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.">

Ckmeans.1d.dp: Optimal, Fast, and Reproducible Univariate Clustering (original) (raw)

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
DOI: 10.32614/CRAN.package.Ckmeans.1d.dp
Author: Joe Song ORCID iD [aut, cre], Hua Zhong ORCID iD [aut], Haizhou Wang [aut]
Maintainer: Joe Song
License: LGPL (≥ 3)
NeedsCompilation: yes
Citation: Ckmeans.1d.dp citation info
Materials: README, NEWS
CRAN checks: Ckmeans.1d.dp results

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Reverse dependencies:

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

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