Distance multivariance is a measure of dependence which can be used to detect
and quantify dependence. The necessary functions are implemented in this packages,
and examples are given. For the theoretic background we refer to the papers:
B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors: generalized distance covariance and Gaussian covariance. VMSTA, 2018, Vol. 5, No. 3, 353-383. <arXiv:1711.07778>.
B. Böttcher, M. Keller-Ressel, R.L. Schilling, Distance multivariance: New dependence measures for random vectors. <arXiv:1711.07775>.
B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using Distance Multivariance. <arXiv:1712.06532>.
G. Berschneider, B. Böttcher, On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. <arXiv:1808.07280>.
igraph, graphics, stats, Rcpp, microbenchmark

Distance multivariance is a measure of dependence which can be used to detect
and quantify dependence. The necessary functions are implemented in this packages,
and examples are given. For the theoretic background we refer to the papers:
B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using Distance Multivariance. <arXiv:1712.06532>.
B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors: generalized distance covariance and Gaussian covariance. VMSTA, 2018, Vol. 5, No. 3, 353-383. <arXiv:1711.07778>.
B. Böttcher, M. Keller-Ressel, R.L. Schilling, Distance multivariance: New dependence measures for random vectors. <arXiv:1711.07775>.
G. Berschneider, B. Böttcher, On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. <arXiv:1808.07280>.

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. Böttcher, 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>.