Circular Statistics, from "Topics in Circular Statistics" (2001)
S-plus original by Ulric Lund <ulund@calpoly.edu>, R port by
Claudio Agostinelli <claudio.agostinelli@unitn.it>
Circular Statistics, from "Topics in Circular Statistics"
(2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.

Algorithm for Computation of the Intersection Areas of N Circles
person(given = "Hugo",
family = "Salinas",
role = c("aut", "cre"),
email = "hugosal@comunidad.unam.mx",
comment = c(ORCID = "0000-0002-1734-2977"))
Implementation of Librino, Levorato, and Zorzi (2014) <doi:10.1002/wcm.2305>
algorithm for computation of the intersection areas of an arbitrary number of circles.

Circular drift-diffusion model for continuous reports.

c(person("Yi-Shin", "Lin", role = c("aut", "cre"),
email = "yishin.lin@utas.edu.au"),
person("Andrew", "Heathcote", role = "aut"),
person("Peter", "Kvam", role = "aut"))
Yi-Shin Lin [aut, cre],
Andrew Heathcote [aut],
Peter Kvam [aut]

Fast Silhouette on Circular or Linear Data Clusters
c(person("Yinong", "Chen", role = "aut",
comment = c(ORCID = "0000-0003-1641-1712")),
person("Tathagata", "Debnath", role = "aut",
comment = c(ORCID = "0000-0001-6445-275X")),
person("Andrew", "Cai", role = "aut",
comment = c(ORCID = "")),
person("Joe", "Song", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6883-6547"),
email = "joemsong@cs.nmsu.edu"))
Yinong Chen [aut] (<https://orcid.org/0000-0003-1641-1712>),
Tathagata Debnath [aut] (<https://orcid.org/0000-0001-6445-275X>),
Andrew Cai [aut],
Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>)
Calculating silhouette information for clusters on
circular or linear data using fast algorithms. These algorithms run in
linear time on sorted data, in contrast to quadratic time by the
definition of silhouette. When used together with the fast and optimal
circular clustering method FOCC (Debnath & Song 2021)
<doi:10.1109/TCBB.2021.3077573> implemented in R package 'OptCirClust',
circular silhouette can be maximized to find the optimal number of
circular clusters; it can also be used to estimate the period of noisy
periodical data.
cluster, ggplot2, knitr, rmarkdown, testthat (>= 3.0.0),
graphics