RJcluster: RJ Clustering Algorithm

Clustering algorithm for high dimensional data. This algorithm is ideal for data where N << P. 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. When the P-dimensional feature vectors of objects are drawn independently from a K distinct mixture distribution, the N-dimensional rows of the modified Gram matrix XX^T/P converges almost surely to K distinct cluster means. This transformation/projection thus allows the clusters to be tighter with order of P. To simulate data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')".

Version: 2.5.0
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 1.0.2), matrixStats, infotheo, rlang, stats, graphics, profvis, mclust, foreach
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 2.1.0), knitr, rmarkdown
Published: 2021-04-06
Author: Rachael Shudde [aut, cre], Shahina Rahman [aut], Valen Johnson [aut]
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_2.5.0.tar.gz
Windows binaries: r-devel: RJcluster_2.5.0.zip, r-release: RJcluster_2.5.0.zip, r-oldrel: RJcluster_2.5.0.zip
macOS binaries: r-release: RJcluster_2.5.0.tgz, r-oldrel: RJcluster_2.5.0.tgz
Old sources: RJcluster archive

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