[aut, cre],
Pedro Delicado
[aut]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
[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 |
| Materials: | README NEWS |
| CRAN checks: | bigmds results |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=bigmds to link to this page.