Outlier Detection via Pruning Mutual Reachability Minimum Spanning Trees
Overview
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'.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 4 (1 internal)includes a package overview page
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 67%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 75%
Downloads
Repository
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5 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/gagolews/deadwood on 2026-10-25.
The R CMD check workflow runs on Linux
GitHub Pages site last deployed 2026-10-14
Dependencies
Installing deadwood adds 2 packages (2 with compiled code, 1.8 MB of source tarballs). Heaviest: Rcpp 1.8 MB, quitefastmst 42 KB.
Code & Tests
People & History
Package metadata
- First published
- 2026-02-20
- Total releases
- 4 / 1 yrs
- License
- AGPL-3 OSI
- Download size
- 32 KB
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