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RelativeDistClust

0.1.0Package record

Clustering with a Novel Non Euclidean Relative Distance

0packages depend
4.9Kdownloads / year
79.7%test coverage
13/13flavors OK

Overview

About

Using the novel Relative Distance to cluster datasets. Implementation of a clustering approach based on the k-means algorithm that can be used with any distance. In addition, implementation of the Hartigan and Wong method to accommodate alternative distance metrics. Both methods can operate with any distance measure, provided a suitable method is available to compute cluster centers under the chosen metric. Additionally, the k-medoids algorithm is implemented, offering a robust alternative for clustering without the need of computing cluster centers under the chosen metric. All three methods are designed to support Relative distances, Euclidean distances, and any user-defined distance functions. The Hartigan and Wong method is described in Hartigan and Wong (1979) doi:10.2307/2346830 and an explanation of the k-medoids algorithm can be found in Reynolds et al (2006) doi:10.1007/s10852-005-9022-1.

Install

Health

CRAN checks
13OK
Slowest check: 1.5 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-06-09
  • ERROR12 OK, 1 ERROR2026-06-08
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 0.1.0
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
33
Examples that run
100%33 run in a plain check
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
24%

Downloads

4.9K
CRAN downloads in the past year
Rank #14,607 · ~13/day · ~408/mo
Daily download trend is not available in this view yet.
27430 days
81490 days
4.9K1 year
Compare downloads with other packages →
Also on54 r2u2 autocran

Dependencies

Declared dependencies
9 external dependencies (excludes base and recommended)

Installing RelativeDistClust adds 118 packages (59 with compiled code, 113 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, bslib 5.6 MB.

Depends (0)
none
LinkingTo (0)
none
Suggests (5)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (1)
Author, Maintainer since 0.1.0 (2025)
Listed in earlier versions (1)
Irene
no longer listed · 0.1.0
Releases (1)
  • R
    R 4.6.0 released · 2026-04-24
  • 0.1.0Latest
    2025-09-22 · current release
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2025-09-22
Total releases
1 / 1 yrs
License
GPL-3 OSI
Download size
33 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("RelativeDistClust")
Creus Martí, I. (2025). RelativeDistClust: Clustering with a Novel Non Euclidean Relative Distance (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.RelativeDistClust

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 RelativeDistClust version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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