doubleCenterSymMat

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

normalize

internal

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

multivariance

Measuring Multivariate Dependence Using Distance Multivariance

Martin Keller-Ressel

doubleCenterSymMat function

double center a symmetric matrix — doubleCenterSymMat

double center a symmetric matrix

doubleCenterSymMat: double center a symmetric matrix

Description

Usage

Arguments