Computed ABC Analysis
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- WARNING12 OK, 1 WARNING2026-06-08
- OK5 OK2026-04-29
Documentation
- Help pages
- 10 (7 internal)
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 10%
Downloads
Repository
Stars over time
1 development-tooling and community-health practice detected across 1 family in the upstream repository
Checks run against github.com/andrehdev/cabc_analysis on 2026-10-11.
Dependencies
Installing cABCanalysis adds 18 packages (7 with compiled code, 14 MB of source tarballs). Heaviest: ggplot2 6.0 MB, isoband 1.5 MB, farver 1.2 MB.
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2026-04-28
- Total releases
- 3 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10.0
- Bundled data
- 6.3 KB / 1 file
- Download size
- 30 KB
Cite
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