polykde

Overview

Companion package for the article Kernel density estimation with polyspherical data and its applications (García-Portugués and Meilán-Vila, 2025).

Installation

# Install it from CRAN
install.packages("polykde")
library(polykde)

# Alternatively, from GitHub
library(pak)
# pak("egarpor/polykde")
install.packages("polykde", repos = c('https://egarpor.r-universe.dev', 'https://cloud.r-project.org'))
library(polykde)

Replicability

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).

References

García-Portugués, E. and Meilán-Vila, A. (2025). Kernel density estimation with polyspherical data and its applications. Journal of the American Statistical Association, to appear. doi:10.1080/01621459.2025.2521898.

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.