Ckmeans.1d.dp (4.3.2)

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Optimal, Fast, and Reproducible Univariate Clustering.

Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four types of problem including univariate k-means, k-median, k-segments, and multi-channel weighted k-means are solved with guaranteed optimality and reproducibility. The core algorithm minimizes 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 k. 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 that are adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility.

Maintainer: Joe Song
Author(s): Joe Song [aut, cre] (<>), Hua Zhong [aut] (<>), Haizhou Wang [aut]

License: LGPL (>= 3)

Uses: Rcpp, Rdpack, testthat, knitr, rmarkdown
Reverse suggests: DiffXTables, FunChisq, GridOnClusters, gsrc, vip, xgboost

Released 23 days ago.

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