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Showing content from https://github.com/egarpor/polykde below:

egarpor/polykde: Kernel density estimation on the polysphere, hypersphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests

Companion package for the article Kernel density estimation with polyspherical data and its applications (García-Portugués and Meilán-Vila, 2024).

The folder /replication contains the scripts to replicate the numerical experiments and real data application of the paper and its Supplementary Material (SM):

• The script kde-sims.R reproduces the asymptotic normality experiment (Figures 5–8 in the SM).
• The script kde-effic.R computes the kernel efficiency table (Table 1 in the SM) and the kernel and kernel efficiency graphs (Figure 1 in the paper).
• The scripts jsd-sims-k2-S2.R, jsd-sims-hippo.R, and jsd-sims-k3-S10^2.R reproduce two simulation experiments for the $k$ -sample test in (Figures 9–12 in the SM).
• The scripts kde-spoke-dirs.R and test-spoke-dirs.R reproduce the real data application on the hippocampus shape analysis (Figure 3 in the paper and Figure 13 in the SM, and Figure 4 in the paper, respectively).

García-Portugués, E. and Meilán-Vila, A. (2024). Kernel density estimation with polyspherical data and its applications. arXiv:2411.04166. doi:10.48550/arXiv.2411.04166.

García-Portugués, E. and Meilán-Vila, A. (2023). Hippocampus shape analysis via skeletal models and kernel smoothing. In Larriba, Y. (Ed.), Statistical Methods at the Forefront of Biomedical Advances, pp. 63–82. Springer, Cham. doi:10.1007/978-3-031-32729-2_4.

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