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).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | SCOUTer_1.0.0.tar.gz |
153.7 KiB |
1.0.0 |
rolling linux/noble R-4.5 | SCOUTer_1.0.0.tar.gz |
153.5 KiB |
1.0.0 |
rolling source/ R- | SCOUTer_1.0.0.tar.gz |
71.6 KiB |
1.0.0 |
latest linux/jammy R-4.5 | SCOUTer_1.0.0.tar.gz |
153.7 KiB |
1.0.0 |
latest linux/noble R-4.5 | SCOUTer_1.0.0.tar.gz |
153.5 KiB |
1.0.0 |
latest source/ R- | SCOUTer_1.0.0.tar.gz |
71.6 KiB |
1.0.0 |
2026-04-26 source/ R- | SCOUTer_1.0.0.tar.gz |
71.6 KiB |
1.0.0 |
2026-04-23 source/ R- | SCOUTer_1.0.0.tar.gz |
71.6 KiB |
1.0.0 |
2026-04-09 windows/windows R-4.5 | SCOUTer_1.0.0.zip |
154.5 KiB |
1.0.0 |
2025-04-20 source/ R- | SCOUTer_1.0.0.tar.gz |
71.6 KiB |