bigmds: Multidimensional Scaling for Big Data

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 developed by Delicado and Pachon-Garcia, (2020) <arXiv:2007.11919>. - Fast MDS, which is an implementation of Tynia, Y., L. Jinze, M. Leonard, and W. Wei, (2006). - MDS based on Gower interpolation, which uses Gower interpolation formula as described in Gower, J.C. and D.J, Hand (1995, ISBN: 978-0-412-71630-0). The main idea of these methods is based on partitioning the dataset into small pieces, where classical methods can work. In order to align all the solutions, it is used Procrustes formula as described in Borg, I. and Groenen, P. (2005, ISBN : 978-0-387-25150-9).

Version: 1.0.0
Imports: stats, pdist
Suggests: testthat (≥ 3.0.0)
Published: 2021-03-29
Author: Cristian Pachón García ORCID iD [aut, cre], Pedro Delicado ORCID iD [aut]
Maintainer: Cristian Pachón García <cc.pachon at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: bigmds results


Reference manual: bigmds.pdf
Package source: bigmds_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: bigmds_1.0.0.tgz, r-oldrel: bigmds_1.0.0.tgz
Old sources: bigmds archive


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