Package: svyROC 1.0.0
svyROC: Estimation of the ROC Curve and the AUC for Complex Survey Data
Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.635>; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) <doi:10.2436/20.8080.02.121>; A. Iparragirre, I. Barrio (2024) <doi:10.1007/978-3-031-65723-8_7>.
Authors:
svyROC_1.0.0.tar.gz
svyROC_1.0.0.zip(r-4.5)svyROC_1.0.0.zip(r-4.4)svyROC_1.0.0.zip(r-4.3)
svyROC_1.0.0.tgz(r-4.4-any)svyROC_1.0.0.tgz(r-4.3-any)
svyROC_1.0.0.tar.gz(r-4.5-noble)svyROC_1.0.0.tar.gz(r-4.4-noble)
svyROC_1.0.0.tgz(r-4.4-emscripten)svyROC_1.0.0.tgz(r-4.3-emscripten)
svyROC.pdf |svyROC.html✨
svyROC/json (API)
NEWS
| # Install 'svyROC' in R: |
| install.packages('svyROC', repos = c('https://aiparragirre.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/aiparragirre/svyroc/issues
- example_data_wroc - Simulated data
- example_variables_wroc - Simulated data
aucauc-optimism-correctioncomplex-survey-dataoptimal-cut-off-pointsroc-curvesampling-weights
Last updated 17 days agofrom:357565b744. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 07 2024 |
| R-4.5-win | OK | Nov 07 2024 |
| R-4.5-linux | OK | Nov 07 2024 |
| R-4.4-win | OK | Nov 07 2024 |
| R-4.4-mac | OK | Nov 07 2024 |
| R-4.3-win | OK | Nov 07 2024 |
| R-4.3-mac | OK | Nov 07 2024 |
Exports:corrected.waucwaucwocpwrocwroc.plotwsewsp
Dependencies:codetoolsDBIforeachglmnetiteratorslatticeMatrixminqamitoolsnumDerivRcppRcppArmadilloRcppEigenshapesurveysurvivalsvyVarSel
