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-4.5)sorocs_0.1.0.zip(r-4.4)sorocs_0.1.0.zip(r-4.3)
sorocs_0.1.0.tgz(r-4.4-any)sorocs_0.1.0.tgz(r-4.3-any)
sorocs_0.1.0.tar.gz(r-4.5-noble)sorocs_0.1.0.tar.gz(r-4.4-noble)
sorocs_0.1.0.tgz(r-4.4-emscripten)sorocs_0.1.0.tgz(r-4.3-emscripten)
sorocs.pdf |sorocs.html✨
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
- 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 5 years agofrom:6cdd45c789. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 06 2024 |
| R-4.5-win | OK | Nov 06 2024 |
| R-4.5-linux | OK | Nov 06 2024 |
| R-4.4-win | OK | Nov 06 2024 |
| R-4.4-mac | OK | Nov 06 2024 |
| R-4.3-win | OK | Nov 06 2024 |
| R-4.3-mac | OK | Nov 06 2024 |
Exports:sorocs
Dependencies:codalatticeMASSMatrixMatrixModelsmcmcMCMCpackmvtnormquantregSparseMsurvival
