Package: GridOnClusters
Type: Package
Title: Cluster-Preserving Multivariate Joint Grid Discretization
Version: 0.1.0.2
Date: 2025-05-09
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@nmsu.edu",
             comment = c(ORCID = "0000-0002-6883-6547")))
Author: Jiandong Wang [aut],
  Sajal Kumar [aut] (ORCID: <https://orcid.org/0000-0003-0930-1582>),
  Joe Song [aut, cre] (ORCID: <https://orcid.org/0000-0002-6883-6547>)
Maintainer: Joe Song <joemsong@nmsu.edu>
Description: Discretize multivariate continuous data using a grid
 that captures the joint distribution via preserving clusters in
 the original data (Wang et al 2020) <doi:10.1145/3388440.3412415>.
 Joint grid discretization is applicable as a data transformation step
 to prepare data for model-free inference of association, function, or
 causality.
Imports: Rcpp, Ckmeans.1d.dp, cluster, fossil, dqrng, mclust, Rdpack,
        plotrix
Suggests: FunChisq, knitr, testthat (>= 3.0.0), rmarkdown
RdMacros: Rdpack
License: LGPL (>= 3)
Encoding: UTF-8
LinkingTo: Rcpp
RoxygenNote: 7.3.1
NeedsCompilation: yes
Config/testthat/edition: 3
VignetteBuilder: knitr
Packaged: 2025-05-27 08:22:29 UTC; joesong
Repository: CRAN
Date/Publication: 2025-05-27 09:10:19 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2025-10-11 01:53:11 UTC; windows
Archs: x64
