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: 3.1.0
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: 2021-07-21
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

Downloads:

Reference manual: RJcluster.pdf
Vignettes: RJclust_Vignette
Package source: RJcluster_3.1.0.tar.gz
Windows binaries: r-devel: RJcluster_3.1.0.zip, r-devel-UCRT: RJcluster_3.0.1.zip, r-release: RJcluster_3.1.0.zip, r-oldrel: RJcluster_3.0.1.zip
macOS binaries: r-release (arm64): RJcluster_2.5.0.tgz, r-release (x86_64): RJcluster_3.1.0.tgz, r-oldrel: RJcluster_3.1.0.tgz
Old sources: RJcluster archive

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