Package: fullROC 0.1.1
Yueran Yang
fullROC: Plot Full ROC Curves using Eyewitness Lineup Data
Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC). References: Yueran Yang & Andrew Smith. (2022). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". Behavior Research Methods. <doi:10.3758/s13428-022-01807-6>, Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. <doi:10.1177/1745691620902426>.
Authors:
fullROC_0.1.1.tar.gz
fullROC_0.1.1.zip(r-4.5)fullROC_0.1.1.zip(r-4.4)fullROC_0.1.1.zip(r-4.3)
fullROC_0.1.1.tgz(r-4.4-any)fullROC_0.1.1.tgz(r-4.3-any)
fullROC_0.1.1.tar.gz(r-4.5-noble)fullROC_0.1.1.tar.gz(r-4.4-noble)
fullROC_0.1.1.tgz(r-4.4-emscripten)fullROC_0.1.1.tgz(r-4.3-emscripten)
fullROC.pdf |fullROC.html✨
fullROC/json (API)
NEWS
# Install 'fullROC' in R: |
install.packages('fullROC', repos = c('https://yuerany.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yuerany/fullroc/issues
Last updated 2 years agofrom:3d529553e8. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 29 2024 |
R-4.5-win | OK | Oct 29 2024 |
R-4.5-linux | OK | Oct 29 2024 |
R-4.4-win | OK | Oct 29 2024 |
R-4.4-mac | OK | Oct 29 2024 |
R-4.3-win | OK | Oct 29 2024 |
R-4.3-mac | OK | Oct 29 2024 |
Exports:auc_bootauc_cidata_cumid_adjid_adj_nameid_adj_posresponse_simuroc_aucroc_lineroc_plotzroc_plot
Dependencies: