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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 |
| Reference manual: | SCOUTer.pdf |
| Vignettes: |
SCOUTer demo |
| 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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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