Working paper

REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit

Daniel Fischer, Alain Berro, Klaus Nordhausen, and Anne Ruiz-Gazen

Abstract

The R-package REPPlab is designed to explore multivariate data sets using one-dimensional unsupervised projection pursuit. It is useful as a preprocessing step to find clusters or as an outlier detection tool for multivariate data. Except from the packages tourr and rggobi, there is no implementation of exploratory projection pursuit tools available in R. REPPlab is an R interface for the Java program EPP-lab that implements four projection indices and three biologically inspired optimization algorithms. It also proposes new tools for plotting and combining the results and specific tools for outlier detection. The functionality of the package is illustrated through some simulations and using some real data.

Keywords

genetic algorithms; Java, kurtosis, particle swarm optimization; projection index; Tribes; projection matrix; unsupervised data analysis;

Replaced by

Daniel Fischer, Alain Berro, Klaus Nordhausen, and Anne Ruiz-Gazen, REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit, Communications in Statistics - Simulation and Computation, 2019, forthcoming.

Reference

Daniel Fischer, Alain Berro, Klaus Nordhausen, and Anne Ruiz-Gazen, REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit, TSE Working Paper, n. 19-1001, March 2019.

See also

Published in

TSE Working Paper, n. 19-1001, March 2019