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: 0.1.1
Imports: dplyr, geigen, ggplot2, ggrepel, methods, ROCR
Published: 2020-02-19
Author: Bo-Shiang Ke [cre, aut, cph], Yuan-chin Ivan Chang [aut], Wen-Ting Wang [aut]
Maintainer: Bo-Shiang Ke <naivete0907 at gmail.com>
BugReports: https://github.com/BoShiangKe/InfluenceAUC/issues
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: influenceAUC results

Downloads:

Reference manual: influenceAUC.pdf
Package source: influenceAUC_0.1.1.tar.gz
Windows binaries: r-devel: influenceAUC_0.1.1.zip, r-devel-gcc8: influenceAUC_0.1.1.zip, r-release: influenceAUC_0.1.1.zip, r-oldrel: influenceAUC_0.1.1.zip
OS X binaries: r-release: influenceAUC_0.1.1.tgz, r-oldrel: influenceAUC_0.1.1.tgz
Old sources: influenceAUC archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=influenceAUC to link to this page.