Package: sorocs 0.1.0
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>.
Authors:
sorocs_0.1.0.tar.gz
sorocs_0.1.0.zip(r-4.7-any)sorocs_0.1.0.zip(r-4.6-any)sorocs_0.1.0.zip(r-4.5-any)
sorocs_0.1.0.tgz(r-4.6-any)sorocs_0.1.0.tgz(r-4.5-any)
sorocs_0.1.0.tar.gz(r-4.7-any)sorocs_0.1.0.tar.gz(r-4.6-any)
sorocs_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
sorocs/json (API)
| # Install 'sorocs' in R: |
| install.packages('sorocs', repos = c('https://wzhang17.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/wzhang17/sorocs/issues
Datasets:
- asrm - The example data is meant to represent the dataset supplied by the Physician Reliability Study (PRS), which is explored in Section 5 of the paper. The 'sampledata' file contains the following variables:
Last updated from:6cdd45c789. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 109 | ||
| source / vignettes | OK | 136 | ||
| linux-release | OK | 119 | ||
| macos-release | OK | 110 | ||
| macos-oldrel | OK | 100 | ||
| windows-devel | OK | 93 | ||
| windows-release | OK | 84 | ||
| windows-oldrel | OK | 94 | ||
| wasm-release | OK | 98 |
Exports:sorocs
Dependencies:codalatticeMASSMatrixMatrixModelsmcmcMCMCpackmvtnormquantregSparseMsurvival
