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Estimate the AUC using a variety of methods as follows: (1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods. (2) frequentist parametric methods using the likelihood ratio test based on higher-order asymptotic results, the signed log-likelihood ratio test, the Wald test, or the approximate ”t” solution to the Behrens-Fisher problem. (3) Bayesian parametric MCMC methods.
| Version: | 0.2-1 |
| Depends: | R (≥ 3.0.2), rjags (≥ 3-11), ProbYX (≥ 1.1) |
| Imports: | coda (≥ 0.16-1), MBESS (≥ 3.3.3) |
| Published: | 2020-04-04 |
| DOI: | 10.32614/CRAN.package.auRoc |
| Author: | Dai Feng [aut, cre], Damjan Manevski [auc], Maja Pohar Perme [auc] |
| Maintainer: | Dai Feng <daifeng.stat at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | auRoc results |
| Reference manual: | auRoc.pdf |
| Package source: | auRoc_0.2-1.tar.gz |
| Windows binaries: | r-devel: auRoc_0.2-1.zip, r-release: auRoc_0.2-1.zip, r-oldrel: auRoc_0.2-1.zip |
| macOS binaries: | r-release (arm64): auRoc_0.2-1.tgz, r-oldrel (arm64): auRoc_0.2-1.tgz, r-release (x86_64): auRoc_0.2-1.tgz, r-oldrel (x86_64): auRoc_0.2-1.tgz |
| Old sources: | auRoc archive |
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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.