Ckmeans.1d.dp (3.4.6-6)

Optimal and Fast Univariate Clustering.

A fast dynamic programming algorithm for optimal univariate clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. With optional weights, the algorithm can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful alternative to heuristic methods for univariate data analysis.

Maintainer: Joe Song
Author(s): Joe Song [aut, cre], Haizhou Wang [aut]

License: LGPL (>= 3)

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

Released almost 3 years ago.