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.
cABCanalysis
R and python implementation of the computed ABC Analysis for rational selection of the most informative variables. Features calculation using monotonic splines, edge-case handling (see details here), base R plots as well as GGPLots.
Install R package
The package can be installed from CRAN:
install.packages("cABCanalysis")
from this github repository using the remotes package:
remotes::install_github("AndreHDev/cABC_Analysis")
or manually by cloning this repository and running
devtools::install("path/to/cABC_Analysis")
Reference
If you use this tool in your research, please cite the original publication:
Ultsch A, Lötsch J (2015) "Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data". PLoS ONE 10(6): e0129767.