##### https://github.com/cran/ABCanalysis
Tip revision: 81afe0e
ABCanalysis-package.Rd
``````\name{ABCanalysis-package}
\alias{ABCanalysis-package}
\alias{ABCanalyse}
\alias{dbt.ABC}
\alias{dbt.ABCanalyse}
\alias{dbt.ABCanalysis}
\docType{package}
\title{
Computed ABC analysis
}
\description{
Computed ABC Analysis allows the optimal calculation of three disjoint subsets A,B,C in data sets containing positive values:

subset A containing few most profitable values, i.e. largest data values ("the important few"),
subset B containing data, where the profit gain equals effort required to obtain this gain, and the
subset C of non-profitable values, i.e. the smallest data sets ("the trivial many").

This package calculates the three subsets A, B and C by means of an algorithm based on
statistically valid definitions of thresholds for the three sets A,B and C.
}
\note{
Check out our new Umatrix package for visualisation and clustering of high-dimensional data on our Webpage.
}

\author{
Michael Thrun, Jorn Lotsch, Alfred Ultsch

\url{http://www.uni-marburg.de/fb12/datenbionik}

\email{mthrun@mathematik.uni-marburg.de}
}
%~~ Optionally other standard keywords, one per ~~
%~~ line, from file KEYWORDS in the R ~~
%~~ documentation directory ~~
\keyword{ package }

\examples{
data("SwissInhabitants")
abc=ABCanalysis(SwissInhabitants,PlotIt=TRUE)
SetA=SwissInhabitants[abc\$Aind]
SetB=SwissInhabitants[abc\$Bind]
SetC=SwissInhabitants[abc\$Cind]
}
\references{
Ultsch. A ., Lotsch J.: Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data, PloS one, Vol. 10(6), pp. e0129767. doi 10.1371/journal.pone.0129767, 2015.
}``````