outForest
Multivariate Outlier Detection and Replacement
Provides a random forest based implementation of the method described in Chapter 7.1.2 (Regression model based anomaly detection) of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as follows: Each numeric variable is regressed onto all other variables by a random forest. If the scaled absolute difference between observed value and out-of-bag prediction of the corresponding random forest is suspiciously large, then a value is considered an outlier. The package offers different options to replace such outliers, e.g. by realistic values found via predictive mean matching. Once the method is trained on a reference data, it can be applied to new data.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.1 |
rolling linux/jammy R-4.5 | outForest_1.0.1.tar.gz |
119.9 KiB |
1.0.1 |
rolling linux/noble R-4.5 | outForest_1.0.1.tar.gz |
119.8 KiB |
1.0.1 |
rolling source/ R- | outForest_1.0.1.tar.gz |
87.5 KiB |
1.0.1 |
latest linux/jammy R-4.5 | outForest_1.0.1.tar.gz |
119.9 KiB |
1.0.1 |
latest linux/noble R-4.5 | outForest_1.0.1.tar.gz |
119.8 KiB |
1.0.1 |
latest source/ R- | outForest_1.0.1.tar.gz |
87.5 KiB |
1.0.1 |
2026-04-26 source/ R- | outForest_1.0.1.tar.gz |
87.5 KiB |
1.0.1 |
2026-04-23 source/ R- | outForest_1.0.1.tar.gz |
87.5 KiB |
1.0.1 |
2026-04-09 windows/windows R-4.5 | outForest_1.0.1.zip |
121.9 KiB |
1.0.1 |
2025-04-20 source/ R- | outForest_1.0.1.tar.gz |
87.5 KiB |