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 |
Reference manual: | uwot.pdf |
Vignettes: |
uwot |
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-devel (arm64): uwot_0.2.2.tgz, r-release (arm64): uwot_0.2.2.tgz, r-oldrel (arm64): uwot_0.2.2.tgz, r-devel (x86_64): uwot_0.2.2.tgz, r-release (x86_64): uwot_0.2.2.tgz, r-oldrel (x86_64): uwot_0.2.2.tgz |
Old sources: | uwot archive |
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, MiscMetabar, MOCHA, netSmooth, NGCHM, pagoda2, ProjectionBasedClustering, qeML, rbiom, SCArray, SCIBER, scp, SingleCellMultiModal, slingshot, solitude, spatialHeatmap, standR, textplot, tidybulk, tidySingleCellExperiment, topicmodels.etm, TreeDist |
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