Optimal, Fast, and Reproducible Univariate Clustering R package Ckmeans.1d.dp version 4.3.5 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. Software Imports: Rcpp, Rdpack (>= 0.6-1) LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, RColorBrewer Joe Song <joemsong@cs.nmsu.edu> Comprehensive R Archive Network (CRAN) 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] LGPL (>= 3) 2023-08-19 application/tgz https://CRAN.R-project.org/package=Ckmeans.1d.dp doi:10.32614/CRAN.package.Ckmeans.1d.dp