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

Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problem 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. Weighted k-means can also process time series to perform peak calling. 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.

Version:	4.3.3
Imports:	Rcpp, Rdpack (≥ 0.6-1)
LinkingTo:	Rcpp
Suggests:	testthat, knitr, rmarkdown
Published:	2020-07-22
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

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