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 proposed by Delicado P. and C. Pachón-García (2021) <arXiv:2007.11919>. - Interpolation MDS, also proposed by Delicado P. and C. Pachón-García (2021) <arXiv:2007.11919>, which uses Gower's interpolation formula as described in Gower, J. C. and D. J. Hand (1995). - Fast MDS, which is an implementation of the algorithm proposed by Yang, T., J. Liu, L. McMillan, and W. Wang (2006). The main idea of these algorithms is based on partitioning the data set into small pieces, where classical methods can work. In order to align all the solutions, Procrustes formula is used as described in Borg, I. and P. Groenen (2005).

Version: 2.0.1
Imports: stats, parallel
Suggests: testthat
Published: 2021-10-05
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
Citation: bigmds citation info
Materials: README NEWS
CRAN checks: bigmds results


Reference manual: bigmds.pdf


Package source: bigmds_2.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bigmds_2.0.1.tgz, r-oldrel (arm64): bigmds_2.0.1.tgz, r-release (x86_64): bigmds_2.0.1.tgz, r-oldrel (x86_64): bigmds_2.0.1.tgz
Old sources: bigmds archive


Please use the canonical form to link to this page.