Package: auRoc 0.2-1

auRoc: Various Methods to Estimate the AUC

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.

Authors:Dai Feng [aut, cre], Damjan Manevski [auc], Maja Pohar Perme [auc]

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auRoc.pdf |auRoc.html✨
auRoc/json (API)

# Install 'auRoc' in R:
install.packages('auRoc', repos = c('https://daifengstat.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 4 scripts 244 downloads 1 mentions 4 exports 36 dependencies

Last updated 5 years agofrom:a631eed9e2. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-winOKNov 20 2024
R-4.5-linuxOKNov 20 2024
R-4.4-winOKNov 20 2024
R-4.4-macOKNov 20 2024
R-4.3-winOKNov 20 2024
R-4.3-macOKNov 20 2024

Exports:auc.nonpara.kernelauc.nonpara.mwauc.para.bayesauc.para.frequentist

Dependencies:abindarmBHbootclicodadigestgluelatticelavaanlifecyclelme4MASSMatrixMBESSmiminqamnormtmvtnormnlmenloptrnumDerivOpenMxpbivnormProbYXquadprogRcppRcppEigenRcppParallelrjagsrlangrootSolverpfsemsemToolsStanHeaders