Package: OptCirClust
Type: Package
Title: Circular, Periodic, or Framed Data Clustering: Fast, Optimal,
        and Reproducible
Version: 0.0.3
Date: 2020-12-15
Authors@R: c(person("Tathagata", "Debnath", role = "aut", 
	                  comment = c(ORCID = "0000-0001-6445-275X")),
             person("Joe", "Song", role = c("aut", "cre"),
                    comment = c(ORCID = "0000-0002-6883-6547"),
		                email = "joemsong@cs.nmsu.edu"))
Author: Tathagata Debnath [aut] (<https://orcid.org/0000-0001-6445-275X>),
  Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>)
Maintainer: Joe Song <joemsong@cs.nmsu.edu>
Description: Fast, optimal, and reproducible clustering algorithms for
 circular, periodic, or framed data. The algorithms introduced here
 are based on a core algorithm for optimal framed clustering the authors
 have developed (under review). The runtime of these algorithms is
 O(K N log^2 N), where K is the number of clusters and N is the number
 of circular data points. On a desktop computer using a single
 processor core, millions of data points can be grouped into a few
 clusters within seconds. One can apply the algorithms to characterize
 events along circular DNA molecules, circular RNA molecules, and
 circular genomes of bacteria, chloroplast, and mitochondria. One can
 also cluster climate data along any given longitude or latitude.
 Periodic data clustering can be formulated as circular clustering.
 The algorithms offer a general high-performance solution to circular,
 periodic, or framed data clustering. 
VignetteBuilder: knitr
License: LGPL (>= 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
LinkingTo: Rcpp
Imports: Ckmeans.1d.dp, graphics, plotrix, Rcpp, stats
Suggests: ape, bazar, ggplot2, knitr, reshape2, rmarkdown, testthat
NeedsCompilation: yes
Packaged: 2020-12-17 03:17:02 UTC; joesong
Repository: CRAN
Date/Publication: 2020-12-18 16:10:08 UTC
