Package: RJcluster
Title: A Fast Clustering Algorithm for High Dimensional Data Based on
        the Gram Matrix Decomposition
Version: 3.2.4
Authors@R: 
    c(person(given = "Shahina",
           family = "Rahman",
           role = c("aut"),
           email = "srahman@stat.tamu.edu"),
          person(given = "Valen E.",
           family = "Johnson",
           role = c("aut"),
           email = "vejohnson@exchange.tamu.edu"),
           person(given = "Suhasini", family = "Subba Rao",
           role = c("aut"),
           email = "suhasini@stat.tamu.edu"),
           person(given = "Rachael",
           family = "Shudde",
           role = c("aut", "cre", "trl"),
           email = "rachael.shudde@gmail.com"))
Author: Shahina Rahman [aut],
  Valen E. Johnson [aut],
  Suhasini Subba Rao [aut],
  Rachael Shudde [aut, cre, trl]
Maintainer: Rachael Shudde <rachael.shudde@gmail.com>
Description: 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")'. 
License: GPL (>= 2)
Encoding: UTF-8
Imports: Rcpp (>= 1.0.2), matrixStats, infotheo, rlang, stats,
        graphics, profvis, mclust, foreach, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (>= 2.1.0), knitr, rmarkdown
RoxygenNote: 7.1.1
VignetteBuilder: knitr
Depends: R (>= 2.10)
NeedsCompilation: yes
Packaged: 2022-02-14 21:00:27 UTC; rachaelshudde
Repository: CRAN
Date/Publication: 2022-02-14 21:30:02 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-21 06:43:21 UTC; windows
Archs: i386, x64
