pearson.qf

value at which the distribution function is to be evaluated

vector with the mean, variance and skewness of the quadratic form

moment

logical, indicating of the lower or upper tail of the distribution function should be calculated

lower.tail

logical, if <code>TRUE</code> a warning is issued if negative moments are sanitized to 0.

verbose

Approximation of the of the value of the distribution function of a Gaussian quadratic form based on its first three moments.

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>.

pearson.qf: approximate distribution function of a Gaussian quadratic form

Description

Usage

Arguments

Details

References