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cABCanalysis  

Computed ABC Analysis
View on CRAN: Click here


Download and install cABCanalysis package within the R console
Install from CRAN:
install.packages("cABCanalysis")

Install from Github:
library("remotes")
install_github("cran/cABCanalysis")

Install by package version:
library("remotes")
install_version("cABCanalysis", "1.0")



Attach the package and use:
library("cABCanalysis")
Maintained by
André Himmelspach
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-28
Latest Update: 2026-04-28
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>.
How to cite:
André Himmelspach (2026). cABCanalysis: Computed ABC Analysis. R package version 1.0, https://cran.r-project.org/web/packages/cABCanalysis. Accessed 28 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:23), 1.0 (2026-04-28 21:00)
Other packages that cited cABCanalysis R package
View cABCanalysis citation profile
Other R packages that cABCanalysis depends, imports, suggests or enhances
Complete documentation for cABCanalysis
Functions, R codes and Examples using the cABCanalysis R package
Full cABCanalysis package functions and examples
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