Title: Circular, Periodic, or Framed Data Clustering: Fast, Optimal,
Authors@R: c(person("Tathagata", "Debnath", role = "aut",
Author: Tathagata Debnath [aut] (<https://orcid.org/0000-0001-6445-275X>),
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 clustered 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
Imports: Ckmeans.1d.dp, graphics, plotrix, Rcpp, stats
Suggests: ape, bazar, ggplot2, knitr, reshape2, rmarkdown, testthat

circular, periodic, or framed data. The algorithms introduced in this
package are based on a core optimal framed clustering algorithm. 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 circular data
points can be clustered within seconds. One can use 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.

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,