RJcluster: A Fast Clustering Algorithm for High Dimensional Data Based on the Gram Matrix Decomposition

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

Documentation:

Reference manual:	RJcluster.pdf
Vignettes:	RJclust_Vignette

Downloads:

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

Linking:

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