Package: Ckmeans.1d.dp
Type: Package
Title: Optimal, Fast, and Reproducible Univariate Clustering
Version: 4.3.4
Date: 2022-01-30
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 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 at a large number of 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.
License: LGPL (>= 3)
Encoding: UTF-8
Imports: Rcpp, Rdpack (>= 0.6-1)
LinkingTo: Rcpp
NeedsCompilation: yes
Suggests: testthat, knitr, rmarkdown, RColorBrewer
RdMacros: Rdpack
VignetteBuilder: knitr
Packaged: 2022-01-31 00:45:01 UTC; joesong
Repository: CRAN
Date/Publication: 2022-01-31 01:10:02 UTC
Built: R 4.0.5; x86_64-apple-darwin17.0; 2022-01-31 11:30:21 UTC; unix
Archs: Ckmeans.1d.dp.so.dSYM
