Package: Ckmeans.1d.dp
Type: Package
Title: Optimal, Fast, and Reproducible Univariate Clustering
Version: 4.3.2
Date: 2020-03-14
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] (<https://orcid.org/0000-0002-6883-6547>),
  Hua Zhong [aut] (<https://orcid.org/0000-0003-1962-2603>),
  Haizhou Wang [aut]
Maintainer: Joe Song <joemsong@cs.nmsu.edu>
Description: 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.
License: LGPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: Rcpp, Rdpack
LinkingTo: Rcpp
NeedsCompilation: yes
Suggests: testthat, knitr, rmarkdown
RdMacros: Rdpack
VignetteBuilder: knitr
Packaged: 2020-03-14 16:52:09 UTC; joesong
Repository: CRAN
Date/Publication: 2020-03-14 17:50:02 UTC
Built: R 3.5.3; i386-w64-mingw32; 2020-04-22 05:02:42 UTC; windows
Archs: i386, x64
