The emstreeR package allows R users to fast and easily compute an Euclidean Minimum Spanning Tree from data.
This package provides an R interface to the Dual-Tree Boruvka algorithm (March, Ram, Gray, 2010) from 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2005). The Dual-Tree Boruvka is theoretically and emiprically the fastest algorithm for computing an Euclidean Minimum Spanning Tree (EMST).
ComputeMST is the main function of this package and
its name speaks for itself. It is a fast wrapper to its
C++ homonym from 'mlpack' for computing an Euclidean Minimum
Spanning Tree. Compared to functions in other MST related R packages,
ComputeMST is easier to use because it receives the
data as a numeric matrix or a data.frame, which are the most
common data input formats in the wild. You won't need to put it into a graph
format as you otherwise would in other packages.
This package also offers wrappers functions and methods for plotting the
resulting MST from ComputeMST.
plot.MST is an S3 method to the generic function
plot that produces 2D scatter plots with segments between the points
in a 'base' R style, following the linkage order in the MST.
plotMST3D produces a 3D point cloud with segments
between the points following the linkage order in the MST and using the
'scatterplot3d' package style for plotting.
stat_MST is a 'ggplot2' Stat extension that
produces 2D scatter plots with segments based on the linkage order in the MST
using the 'ggplot2' style.
Curtin, R. R. et al. (2005). Mlpack: A scalable C++ machine learning library. Journal of Machine Learning Research, v. 14, 2013.
March, W. B., and Ram, P., and Gray, A. G. (2010). Fast euclidian minimum spanning tree: algorithm analysis, and applications. 16th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, July 25-28 2010. Washington, DC, USA.
mlpack: http://www.mlpack.org/
ggplot2: https://ggplot2.tidyverse.org