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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.2 |
rolling linux/jammy R-4.5 | influenceAUC_0.1.2.tar.gz |
61.8 KiB |
0.1.2 |
rolling linux/noble R-4.5 | influenceAUC_0.1.2.tar.gz |
62.1 KiB |
0.1.2 |
rolling source/ R- | influenceAUC_0.1.2.tar.gz |
11.0 KiB |
0.1.2 |
latest source/ R- | influenceAUC_0.1.2.tar.gz |
11.0 KiB |
0.1.2 |
latest linux/jammy R-4.5 | influenceAUC_0.1.2.tar.gz |
61.8 KiB |
0.1.2 |
latest linux/noble R-4.5 | influenceAUC_0.1.2.tar.gz |
62.1 KiB |
0.1.2 |
2026-04-26 source/ R- | influenceAUC_0.1.2.tar.gz |
11.0 KiB |
0.1.2 |
2026-04-23 source/ R- | influenceAUC_0.1.2.tar.gz |
11.0 KiB |
0.1.2 |
2026-04-09 windows/windows R-4.5 | influenceAUC_0.1.2.zip |
64.6 KiB |
0.1.2 |
2025-04-20 source/ R- | influenceAUC_0.1.2.tar.gz |
11.0 KiB |