Package: RJcluster
Title: RJ Clustering Algorithm
Version: 2.5.0
Authors@R: 
    c(person(given = "Rachael",
           family = "Shudde",
           role = c("aut", "cre"),
           email = "rachael.shudde@gmail.com"),
           person(given = "Shahina",
           family = "Rahman",
           role = c("aut"),
           email = "srahman@stat.tamu.edu"),
          person(given = "Valen",
           family = "Johnson",
           role = c("aut"),
           email = "vejohnson@exchange.tamu.edu"))
Author: Rachael Shudde [aut, cre],
  Shahina Rahman [aut],
  Valen Johnson [aut]
Maintainer: Rachael Shudde <rachael.shudde@gmail.com>
Description: 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')".
License: GPL (>= 2)
Encoding: UTF-8
Imports: Rcpp (>= 1.0.2), matrixStats, infotheo, rlang, stats,
        graphics, profvis, mclust, foreach
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (>= 2.1.0), knitr, rmarkdown
RoxygenNote: 7.1.1
VignetteBuilder: knitr
Depends: R (>= 2.10)
NeedsCompilation: yes
Packaged: 2021-04-06 20:08:35 UTC; rachaelshudde
Repository: CRAN
Date/Publication: 2021-04-06 21:00:03 UTC
