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 |
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.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, SCORPIUS |
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