outForest

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.

Last 30 days

This package has been downloaded 206 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 33 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,817 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 38 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.