GridOnClusters: Cluster-Preserving Multivariate Joint Grid Discretization

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.

Version: 0.1.0
Imports: Rcpp, Ckmeans.1d.dp, cluster, fossil, dqrng, mclust, Rdpack, plotrix
LinkingTo: Rcpp
Suggests: FunChisq, knitr, testthat (≥ 2.1.0), rmarkdown
Published: 2022-01-28
Author: Jiandong Wang [aut], Sajal Kumar ORCID iD [aut], Joe Song ORCID iD [aut, cre]
Maintainer: Joe Song <joemsong at>
License: LGPL (≥ 3)
NeedsCompilation: yes
Citation: GridOnClusters citation info
Materials: README NEWS
CRAN checks: GridOnClusters results


Reference manual: GridOnClusters.pdf
Vignettes: Examples of joint grid discretization


Package source: GridOnClusters_0.1.0.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): GridOnClusters_0.1.0.tgz, r-release (arm64): GridOnClusters_0.1.0.tgz, r-oldrel (arm64): GridOnClusters_0.1.0.tgz, r-prerel (x86_64): GridOnClusters_0.1.0.tgz, r-release (x86_64): GridOnClusters_0.1.0.tgz
Old sources: GridOnClusters archive

Reverse dependencies:

Reverse suggests: FunChisq


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