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
DOI:	10.32614/CRAN.package.SCOUTer
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-oldrel (arm64): SCOUTer_1.0.0.tgz, r-release (x86_64): SCOUTer_1.0.0.tgz, r-oldrel (x86_64): SCOUTer_1.0.0.tgz

Linking:

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