stranger package

stranger is a framework for unsupervised anomalies detection that simplifies the user experience because the one does not need to be concerned with the many packages and functions that are required. It acts as a wrapper around existing packages (“a la Caret”) and provides in a clean and uniform toolkit for evaluation explaination reporting routines. Hence the name stranger taht stands for “Simple Toolkit in R for Anomalies Get Explain and Report”.

stranger provides wrapper around several packages that contain anomaly detection routines. One approach is called a weird. Currently implemented methods (weirds) can be obtained by using weird_list function. Underlying methods deal with: Angle-based Outlier Factor, autoencode, isolation Forest, kmeans (), k-Nearest Neighbour, Local Outlier Factor, Mahalanobis distance, Semi-robust principal components > distances, randomforest outlier metric.

Obviously, to be able to exploit stranger, user will need to have various packages installed – those ones containing computational routines.

stranger basics

Using stranger, user has at disposal an analysis workflow.

Analysis workflow

Main functions associated with proposed analysis workflow deal with:

In addition, those steps lead to objects having a specific S3 class and some visualization is possible thanks to dedicated plot methods.

Guide to use stranger package

We did write some vignette to accompany you in the discovery of anomalies using stranger. We recommend to read vignettes in the following order:


stranger is not currently available on CRAN. Install it from github:

# install.packages(devtools)