---
title: "Package sorocs"
author: "Zhen Chen, Beom Seuk Hwang, Weimin Zhang" 
date: "`r Sys.Date()`" 
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Package sorocs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(sorocs)

```
Description: A Bayesian nonparametric Dirichlet process mixtures to estimate the receiver operating characteristic (ROC)  surfaces and the associated volume under the surface (VUS), a summary measure similar to the area under the 
 curve measure for ROC curves. To model distributions flexibly, including their skewness and multi-modality
 characteristics a Bayesian nonparametric Dirichlet process mixtures was used. Between-setting correlations is handled
 by dependent Dirichlet process mixtures that have bivariate distributions with nonzero
 correlations as their bases. To  accommodate ordering constraints, the stochastic ordering in the
 context of mixture distributions was adopted.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. <https://doi.org/10.1111/biom.12997>.
 