cABCanalysis
Computed ABC Analysis
Identify the most relative data points by dividing a numeric data set into three classes A, B, and C, where class A items are the "import few", class C items are the "trivial many" with class B items being something in between, resembling the idea of the Pareto principle. This ABC classification is done using an ABC curve, which plots cumulative "Yield" against "Effort", similar to a Lorenz curve. Class borders are then precisely mathematically defined on that curve, aiding in interpretation. Based on: Ultsch A, Lotsch J (2015) "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data". PLoS ONE 10(6): e0129767. <doi:10.1371/journal.pone.0129767>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling linux/jammy R-4.5 | cABCanalysis_1.0.tar.gz |
67.4 KiB |
1.0 |
rolling linux/noble R-4.5 | cABCanalysis_1.0.tar.gz |
67.2 KiB |
1.0 |
rolling source/ R- | cABCanalysis_1.0.tar.gz |
25.3 KiB |
1.0 |
latest linux/jammy R-4.5 | cABCanalysis_1.0.tar.gz |
67.4 KiB |
1.0 |
latest linux/noble R-4.5 | cABCanalysis_1.0.tar.gz |
67.2 KiB |
1.0 |
latest source/ R- | cABCanalysis_1.0.tar.gz |
25.3 KiB |
1.0 |
2026-04-23 source/ R- | cABCanalysis_1.0.tar.gz |
0 B |