uwot: The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction

An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) <doi:10.48550/arXiv.1802.03426>. It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) <doi:10.48550/arXiv.1602.00370> is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website (<https://github.com/jlmelville/uwot>) for more documentation and examples.

Version: 0.2.2
Depends: Matrix
Imports: FNN, irlba, methods, Rcpp, RcppAnnoy (≥ 0.0.17), RSpectra
LinkingTo: dqrng, Rcpp, RcppAnnoy, RcppProgress
Suggests: bigstatsr, covr, knitr, RcppHNSW, rmarkdown, rnndescent, testthat
Published: 2024-04-21
DOI: 10.32614/CRAN.package.uwot
Author: James Melville [aut, cre, cph], Aaron Lun [ctb], Mohamed Nadhir Djekidel [ctb], Yuhan Hao [ctb], Dirk Eddelbuettel [ctb]
Maintainer: James Melville <jlmelville at gmail.com>
BugReports: https://github.com/jlmelville/uwot/issues
License: GPL (≥ 3)
URL: https://github.com/jlmelville/uwot, https://jlmelville.github.io/uwot/
NeedsCompilation: yes
Materials: NEWS
CRAN checks: uwot results

Documentation:

Reference manual: uwot.pdf
Vignettes: uwot

Downloads:

Package source: uwot_0.2.2.tar.gz
Windows binaries: r-devel: uwot_0.2.2.zip, r-release: uwot_0.2.2.zip, r-oldrel: uwot_0.2.2.zip
macOS binaries: r-release (arm64): uwot_0.2.2.tgz, r-oldrel (arm64): uwot_0.1.16.tgz, r-release (x86_64): uwot_0.2.2.tgz, r-oldrel (x86_64): uwot_0.2.2.tgz
Old sources: uwot archive

Reverse dependencies:

Reverse imports: Banksy, bbknnR, BioM2, celda, CiteFuse, ClustAssess, embed, EmbedSOM, fastTopics, HDStIM, iCellR, infinityFlow, MAPFX, MOFA2, mumosa, musicatk, mxnorm, pipeComp, POMA, rliger, scater, sccore, scDHA, scROSHI, Seurat, symphony, WormTensor
Reverse suggests: bundle, CATALYST, celltrackR, conos, curatedMetagenomicData, cytofQC, densvis, DepecheR, doc2vec, dyndimred, Evacluster, FLAMES, ggspavis, graphlayouts, healthyR.ai, inferCSN, iSEEu, lemur, miloR, MOCHA, netSmooth, NGCHM, pagoda2, ProjectionBasedClustering, qeML, SCArray, SCIBER, scp, SingleCellMultiModal, slingshot, solitude, spatialHeatmap, standR, textplot, tidybulk, tidySingleCellExperiment, topicmodels.etm, TreeDist

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