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influenceAUC  

Identify Influential Observations in Binary Classification
View on CRAN: Click here


Download and install influenceAUC package within the R console
Install from CRAN:
install.packages("influenceAUC")

Install from Github:
library("remotes")
install_github("cran/influenceAUC")

Install by package version:
library("remotes")
install_version("influenceAUC", "0.1.2")



Attach the package and use:
library("influenceAUC")
Maintained by
Bo-Shiang Ke
[Scholar Profile | Author Map]
First Published: 2020-01-31
Latest Update: 2020-05-30
Description:
Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) 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.
How to cite:
Bo-Shiang Ke (2020). influenceAUC: Identify Influential Observations in Binary Classification. R package version 0.1.2, https://cran.r-project.org/web/packages/influenceAUC. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.1.0 (2020-01-31 15:40), 0.1.1 (2020-02-19 06:40)
Other packages that cited influenceAUC R package
View influenceAUC citation profile
Other R packages that influenceAUC depends, imports, suggests or enhances
Complete documentation for influenceAUC
Functions, R codes and Examples using the influenceAUC R package
Some associated functions: IAUC . ICLC . LAUC . build_AUC_plot . create_lift_chart . fetch_output_indeces . pinpoint . plot.IAUC . plot.ICLC . plot.LAUC . plot_sequentially . print.IAUC . print.LAUC . print_output . 
Some associated R codes: global.R . influenceAUC.R . reporter.R . utils.R .  Full influenceAUC package functions and examples
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