bigmds: Multidimensional Scaling for Big Data

We present a set of algorithms for Multidimensional Scaling (MDS) to be used with large datasets. MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of three algorithms: Divide and Conquer MDS, Fast MDS and MDS based on Gower interpolation. The main idea of these methods is based on partitioning the dataset into small pieces, where classical methods can work.

Version: 0.0.1
Imports: MCMCpack, stats, pdist
Suggests: testthat (≥ 3.0.0)
Published: 2021-01-18
Author: Cristian Pachón García ORCID iD [aut, cre], Pedro Delicado ORCID iD [aut]
Maintainer: Cristian Pachón García <cc.pachon at gmail.com>
BugReports: https://github.com/pachoning/bigmds/issues
License: MIT + file LICENSE
URL: https://github.com/pachoning/bigmds
NeedsCompilation: no
Materials: README NEWS
CRAN checks: bigmds results

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Reference manual: bigmds.pdf
Package source: bigmds_0.0.1.tar.gz
Windows binaries: r-devel: bigmds_0.0.1.zip, r-release: bigmds_0.0.1.zip, r-oldrel: bigmds_0.0.1.zip
macOS binaries: r-release: bigmds_0.0.1.tgz, r-oldrel: bigmds_0.0.1.tgz

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