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-any)influenceAUC_0.1.3.zip(r-4.6-any)influenceAUC_0.1.3.zip(r-4.5-any)
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✨
DESCRIPTION
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 324 downloads 4 exports 34 dependencies

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

TargetResultTimeFilesSyslog
linux-develNOTE118
source / vignettesOK144
linux-releaseNOTE153
macos-releaseNOTE185
macos-oldrelNOTE180
windows-develNOTE81
windows-releaseNOTE82
windows-oldrelNOTE59
wasm-releaseOK97

Exports:IAUCICLCLAUCpinpoint

Dependencies:bitopscaToolsclicpp11dplyrfarvergeigengenericsggplot2ggrepelgluegplotsgtablegtoolsisobandKernSmoothlabelinglifecyclemagrittrpillarpkgconfigR6RColorBrewerRcpprlangROCRS7scalestibbletidyselectutf8vctrsviridisLitewithr