SCOUTer: Simulate Controlled Outliers

Using principal component analysis as a base model, 'SCOUTer' offers a new approach to simulate outliers in a simple and precise way. The user can generate new observations defining them by a pair of well-known statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) statistics. Just by introducing the target values of the SPE and T^2, 'SCOUTer' returns a new set of observations with the desired target properties. Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and Alberto Ferrer (2020).

Version: 1.0.0
Depends: R (≥ 3.5.0), ggplot2, ggpubr, stats
Suggests: knitr, rmarkdown
Published: 2020-06-30
Author: Alba Gonzalez Cebrian [aut, cre], Abel Folch-Fortuny [aut], Francisco Arteaga [aut], Alberto Ferrer [aut]
Maintainer: Alba Gonzalez Cebrian <algonceb at upv.es>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: SCOUTer results

Documentation:

Reference manual: SCOUTer.pdf
Vignettes: SCOUTer demo

Downloads:

Package source: SCOUTer_1.0.0.tar.gz
Windows binaries: r-devel: SCOUTer_1.0.0.zip, r-release: SCOUTer_1.0.0.zip, r-oldrel: SCOUTer_1.0.0.zip
macOS binaries: r-release (arm64): SCOUTer_1.0.0.tgz, r-release (x86_64): SCOUTer_1.0.0.tgz, r-oldrel: SCOUTer_1.0.0.tgz

Linking:

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