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 |
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-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 |
Please use the canonical form https://CRAN.R-project.org/package=sorocs to link to this page.