Package: Ckmeans.1d.dp
Type: Package
Title: Optimal, Fast, and Reproducible Univariate Clustering
Version: 4.3.6
Date: 2026-07-25
Authors@R: 
  c(person("Joe", "Song", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6883-6547"),
		       email = "joemsong@nmsu.edu"),
	  person("Hua", "Zhong", role = "aut", 
	         comment = c(ORCID = "0000-0003-1962-2603")),
	  person("Haizhou", "Wang", role = "aut"))
Author: Joe Song [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-6883-6547>), Hua Zhong [aut]
        (ORCID: <https://orcid.org/0000-0003-1962-2603>), Haizhou Wang
        [aut]
Maintainer: Joe Song <joemsong@nmsu.edu>
Description: Fast, optimal, and reproducible 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. It substantially outperforms heuristic
        clustering in both efficiency and accuracy as the number of
        clusters increases. 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 in addition to univariate clustering.
License: LGPL (>= 3)
Encoding: UTF-8
Imports: Rcpp, Rdpack (>= 0.6-1)
LinkingTo: Rcpp
NeedsCompilation: yes
Suggests: testthat, knitr, rmarkdown, RColorBrewer
RdMacros: Rdpack
VignetteBuilder: knitr
Packaged: 2026-09-28 09:10:17 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2026-07-26 16:30:13 UTC
RemoteUrl: https://github.com/cran/Ckmeans.1d.dp
RemoteRef: HEAD
RemoteSha: c960a5334000b27ebc8badec661dac9784e430ba