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 |
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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 |
Maintainer: | Joe Song |
License: | LGPL (≥ 3) |
NeedsCompilation: | yes |
Citation: | Ckmeans.1d.dp citation info |
Materials: | README NEWS |
CRAN checks: | Ckmeans.1d.dp results |
Documentation:
Downloads:
Reverse dependencies:
Reverse depends: | GenomicOZone |
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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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