Computed ABC Analysis R package cABCanalysis version 1.0 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>. Software Depends: R (>= 2.10.0) Imports: ggplot2, plotrix, grDevices, graphics, stats, utils Suggests: datasets, testthat (>= 3.0.0) André Himmelspach <himmelspach@med.uni-frankfurt.de> Comprehensive R Archive Network (CRAN) Jorn Lotsch [aut] (ORCID: <https://orcid.org/0000-0002-5818-6958>), André Himmelspach [aut, cre] (ORCID: <https://orcid.org/0009-0009-9857-227X>) GPL-3 2026-04-28 application/tgz https://CRAN.R-project.org/package=cABCanalysis doi:10.32614/CRAN.package.cABCanalysis