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Ckmeans.1d.dp: Optimal, Fast, and Reproducible Univariate Clustering

Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015> (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data.

Version: 4.3.5
Imports: Rcpp, Rdpack (≥ 0.6-1)
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, RColorBrewer
Published: 2023-08-19
Author: Joe Song ORCID iD [aut, cre], Hua Zhong ORCID iD [aut], Haizhou Wang [aut]
Maintainer: Joe Song <joemsong at cs.nmsu.edu>
License: LGPL (≥ 3)
NeedsCompilation: yes
Citation: Ckmeans.1d.dp citation info
Materials: README NEWS
CRAN checks: Ckmeans.1d.dp results

Documentation:

Reference manual: Ckmeans.1d.dp.pdf
Vignettes: Tutorial: Optimal univariate clustering
Note: Weight scaling in cluster analysis
Tutorial: Adaptive versus regular histograms

Downloads:

Package source: Ckmeans.1d.dp_4.3.5.tar.gz
Windows binaries: r-devel: Ckmeans.1d.dp_4.3.5.zip, r-release: Ckmeans.1d.dp_4.3.5.zip, r-oldrel: Ckmeans.1d.dp_4.3.5.zip
macOS binaries: r-release (arm64): Ckmeans.1d.dp_4.3.5.tgz, r-oldrel (arm64): Ckmeans.1d.dp_4.3.5.tgz, r-release (x86_64): Ckmeans.1d.dp_4.3.5.tgz, r-oldrel (x86_64): Ckmeans.1d.dp_4.3.5.tgz
Old sources: Ckmeans.1d.dp archive

Reverse dependencies:

Reverse depends: GenomicOZone
Reverse imports: CellBarcode, clusterHD, GridOnClusters, Harman, kcmeans, OptCirClust, SPECK, STREAK, weitrix
Reverse suggests: bakR, DiffXTables, FunChisq, xgboost

Linking:

Please use the canonical form https://CRAN.R-project.org/package=Ckmeans.1d.dp to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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