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) . 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.