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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

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egarpor/polykde

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polykde

License: GPLv3 R build status R build status

Overview

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

Replicability

The folder /replication contains the scripts to replicate the numerical experiments of the Supplementary Material (SM):

  • The script kde-sims.R reproduces the asymptotic normality experiment in Section B.1 of the SM.
  • The script kde-efic.R computes the kernel efficiency table in Section B.2 of the SM and plots in Section 4 of the paper.
  • The two scripts jsd-sims-k2-S2.R and jsd-sims-k3-S10^2.R reproduce two simulation experiments for the $k$-sample test in Section B.3 of the SM.

References

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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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

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