Package: svyROC 1.2.0

svyROC: Estimation of the ROC Curve and the AUC for Complex Survey Data

Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.635>; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) <doi:10.2436/20.8080.02.121>; A. Iparragirre, I. Barrio (2024) <doi:10.1007/978-3-031-65723-8_7>.

Authors:Amaia Iparragirre [aut, cre, cph], Irantzu Barrio [aut], Inmaculada Arostegui [aut]

svyROC_1.2.0.tar.gz
svyROC_1.2.0.zip(r-4.7-any)svyROC_1.2.0.zip(r-4.6-any)svyROC_1.2.0.zip(r-4.5-any)
svyROC_1.2.0.tgz(r-4.6-any)svyROC_1.2.0.tgz(r-4.5-any)
svyROC_1.2.0.tar.gz(r-4.7-any)svyROC_1.2.0.tar.gz(r-4.6-any)
svyROC_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
svyROC/json (API)

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

Bug tracker:https://github.com/aiparragirre/svyroc/issues

Datasets:

On CRAN:

Conda:

aucauc-optimism-correctioncomplex-survey-dataoptimal-cut-off-pointsroc-curvesampling-weights

3.48 score 2 stars 2 scripts 378 downloads 10 exports 17 dependencies

Last updated from:0cd00b70e4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK180
source / vignettesOK179
linux-releaseOK145
macos-releaseOK114
macos-oldrelOK107
windows-develOK83
windows-releaseOK96
windows-oldrelOK106
wasm-releaseOK135

Exports:ci.wauccorrected.waucht.indepht.pairedwaucwocpwrocwroc.plotwsewsp

Dependencies:codetoolsDBIforeachglmnetiteratorslatticeMatrixminqamitoolsnumDerivRcppRcppArmadilloRcppEigenshapesurveysurvivalsvyVarSel