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Clustering algorithm for high dimensional data. Assuming that P feature measurements on N objects are arranged in an N×P matrix X, this package provides clustering based on the left Gram matrix XX^T. To simulate test data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')". To cite this package, type 'citation("RJcluster")'.
Version: | 3.2.4 |
Depends: | R (≥ 2.10) |
Imports: | Rcpp (≥ 1.0.2), matrixStats, infotheo, rlang, stats, graphics, profvis, mclust, foreach, utils |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | testthat (≥ 2.1.0), knitr, rmarkdown |
Published: | 2022-02-14 |
DOI: | 10.32614/CRAN.package.RJcluster |
Author: | Shahina Rahman [aut], Valen E. Johnson [aut], Suhasini Subba Rao [aut], Rachael Shudde [aut, cre, trl] |
Maintainer: | Rachael Shudde <rachael.shudde at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
Materials: | README |
CRAN checks: | RJcluster results |
Reference manual: | RJcluster.pdf |
Vignettes: |
RJclust_Vignette |
Package source: | RJcluster_3.2.4.tar.gz |
Windows binaries: | r-devel: RJcluster_3.2.4.zip, r-release: RJcluster_3.2.4.zip, r-oldrel: RJcluster_3.2.4.zip |
macOS binaries: | r-release (arm64): RJcluster_3.2.4.tgz, r-oldrel (arm64): RJcluster_3.2.4.tgz, r-release (x86_64): RJcluster_3.2.4.tgz, r-oldrel (x86_64): RJcluster_3.2.4.tgz |
Old sources: | RJcluster 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.