Package: Ckmeans.1d.dp Type: Package Title: Optimal, Fast, and Reproducible Univariate Clustering Version: 4.3.5 Date: 2023-08-19 Authors@R: c(person("Joe", "Song", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6883-6547"), email = "joemsong@cs.nmsu.edu"), person("Hua", "Zhong", role = "aut", comment = c(ORCID = "0000-0003-1962-2603")), person("Haizhou", "Wang", role = "aut")) Author: Joe Song [aut, cre] (), Hua Zhong [aut] (), Haizhou Wang [aut] Maintainer: Joe Song Description: Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) (Song & Zhong 2020) , 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. 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: 2023-08-19 17:13:22 UTC; joesong Repository: CRAN Date/Publication: 2023-08-19 18:12:31 UTC