emstreeR: Tools for Fast Computing and Plotting Euclidean Minimum Spanning Trees

Fast and easily computes an Euclidean Minimum Spanning Tree (EMST) from data, relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013). 'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>), which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides functions and an S3 method for readily plotting Minimum Spanning Trees (MST) using either the style of the 'base', 'scatterplot3d', or 'ggplot2' libraries.

Version: 3.0.0
Depends: R (≥ 3.5.0)
Imports: mlpack, scatterplot3d, ggplot2, BBmisc, graphics, stats
Published: 2022-03-21
Author: Allan Quadros [aut, cre], Duncan Garmonsway [ctb]
Maintainer: Allan Quadros <allanvcq at gmail.com>
BugReports: https://github.com/allanvc/emstreeR/issues/
License: BSD_3_clause + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: emstreeR results


Reference manual: emstreeR.pdf


Package source: emstreeR_3.0.0.tar.gz
Windows binaries: r-devel: emstreeR_3.0.0.zip, r-release: emstreeR_3.0.0.zip, r-oldrel: emstreeR_3.0.0.zip
macOS binaries: r-release (arm64): emstreeR_3.0.0.tgz, r-oldrel (arm64): emstreeR_3.0.0.tgz, r-release (x86_64): emstreeR_3.0.0.tgz, r-oldrel (x86_64): emstreeR_3.0.0.tgz
Old sources: emstreeR archive


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