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.

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.

Provides recent kernel density estimation methods for circular data, including adaptive and higher-order techniques. The implementation is based on recent advances in bandwidth selection and circular smoothing. Key methods include adaptive bandwidth selection methods by Zámečník et al. (2024) <doi:10.1007/s00180-023-01401-0>, complete cross-validation by Hasilová et al. (2024) <doi:10.59170/stattrans-2024-024>, Fourier-based plug-in rules by Tenreiro (2022) <doi:10.1080/10485252.2022.2057974>, and higher-order kernels by Tsuruta & Sagae (2017) <doi:10.1016/j.spl.2017.08.003>.