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 &amp; Song 2011) &lt;<a href="https://doi.org/10.32614%2FRJ-2011-015" target="_top">doi:10.32614/RJ-2011-015</a>&gt; (Song &amp; Zhong 2020) &lt;<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtaa613" target="_top">doi:10.1093/bioinformatics/btaa613</a>&gt;, 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.