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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>.

Version: 1.0.0
Depends: R (≥ 2.10)
Imports: survey, svyVarSel
Published: 2024-10-25
DOI: 10.32614/CRAN.package.svyROC
Author: Amaia Iparragirre ORCID iD [aut, cre, cph], Irantzu Barrio [aut], Inmaculada Arostegui [aut]
Maintainer: Amaia Iparragirre <amaia.iparragirre at ehu.eus>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: svyROC citation info
Materials: README NEWS
CRAN checks: svyROC results

Documentation:

Reference manual: svyROC.pdf

Downloads:

Package source: svyROC_1.0.0.tar.gz
Windows binaries: r-devel: svyROC_1.0.0.zip, r-release: svyROC_1.0.0.zip, r-oldrel: svyROC_1.0.0.zip
macOS binaries: r-release (arm64): svyROC_1.0.0.tgz, r-oldrel (arm64): svyROC_1.0.0.tgz, r-release (x86_64): svyROC_1.0.0.tgz, r-oldrel (x86_64): svyROC_1.0.0.tgz

Linking:

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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.