fastEuclideanCdm

matrix with sample rows for which the distance matrix is computed (to use with vectors, use <code>as.matrix(x)</code>)

boolean. If <code>TRUE</code> the matrix will be normalized to mean 1.

normalize

fast centered Euclidean distance matrix

Distance multivariance is a measure of dependence which can be used to detect
and quantify dependence of arbitrarily many random vectors. The necessary functions are
implemented in this packages and examples are given. It includes: distance multivariance,
distance multicorrelation, dependence structure detection, tests of independence and
copula versions of distance multivariance based on the Monte Carlo empirical transform.
Detailed references are given in the package description, as starting point for the
theoretic background we refer to:
B. B<c3><b6>ttcher, Dependence and Dependence Structures: Estimation and Visualization Using
the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020),
<doi:10.1515/stat-2020-0001>.

Bj<c3><b6>rn B<c3><b6>ttcher

fastEuclideanCdm: fast centered Euclidean distance matrix

Description

Usage

Arguments