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Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <doi:10.48550/arXiv.1812.09869>).
Version: | 2.3.1 |
Depends: | R (≥ 3.5.0) |
Imports: | Rcpp (≥ 0.12.0), bigmemory (≥ 4.5.0), parallel (≥ 3.5.0), RColorBrewer, colorspace |
LinkingTo: | Rcpp, RcppArmadillo, BH, bigmemory |
Suggests: | knitr, rmarkdown |
Published: | 2020-06-30 |
DOI: | 10.32614/CRAN.package.bigMap |
Author: | Joan Garriga [aut, cre], Frederic Bartumeus [aut] |
Maintainer: | Joan Garriga <jgarriga at ceab.csic.es> |
License: | GPL-3 |
NeedsCompilation: | yes |
SystemRequirements: | GNU make |
Materials: | NEWS |
CRAN checks: | bigMap results |
Reference manual: | bigMap.pdf |
Vignettes: |
The bigMap R-package: quick reference |
Package source: | bigMap_2.3.1.tar.gz |
Windows binaries: | r-devel: bigMap_2.3.1.zip, r-release: bigMap_2.3.1.zip, r-oldrel: bigMap_2.3.1.zip |
macOS binaries: | r-release (arm64): bigMap_2.3.1.tgz, r-oldrel (arm64): bigMap_2.3.1.tgz, r-release (x86_64): bigMap_2.3.1.tgz, r-oldrel (x86_64): bigMap_2.3.1.tgz |
Old sources: | bigMap archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.