umap: Uniform Manifold Approximation and Projection

Uniform manifold approximation and projection is a technique for dimension reduction. The algorithm was described by McInnes and Healy (2018) in <arXiv:1802.03426>. This package provides an interface for two implementations. One is written from scratch, including components for nearest-neighbor search and for embedding. The second implementation is a wrapper for 'python' package 'umap-learn' (requires separate installation, see vignette for more details).

Depends: R (≥ 3.1.2)
Imports: methods, openssl, reticulate, Rcpp (≥ 0.12.6), RSpectra, stats
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat
Published: 2020-11-04
Author: Tomasz Konopka [aut, cre]
Maintainer: Tomasz Konopka <tokonopka at>
License: MIT + file LICENSE
NeedsCompilation: yes
CRAN checks: umap results


Reference manual: umap.pdf
Vignettes: Uniform Manifold Approximate and Projection in R
Interfacing with 'umap-learn'
Package source: umap_0.2.7.0.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): umap_0.2.7.0.tgz, r-release (x86_64): umap_0.2.7.0.tgz, r-oldrel: umap_0.2.7.0.tgz
Old sources: umap archive

Reverse dependencies:

Reverse imports: animalcules, CelliD, ChromSCape, CytoTree, EmbedSOM, FateID, HIPPO, ILoReg, InterCellar, M3C, MatrixQCvis, RaceID, RCSL, RCSL, scDataviz, sRACIPE, tomoda
Reverse suggests: cola, dimRed, HDCytoData, OTclust, ProjectionBasedClustering


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