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).


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install.packages("SCOUTer")

1.0.0 by Alba Gonzalez Cebrian, 15 days ago


Browse source code at https://github.com/cran/SCOUTer


Authors: Alba Gonzalez Cebrian [aut, cre] , Abel Folch-Fortuny [aut] , Francisco Arteaga [aut] , Alberto Ferrer [aut]


Documentation:   PDF Manual  


GPL-3 license


Depends on ggplot2, ggpubr, stats

Suggests knitr, rmarkdown


See at CRAN