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

Plotting functions to create circular boxplots for grouped data.
  The primary 2-dimensional version creates concentric circular boxplots for
  specified groups, scaling the width of each boxplot to adjust for human
  perception. The 3-dimensional version maps these plots onto a torus which is suitable
  for periodic circular data such as wind direction over the course of a year. An
  example dataset of this type is provided for reference. For examples of circular
  boxplots and additional implementation details, see Berlinski et al. (2026) <doi:10.48550/arXiv.2602.05335>.

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 <doi:10.1002/env.2667>.

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>.

Provides methods for estimating circular graphical models using maximum likelihood estimation (MLE) and circular mean squared error (CMSE) approaches. The package includes tools for model fitting, network construction, network evaluation, and visualization. The CMSE-based methodology is related to Dar (2023) <https://open.metu.edu.tr/handle/11511/102577>. The package supports both simulated circular data and real-world applications, including gene-expression network analysis.