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:	0.1.0.1
Imports:	Rcpp, Ckmeans.1d.dp, cluster, fossil, dqrng, mclust, Rdpack, plotrix
LinkingTo:	Rcpp
Suggests:	FunChisq, knitr, testthat (≥ 3.0.0), rmarkdown
Published:	2024-05-10
DOI:	10.32614/CRAN.package.GridOnClusters
Author:	Jiandong Wang [aut], Sajal Kumar [aut], Joe Song [aut, cre]
Maintainer:	Joe Song <joemsong at cs.nmsu.edu>
License:	LGPL (≥ 3)
NeedsCompilation:	yes
Citation:	GridOnClusters citation info
Materials:	README NEWS
CRAN checks:	GridOnClusters results

Documentation:

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

Downloads:

Package source:	GridOnClusters_0.1.0.1.tar.gz
Windows binaries:	r-devel: GridOnClusters_0.1.0.1.zip, r-release: GridOnClusters_0.1.0.1.zip, r-oldrel: GridOnClusters_0.1.0.1.zip
macOS binaries:	r-release (arm64): GridOnClusters_0.1.0.1.tgz, r-oldrel (arm64): GridOnClusters_0.1.0.1.tgz, r-release (x86_64): GridOnClusters_0.1.0.1.tgz, r-oldrel (x86_64): GridOnClusters_0.1.0.1.tgz
Old sources:	GridOnClusters archive

Reverse dependencies:

Reverse suggests:

FunChisq

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