Package: auRoc 0.2-1

Dai Feng

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]

auRoc_0.2-1.tar.gz
auRoc_0.2-1.zip(r-4.7-any)auRoc_0.2-1.zip(r-4.6-any)
auRoc_0.2-1.tar.gz(r-4.7-any)auRoc_0.2-1.tar.gz(r-4.6-any)
auRoc_0.2-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
auRoc/json (API)

# Install 'auRoc' in R:
install.packages('auRoc', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

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

jagscpp

1.00 score 4 scripts 369 downloads 1 mentions 4 exports 38 dependencies

Last updated from:a631eed9e2. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK124
source / vignettesOK157
linux-releaseOK136
windows-develOK97
windows-releaseOK87
wasm-releaseOK105

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

Dependencies:abindarmBHbootclicodadigestlatticelavaanlifecyclelme4MASSMatrixMBESSmiminqamnormtmvtnormnlmenloptrnumDerivOpenMxpbivnormProbYXquadprogrbibutilsRcppRcppEigenRcppParallelRdpackreformulasrjagsrlangrootSolverpfsemsemToolsStanHeaders