Package: influenceAUC 0.1.3

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.

Authors:Bo-Shiang Ke [cre, aut, cph], Yuan-chin Ivan Chang [aut], Wen-Ting Wang [aut]

influenceAUC_0.1.3.tar.gz
influenceAUC_0.1.3.zip(r-4.7)influenceAUC_0.1.3.zip(r-4.6)influenceAUC_0.1.3.zip(r-4.5)
influenceAUC_0.1.3.tgz(r-4.6-any)influenceAUC_0.1.3.tgz(r-4.5-any)
influenceAUC_0.1.3.tar.gz(r-4.7-any)influenceAUC_0.1.3.tar.gz(r-4.6-any)
influenceAUC_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
influenceAUC/json (API)

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

Bug tracker:https://github.com/boshiangke/influenceauc/issues

Pkgdown/docs site:https://boshiangke.github.io

On CRAN:

Conda:

3.00 score 138 downloads 4 exports 34 dependencies

Last updated from:b78ace93dc. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE112
source / vignettesOK149
linux-release-x86_64NOTE131
macos-release-arm64NOTE148
macos-oldrel-arm64NOTE178
windows-develNOTE96
windows-releaseNOTE84
windows-oldrelNOTE80
wasm-releaseOK100

Exports:IAUCICLCLAUCpinpoint

Dependencies:bitopscaToolsclicpp11dplyrfarvergeigengenericsggplot2ggrepelgluegplotsgtablegtoolsisobandKernSmoothlabelinglifecyclemagrittrpillarpkgconfigR6RColorBrewerRcpprlangROCRS7scalestibbletidyselectutf8vctrsviridisLitewithr