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). Joint grid discretization is applicable as a data transformation step to prepare data for model-free inference of association, function, or causality.

Version: 0.0.8
Depends: R (≥ 3.0)
Imports: Rcpp, cluster, fossil, dqrng, Rdpack, plotrix
LinkingTo: Rcpp
Suggests: Ckmeans.1d.dp, FunChisq, knitr, testthat (≥ 2.1.0), rmarkdown
Published: 2020-09-15
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
Citation: GridOnClusters citation info
Materials: README NEWS
CRAN checks: GridOnClusters results

Downloads:

Reference manual: GridOnClusters.pdf
Vignettes: Examples of joint grid discretization
Package source: GridOnClusters_0.0.8.tar.gz
Windows binaries: r-devel: GridOnClusters_0.0.8.zip, r-release: GridOnClusters_0.0.8.zip, r-oldrel: GridOnClusters_0.0.8.zip
macOS binaries: r-release: GridOnClusters_0.0.8.tgz, r-oldrel: GridOnClusters_0.0.8.tgz
Old sources: GridOnClusters archive

Reverse dependencies:

Reverse suggests: FunChisq

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