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>.

Version:	1.0
Depends:	R (≥ 2.10.0)
Imports:	ggplot2, plotrix, grDevices, graphics, stats, utils
Suggests:	datasets, testthat (≥ 3.0.0)
Published:	2026-04-28
DOI:	10.32614/CRAN.package.cABCanalysis (may not be active yet)
Author:	Jorn Lotsch [aut], André Himmelspach [aut, cre]
Maintainer:	André Himmelspach <himmelspach at med.uni-frankfurt.de>
License:	GPL-3
URL:	https://github.com/AndreHDev/cABC_Analysis
NeedsCompilation:	no
Materials:	README
CRAN checks:	cABCanalysis results

Documentation:

Reference manual:

cABCanalysis.html , cABCanalysis.pdf

Downloads:

Package source:	cABCanalysis_1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: cABCanalysis_1.0.zip
macOS binaries:	r-release (arm64): cABCanalysis_1.0.tgz, r-oldrel (arm64): cABCanalysis_1.0.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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