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deadwood

0.9.2Package record

Outlier Detection via Pruning Mutual Reachability Minimum Spanning Trees

2packages dependsuggested by 1
10.6Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

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

CRAN checks
13OK
Slowest check: 2.6 min · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-04-22
  • ERROR13 OK, 1 ERROR2026-04-18
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 0.9.2
README· In the repository, left out of the releaseVignettesNoNEWSYes · 100% structuredCitation fileNoWebsiteYes
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

10.6K
CRAN downloads in the past year
Rank #2,615 · ~29/day · ~887/mo
Daily download trend is not available in this view yet.
2.3K30 days
6K90 days
10.6K1 year
Compare downloads with other packages →
Also on2K r2u5 autocran

Repository

1Stars
0Forks
1Open issues
0Open PRs
3Releases
73Commits
1Commit identities
anomaly-detectiondata-sciencemachine-learningmachine-learning-algorithmsminimum-spanning-treeminimum-spanning-treesmstnoise-detection
73 commits · Last activity 2026-10-14 · 0% stars, 30d · Default branch at 0.9.2.9001, ahead of CRAN 0.9.2 (checked 2026-10-25)
Responsiveness
not measured

Stars over time

2026-02-15 · 12026-07-07 · 1
PracticesBeta

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.

Continuous integration (2)
GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux

Governance and community (1)
Code of conduct
Docs source (1)

GitHub Pages site last deployed 2026-10-14

Reproducibility and dev environment (1)
Makefile
How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
3 external dependencies (excludes base and recommended)

Installing deadwood adds 2 packages (2 with compiled code, 1.8 MB of source tarballs). Heaviest: Rcpp 1.8 MB, quitefastmst 42 KB.

Depends (0)
none
Imports (2)
LinkingTo (1)
Suggests (1)
datasets
Enhances (0)
none
Reverse dependencies
2direct
7indirect
Imports 2
Suggested by 1
Depends, Imports or LinkingTo, most downloaded first
Suggests, most downloaded first

People & History

People (1)
Author, Maintainer since 0.9.0-2 (2026), Copyright holder
Releases (4)

Pick two releases to compare their code metrics.

Package metadata

First published
2026-02-20
Total releases
4 / 1 yrs
License
AGPL-3 OSI
Download size
32 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("deadwood")
Gagolewski, M. (2026). deadwood: Outlier Detection via Pruning Mutual Reachability Minimum Spanning Trees (Version 0.9.2) [Computer software]. https://doi.org/10.32614/CRAN.package.deadwood

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for deadwood version 0.9.2 [Data set]. HJJB, LLC. Data release v2026-10-26. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-26, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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