polykde/README.Rmd at main · egarpor/polykde · GitHub

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---
output:
  md_document:
    variant: gfm
---

```{r, setup, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE, comment = "#>", fig.path = "README/README-",
  message = FALSE, warning = FALSE, fig.asp = 1, fig.align = 'center'
)
```

# polykde <img src="man/figures/logo.png" align="right" height="175" alt="polykde logo"/>

```{r, badges, echo = FALSE, results = 'asis'}
cat(
  badger::badge_license(license = "GPLv3", color = "blue",
                        url = "https://www.gnu.org/licenses/gpl-3.0"),
  badger::badge_github_actions(action = "R-CMD-check"),
  badger::badge_github_actions(action = "test-coverage"),
  badger::badge_codecov(),
  badger::badge_cran_release(color = "green"),
  badger::badge_cran_download(pkg = NULL, type = "grand-total"),
  badger::badge_cran_download(pkg = NULL, type = "last-month")
)
```

## Overview

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

## Installation

```{r, install-CRAN, eval = FALSE}
# Install it from CRAN
install.packages("polykde")
library(polykde)
```

```{r, install-GitHub, eval = FALSE}
# Alternatively, from GitHub
library(pak)
pak("egarpor/polykde")
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](https://doi.org/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](https://doi.org/10.1007/978-3-031-32729-2_4).