influenceAUC: Identify Influential Observations in Binary Classification

Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.

Version: 0.1.2
Imports: dplyr, geigen, ggplot2, ggrepel, methods, ROCR
Published: 2020-05-30
DOI: 10.32614/CRAN.package.influenceAUC
Author: Bo-Shiang Ke [cre, aut, cph], Yuan-chin Ivan Chang [aut], Wen-Ting Wang [aut]
Maintainer: Bo-Shiang Ke <naivete0907 at>
License: GPL-3
NeedsCompilation: no
CRAN checks: influenceAUC results


Reference manual: influenceAUC.pdf


Package source: influenceAUC_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): influenceAUC_0.1.2.tgz, r-oldrel (arm64): influenceAUC_0.1.2.tgz, r-release (x86_64): influenceAUC_0.1.2.tgz, r-oldrel (x86_64): influenceAUC_0.1.2.tgz
Old sources: influenceAUC archive


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