A fast dimensionality reduction method scaleable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated.
| Version: | 0.1.0 |
| Imports: | assertthat, dynutils (≥ 1.0.3), irlba, Matrix |
| Suggests: | testthat |
| Published: | 2019-09-27 |
| DOI: | 10.32614/CRAN.package.lmds |
| Author: | Robrecht Cannoodt |
| Maintainer: | Robrecht Cannoodt <rcannood at gmail.com> |
| BugReports: | https://github.com/dynverse/lmds/issues |
| License: | GPL-3 |
| URL: | http://github.com/dynverse/lmds |
| NeedsCompilation: | no |
| Materials: | README, NEWS |
| CRAN checks: | lmds results |
| Reference manual: | lmds.html , lmds.pdf |
| Package source: | lmds_0.1.0.tar.gz |
| Windows binaries: | r-devel: lmds_0.1.0.zip, r-release: lmds_0.1.0.zip, r-oldrel: lmds_0.1.0.zip |
| macOS binaries: | r-release (arm64): lmds_0.1.0.tgz, r-oldrel (arm64): lmds_0.1.0.tgz, r-release (x86_64): lmds_0.1.0.tgz, r-oldrel (x86_64): lmds_0.1.0.tgz |
| Reverse imports: | dyndimred, dyngen, SCORPIUS |
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