Package: MRMCaov 0.3.0
MRMCaov: Multi-Reader Multi-Case Analysis of Variance
Estimation and comparison of the performances of diagnostic tests in multi-reader multi-case studies where true case statuses (or ground truths) are known and one or more readers provide test ratings for multiple cases. Reader performance metrics are provided for area under and expected utility of ROC curves, likelihood ratio of positive or negative tests, and sensitivity and specificity. ROC curves can be estimated empirically or with binormal or binormal likelihood-ratio models. Statistical comparisons of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and the unified framework of Hillis (2005) <doi:10.1002/sim.2024>. The ANOVA can be conducted with data from a full factorial, nested, or partially paired study design; with random or fixed readers or cases; and covariances estimated with the DeLong method, jackknifing, or an unbiased method. Smith and Hillis (2020) <doi:10.1117/12.2549075>.
Authors:
MRMCaov_0.3.0.tar.gz
MRMCaov_0.3.0.zip(r-4.5)MRMCaov_0.3.0.zip(r-4.4)MRMCaov_0.3.0.zip(r-4.3)
MRMCaov_0.3.0.tgz(r-4.4-any)MRMCaov_0.3.0.tgz(r-4.3-any)
MRMCaov_0.3.0.tar.gz(r-4.5-noble)MRMCaov_0.3.0.tar.gz(r-4.4-noble)
MRMCaov_0.3.0.tgz(r-4.4-emscripten)MRMCaov_0.3.0.tgz(r-4.3-emscripten)
MRMCaov.pdf |MRMCaov.html✨
MRMCaov/json (API)
NEWS
| # Install 'MRMCaov' in R: |
| install.packages('MRMCaov', repos = c('https://brian-j-smith.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/brian-j-smith/mrmcaov/issues
Last updated 2 years agofrom:d79e49bcef. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 08 2024 |
| R-4.5-win | OK | Nov 08 2024 |
| R-4.5-linux | OK | Nov 08 2024 |
| R-4.4-win | OK | Nov 08 2024 |
| R-4.4-mac | OK | Nov 08 2024 |
| R-4.3-win | OK | Nov 08 2024 |
| R-4.3-mac | OK | Nov 08 2024 |
Exports:binary_sensbinary_specbinormal_aucbinormal_eubinormal_sensbinormal_specbinormalLR_aucbinormalLR_eubinormalLR_sensbinormalLR_specDeLongempirical_aucempirical_euempirical_sensempirical_specjackknifemrmcOR_to_RMHparametersRMH_to_ORroc_curvessrmcstmctrapezoidal_auctrapezoidal_senstrapezoidal_specunbiased
Dependencies:clicolorspacecrayonfansifarverggplot2gluegtablehmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellmvtnormnlmepillarpkgconfigprettyunitsprogressR6RColorBrewerrlangscalestibbletrustutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| MRMCaov: Multi-Reader Multi-Case Analysis of Variance | MRMCaov-package MRMCaov |
| Covariance Methods | cov_methods DeLong jackknife unbiased |
| Multi-reader multi-case dataset | Franken |
| Multi-reader multi-case dataset | Kundel |
| Performance Metrics | binary_sens binary_spec binormalLR_auc binormalLR_eu binormalLR_sens binormalLR_spec binormal_auc binormal_eu binormal_sens binormal_spec empirical_auc empirical_eu empirical_sens empirical_spec metrics trapezoidal_auc trapezoidal_sens trapezoidal_spec |
| Multi-Reader Multi-Case ROC Analysis | mrmc |
| Convert Obuchowski-Rockette Parameters to Roe & Metz Parameters | OR_to_RMH OR_to_RMH.data.frame OR_to_RMH.default |
| ROC Plots | plot plot.empirical_curve plot.empirical_curves plot.mrmc plot.roc_curve plot.roc_curves plot.roc_points plot.stmc |
| Print ROC Objects | print.roc_curve print.roc_curves |
| Convert Roe & Metz Parameters to Obuchowski-Rockette Parameters | RMH_to_OR RMH_to_OR.data.frame RMH_to_OR.default |
| ROC Performance Curves | mean mean.binormal_curves mean.roc_curve mean.roc_curves parameters parameters.mrmc parameters.roc_curve parameters.roc_curves parameters.stmc points points.empirical_curve points.empirical_curves points.roc_curve points.roc_curves roc_curves roc_curves.default roc_curves.mrmc roc_curves.stmc |
| Single-Reader Multi-Case ROC Analysis | srmc |
| Single-Test (Single-Reader) Multi-Case ROC Analysis | stmc |
| Summary Estimates and Statistical Tests | summary summary.mrmc summary.stmc |
| Multi-reader multi-case dataset | VanDyke |
