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 six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021) <doi:10.48550/arXiv.2007.11919> (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021) <doi:10.48550/arXiv.2007.11919> (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).

Version:	3.0.0
Depends:	R (≥ 3.0.2)
Imports:	pracma, svd, corpcor, parallel, stats
Suggests:	testthat
Published:	2024-01-09
DOI:	10.32614/CRAN.package.bigmds
Author:	Cristian Pachón García [aut, cre], Pedro Delicado [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
Citation:	bigmds citation info
Materials:	README, NEWS
CRAN checks:	bigmds results

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