Package: cABCanalysis Type: Package Title: Computed ABC Analysis Version: 1.0.2 Description: 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. . Depends: R (>= 2.10.0) Imports: ggplot2, plotrix, grDevices, graphics, stats, utils LazyData: true Suggests: datasets, testthat (>= 3.0.0) License: GPL-3 URL: https://github.com/AndreHDev/cABC_Analysis Encoding: UTF-8 Authors@R: c(person(given = "Jorn", family = "Lotsch", role = "aut", email = "j.lotsch@em.uni-frankfurt.de", comment = c(ORCID = "0000-0002-5818-6958")), person(given = "André", family = "Himmelspach", role = c("aut","cre"), email = "himmelspach@med.uni-frankfurt.de", comment = c(ORCID = "0009-0009-9857-227X"))) Date: 2026-08-27 RoxygenNote: 7.3.3 NeedsCompilation: no Config/testthat/edition: 3 Repository: https://andrehdev.r-universe.dev Date/Publication: 2026-09-11 10:52:14 UTC RemoteUrl: https://github.com/andrehdev/cabc_analysis RemoteRef: HEAD RemoteSha: 7d8bcc95254918190958215305077550ee1eaf37 Packaged: 2026-10-11 10:29:23 UTC; root Author: Jorn Lotsch [aut] (ORCID: ), André Himmelspach [aut, cre] (ORCID: ) Maintainer: André Himmelspach