A collection of utilities for the statistical analysis of multivariate circular data using distributions based on Multivariate Nonnegative Trigonometric Sums (MNNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more.

The statistical analysis of circular data using distributions based on symmetric Nonnegative Trigonometric Sums (NNTS). It includes functions to perform empirical analysis and estimate the parameters of density functions. Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), "Multimodal Symmetric Circular Distributions Based on Nonnegative Trigonometric Sums and a Likelihood Ratio Test for Reflective Symmetry", <doi:10.48550/arXiv.2412.19501>.

Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.

Implementation of Bayesian models for spatial and spatio-temporal
             interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions.
             We developed the methods described in Jona Lasinio G. et al. (2012) <doi: 10.1214/12-aoas576>, 
             Wang F. et al. (2014) <doi: 10.1080/01621459.2014.934454> and 
             Mastrantonio G. et al. (2016) <doi: 10.1007/s11749-015-0458-y>.

Circular Statistics, from "Topics in Circular Statistics"
        (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.

Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.

Circular drift-diffusion model for continuous reports.

General functions for performing extreme value analysis on a circular 
    domain as part of the statistical methodology in the paper by Konzen, E., 
    Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.

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.