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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. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". <doi:10.13140/RG.2.2.20415.94885/1> , 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>.
Version: | 0.1.0 |
Imports: | stats, graphics |
Published: | 2021-01-13 |
DOI: | 10.32614/CRAN.package.fullROC |
Author: | Yueran Yang [aut, cre] |
Maintainer: | Yueran Yang <yuerany at unr.edu> |
BugReports: | https://github.com/yuerany/fullROC/issues |
License: | GPL (≥ 3) |
NeedsCompilation: | no |
Language: | en-US |
CRAN checks: | fullROC results |
Reference manual: | fullROC.pdf |
Package source: | fullROC_0.1.0.tar.gz |
Windows binaries: | r-devel: fullROC_0.1.0.zip, r-release: fullROC_0.1.0.zip, r-oldrel: fullROC_0.1.0.zip |
macOS binaries: | r-release (arm64): fullROC_0.1.0.tgz, r-oldrel (arm64): fullROC_0.1.0.tgz, r-release (x86_64): fullROC_0.1.0.tgz, r-oldrel (x86_64): fullROC_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.