kde.torus

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

data

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

eval.point

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

concentration

<code>kde.torus</code> returns a kde using independent multivariate von mises kernel.

Provides various tools of for clustering multivariate angular
data on the torus. The package provides angular
adaptations of usual clustering methods such as the k-means
clustering, pairwise angular distances, which can be used as an
input for distance-based clustering algorithms, and implements
clustering based on the conformal prediction framework. Options
for the conformal scores include scores based on a kernel density
estimate, multivariate von Mises mixtures, and naive k-means clusters.
Moreover, the package provides some basic data handling tools for
angular data.

kde.torus: Kernel density estimation using circular von Mises distribution

Description

Usage

Arguments

Value

References

See Also

Examples