar.gz
Windows binaries: r-devel: sorocs_0.1.0.zip, r-devel-gcc8: sorocs_0.1.0.zip, r-release: sorocs_0.1.0.zip, r-oldrel: sorocs_0.1.0.zip
OS X binaries: r-release: sorocs_0.1.0.tgz, r-oldrel: sorocs_0.1.0.tgz

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