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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "cABCanalysis" in publications use:'
type: software
license: GPL-3.0-only
title: 'cABCanalysis: Computed ABC Analysis'
version: '1.0'
doi: 10.32614/CRAN.package.cABCanalysis
abstract: '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. <https://doi.org/10.1371/journal.pone.0129767>.'
authors:
- family-names: Lotsch
  given-names: Jorn
  email: j.lotsch@em.uni-frankfurt.de
  orcid: https://orcid.org/0000-0002-5818-6958
- family-names: Himmelspach
  given-names: André
  email: himmelspach@med.uni-frankfurt.de
  orcid: https://orcid.org/0009-0009-9857-227X
repository: https://andrehdev.r-universe.dev
repository-code: https://github.com/AndreHDev/cABC_Analysis
commit: ac00c13047549c7829382594d68f9659611c45d0
url: https://github.com/AndreHDev/cABC_Analysis
date-released: '2026-04-20'
contact:
- family-names: Himmelspach
  given-names: André
  email: himmelspach@med.uni-frankfurt.de
  orcid: https://orcid.org/0009-0009-9857-227X