Package: SCOUTer 1.0.0
Alba Gonzalez Cebrian
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).
Authors:
SCOUTer_1.0.0.tar.gz
SCOUTer_1.0.0.tar.gz(r-4.5-noble)SCOUTer_1.0.0.tar.gz(r-4.4-noble)
SCOUTer_1.0.0.tgz(r-4.4-emscripten)SCOUTer_1.0.0.tgz(r-4.3-emscripten)
SCOUTer.pdf |SCOUTer.html✨
SCOUTer/json (API)
| # Install 'SCOUTer' in R: |
| install.packages('SCOUTer', repos = 'https://cloud.r-project.org') |
- X - Demo dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:e19a07eb39. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 06 2025 |
| R-4.5-linux | OK | Mar 06 2025 |
| R-4.4-linux | OK | Mar 06 2025 |
Exports:barwithuclcustombardistplotdistplotsimpledotagdscplotht2infoobscontribpanelpcamb_classicpcamescoreplotscoreplotsimplescoutscoutgridscoutsimplescoutstepsspeinfoxshift
Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr
Citation
To cite package ‘SCOUTer’ in publications use:
Gonzalez Cebrian A, Folch-Fortuny A, Arteaga F, Ferrer A (2020). SCOUTer: Simulate Controlled Outliers. R package version 1.0.0, https://CRAN.R-project.org/package=SCOUTer.
Corresponding BibTeX entry:
@Manual{,
title = {SCOUTer: Simulate Controlled Outliers},
author = {Alba {Gonzalez Cebrian} and Abel Folch-Fortuny and
Francisco Arteaga and Alberto Ferrer},
year = {2020},
note = {R package version 1.0.0},
url = {https://CRAN.R-project.org/package=SCOUTer},
}
Readme and manuals
SCOUTerRpack
SCOUTer package in R
Simulating anomalous data is an extremely common procedure. However, very little attention is paid to this step and it is usually defined ad hoc, existing a lack of standard. In this package, a new framework to simulate outliers directly controlling their outlying properties has been proposed. This framework offers the possibility of generating data sets with all type of desired properties, given that the user can control the pair of statistics that essentially define outliers: the Squared Prediction Error ( SPE ) and the Hotelling-T2 ( T2 ). These metrics evaluate in a complementary way how far is an observation from the majority of a data set. Since Given an observation with initial values for the statistics, a PCA model and target values for the statistics, our simulation method drifts the observation in a direction that shifts the initial SPE and the T2 until reaching their target values.
Help Manual
| Help page | Topics |
|---|---|
| barwithucl | barwithucl |
| custombar | custombar |
| distplot | distplot |
| displotsimple | distplotsimple |
| dotag | dotag |
| dscplot | dscplot |
| ht2info | ht2info |
| obscontribpanel | obscontribpanel |
| pcamb_classic | pcamb_classic |
| pcame | pcame |
| scoreplot | scoreplot |
| scoreplotsimple | scoreplotsimple |
| scout | scout |
| scoutgrid | scoutgrid |
| scoutsimple | scoutsimple |
| scoutsteps | scoutsteps |
| speinfo | speinfo |
| Demo dataset | X |
| xshift | xshift |