Package: deadwood 0.9.2

deadwood: Outlier Detection via Pruning Mutual Reachability Minimum Spanning Trees

Implements an anomaly detection algorithm based on a dataset's mutual reachability minimum spanning tree: 'deadwood' prunes protruding tree segments and marks small debris as outliers; see Gagolewski (2026) <https://deadwood.gagolewski.com/>. More precisely, tree edges with weights greater than the detected elbow point are removed. All the resulting connected components whose sizes do not exceed a prespecified threshold are deemed anomalous. The use of a mutual reachability distance pulls peripheral observations farther away from one another. If the dataset is comprised of well-separated clusters of heterogeneous densities, an attempt to split the dataset and refine the outlierness markers will be made. The 'Python' version of 'deadwood' is available via 'PyPI'.

Authors:Marek Gagolewski [aut, cre, cph]