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-4.5)influenceAUC_0.1.3.zip(r-4.4)influenceAUC_0.1.3.zip(r-4.3)
influenceAUC_0.1.3.tgz(r-4.4-any)influenceAUC_0.1.3.tgz(r-4.3-any)
influenceAUC_0.1.3.tar.gz(r-4.5-noble)influenceAUC_0.1.3.tar.gz(r-4.4-noble)
influenceAUC_0.1.3.tgz(r-4.4-emscripten)influenceAUC_0.1.3.tgz(r-4.3-emscripten)
influenceAUC.pdf |influenceAUC.html✨
influenceAUC/json (API)

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

Peer review:

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

On CRAN:

3.70 score 127 downloads 4 exports 40 dependencies

Last updated 20 days agofrom:0cb5e60121. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-winNOTENov 03 2024
R-4.5-linuxNOTENov 03 2024
R-4.4-winNOTENov 03 2024
R-4.4-macNOTENov 03 2024
R-4.3-winNOTENov 03 2024
R-4.3-macNOTENov 03 2024

Exports:IAUCICLCLAUCpinpoint

Dependencies:bitopscaToolsclicolorspacedplyrfansifarvergeigengenericsggplot2ggrepelgluegplotsgtablegtoolsisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcpprlangROCRscalestibbletidyselectutf8vctrsviridisLitewithr