lmds: Landmark Multi-Dimensional Scaling

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 ORCID iD [aut, cre] (rcannood), Wouter Saelens ORCID iD [aut] (zouter)
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 dependencies:

Reverse imports: dyndimred, dyngen, SCORPIUS


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