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  "Title": "Outlier Detection via Pruning Mutual Reachability Minimum\nSpanning Trees",
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  "Description": "Implements an anomaly detection algorithm based on a\ndataset's mutual reachability minimum spanning tree: 'deadwood'\nprunes protruding tree segments and marks small debris as\noutliers; see Gagolewski (2026)\n<https://deadwood.gagolewski.com/>. More precisely, tree edges\nwith weights greater than the detected elbow point are removed.\nAll the resulting connected components whose sizes do not\nexceed a prespecified threshold are deemed anomalous.  The use\nof a mutual reachability distance pulls peripheral observations\nfarther away from one another.  If the dataset is comprised of\nwell-separated clusters of heterogeneous densities, an attempt\nto split the dataset and refine the outlierness markers will be\nmade. The 'Python' version of 'deadwood' is available via\n'PyPI'.",
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