Package: SCOUTer
Title: Simulate Controlled Outliers
Version: 1.0.0
Authors@R: 
    c(person(given = "Alba",
           family = "Gonzalez Cebrian",
           role = c("aut", "cre"),
           email = "algonceb@upv.es"),
           person(given = "Abel",
           family = "Folch-Fortuny",
           role = "aut",
           email = "Abel.Folch-Fortuny@dsm.com"),
           person(given = "Francisco",
           family = "Arteaga",
           role = c("aut"),
           email = "francisco.arteaga@ucv.es"),
           person(given = "Alberto",
           family = "Ferrer",
           role = "aut",
           email = "aferrer@eio.upv.es"))
Description: 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).
License: GPL-3
Encoding: UTF-8
LazyData: true
Maintainer: Alba Gonzalez Cebrian <algonceb@upv.es>
RoxygenNote: 7.1.1
Depends: R (>= 3.5.0), ggplot2, ggpubr, stats
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-06-29 21:47:06 UTC; AlbaGC
Author: Alba Gonzalez Cebrian [aut, cre],
  Abel Folch-Fortuny [aut],
  Francisco Arteaga [aut],
  Alberto Ferrer [aut]
Repository: CRAN
Date/Publication: 2020-06-30 09:30:03 UTC
Built: R 4.6.0; ; 2026-04-25 14:15:32 UTC; unix
