kde.torus {ClusTorus}

Kernel density estimation using circular von Mises distribution

Description

kde.torus returns a kde using independent multivariate von mises kernel.

Usage

kde.torus(data, eval.point = NULL, concentration = 25)

Arguments

data

n x d matrix of toroidal data on [0, 2\pi)^d

eval.point

N x N numeric matrix on [0, 2\pi)^d. Default input is NULL, which represents the fine grid points on [0, 2\pi)^d.

concentration

positive number which has the role of \kappa of von Mises distribution. Default value is 25.

Value

kde.torus returns N-dimensional vector of kdes evaluated at eval.point

References

Jung, S., Park, K., & Kim, B. (2021). Clustering on the torus by conformal prediction. The Annals of Applied Statistics, 15(4), 1583-1603.

Di Marzio, M., Panzera, A., & Taylor, C. C. (2011). Kernel density estimation on the torus. Journal of Statistical Planning and Inference, 141(6), 2156-2173.

See Also

grid.torus

Examples

data <- ILE[1:200, 1:2]

kde.torus(data)