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The leader clustering algorithm provides a means for clustering a set of data points. Unlike many other clustering algorithms it does not require the user to specify the number of clusters, but instead requires the approximate radius of a cluster as its primary tuning parameter. The package provides a fast implementation of this algorithm in n-dimensions using Lp-distances (with special cases for p=1,2, and infinity) as well as for spatial data using the Haversine formula, which takes latitude/longitude pairs as inputs and clusters based on great circle distances.
| Version: | 1.5 |
| Published: | 2023-03-24 |
| DOI: | 10.32614/CRAN.package.leaderCluster |
| Author: | Taylor B. Arnold |
| Maintainer: | Taylor B. Arnold <tarnold2 at richmond.edu> |
| License: | LGPL-2 |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | leaderCluster results |
| Reference manual: | leaderCluster.pdf |
| Package source: | leaderCluster_1.5.tar.gz |
| Windows binaries: | r-devel: leaderCluster_1.5.zip, r-release: leaderCluster_1.5.zip, r-oldrel: leaderCluster_1.5.zip |
| macOS binaries: | r-release (arm64): leaderCluster_1.5.tgz, r-oldrel (arm64): leaderCluster_1.5.tgz, r-release (x86_64): leaderCluster_1.5.tgz, r-oldrel (x86_64): leaderCluster_1.5.tgz |
| Old sources: | leaderCluster archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.