GridOnClusters: Joint Discretization of Data on a Grid that Preserves Clusters

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.

Version: 0.0.7
Depends: R (≥ 3.0)
Imports: Rcpp, cluster, fossil, dqrng
LinkingTo: Rcpp
Suggests: Ckmeans.1d.dp, FunChisq, knitr, testthat (≥ 2.1.0), rmarkdown
Published: 2020-04-06
Author: Jiandong Wang [aut], Sajal Kumar ORCID iD [aut], Joe Song ORCID iD [aut, cre]
Maintainer: Joe Song <joemsong at cs.nmsu.edu>
License: LGPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: GridOnClusters results

Downloads:

Reference manual: GridOnClusters.pdf
Vignettes: Examples of joint grid discretization
Package source: GridOnClusters_0.0.7.tar.gz
Windows binaries: r-prerelease: GridOnClusters_0.0.7.zip, r-release: GridOnClusters_0.0.7.zip, r-oldrel: GridOnClusters_0.0.7.zip
macOS binaries: r-prerelease: GridOnClusters_0.0.7.tgz, r-release: GridOnClusters_0.0.7.tgz, r-oldrel: not available
Old sources: GridOnClusters archive

Reverse dependencies:

Reverse suggests: FunChisq

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GridOnClusters to link to this page.