emp.transf

data matrix (rows: samples, columns: variables)

boolean, if TRUE it provides the classical (non-Monte-Carlo) transformation by the empirical distribution function, which is a reasonable choice for data of continuous distributions.

continuous

Transforms a matrix (rows: samples, columns: variables) into a matrix of uniform samples with the same dependence structure via the Monte Carlo empirical transform.

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

emp.transf: Monte Carlo empirical transform

Description

Usage

Arguments

References