Package: cABCanalysis
Type: Package
Title: Computed ABC Analysis
Version: 1.0.1
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. <doi:10.1371/journal.pone.0129767>.
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: 2026-06-08
RoxygenNote: 7.3.3
NeedsCompilation: no
Config/testthat/edition: 3
Packaged: 2026-06-09 05:57:37 UTC; andre
Author: Jorn Lotsch [aut] (ORCID: <https://orcid.org/0000-0002-5818-6958>),
  André Himmelspach [aut, cre] (ORCID:
    <https://orcid.org/0009-0009-9857-227X>)
Maintainer: André Himmelspach <himmelspach@med.uni-frankfurt.de>
Repository: CRAN
Date/Publication: 2026-06-09 14:00:19 UTC
Built: R 4.6.0; ; 2026-06-09 14:46:51 UTC; unix
