Package: GridOnClusters
Type: Package
Title: Joint Discretization of Data on a Grid that Preserves Clusters
Version: 0.0.7
Date: 2020-04-03
Authors@R: 
    c(person(given = "Jiandong",
             family = "Wang",
             role = "aut",
             email = "wangjd24@nmsu.edu"),
      person(given = "Sajal",
             family = "Kumar",
             role = "aut",
             email = "sajal49@nmsu.edu",
             comment = c(ORCID = "0000-0003-0930-1582")),
      person(given = "Joe",
             family = "Song",
             role = c("aut", "cre"),
             email = "joemsong@cs.nmsu.edu",
             comment = c(ORCID = "0000-0002-6883-6547")))
Author: Jiandong Wang [aut],
  Sajal Kumar [aut] (<https://orcid.org/0000-0003-0930-1582>),
  Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>)
Maintainer: Joe Song <joemsong@cs.nmsu.edu>
Description: Discretize multivariate continuous data using a grid
    that captures the joint distribution via preserving clusters in
    the original data. Joint grid discretization is applicable as a
    data transformation step before using other methods to infer
    association, function, or causality without assuming a
    parametric model.
Imports: Rcpp, cluster, fossil, dqrng
Suggests: Ckmeans.1d.dp, FunChisq, knitr, testthat (>= 2.1.0),
        rmarkdown
Depends: R (>= 3.0)
License: LGPL (>= 3)
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp
RoxygenNote: 7.1.0
NeedsCompilation: yes
VignetteBuilder: knitr
Packaged: 2020-04-04 05:58:10 UTC; joesong
Repository: CRAN
Date/Publication: 2020-04-06 12:42:11 UTC
Built: R 4.0.2; x86_64-w64-mingw32; 2020-08-02 08:35:54 UTC; windows
Archs: i386, x64
