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 at a large number of 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: | 4.3.4 |
| Imports: | Rcpp, Rdpack (≥ 0.6-1) |
| LinkingTo: | Rcpp |
| Suggests: | testthat, knitr, rmarkdown, RColorBrewer |
| Published: | 2022-01-31 |
| Author: | Joe Song  [aut,
cre],
Hua Zhong [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_4.3.4.tar.gz |
| Windows binaries: | r-devel: Ckmeans.1d.dp_4.3.4.zip, r-release: Ckmeans.1d.dp_4.3.4.zip, r-oldrel: Ckmeans.1d.dp_4.3.4.zip |
| macOS binaries: | r-release (arm64): Ckmeans.1d.dp_4.3.4.tgz, r-oldrel (arm64): Ckmeans.1d.dp_4.3.4.tgz, r-release (x86_64): Ckmeans.1d.dp_4.3.4.tgz, r-oldrel (x86_64): Ckmeans.1d.dp_4.3.4.tgz |
| Old sources: | Ckmeans.1d.dp archive |
Reverse dependencies:
| Reverse depends: | GenomicOZone |
| Reverse imports: | autostats, CellBarcode, clusterHD, emba, GridOnClusters, Harman, OptCirClust, weitrix |
| Reverse suggests: | DiffXTables, FunChisq, vip, xgboost |
Linking:
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