multivariance: Measuring Multivariate Dependence Using Distance Multivariance

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>.

Version: 2.2.0
Depends: R (≥ 3.3.0)
Imports: igraph, graphics, stats, Rcpp, microbenchmark
LinkingTo: Rcpp
Suggests: testthat
Published: 2019-06-18
Author: Björn Böttcher [aut, cre], Martin Keller-Ressel [ctb]
Maintainer: Björn Böttcher <bjoern.boettcher at>
License: GPL-3
NeedsCompilation: yes
Materials: NEWS
CRAN checks: multivariance results


Reference manual: multivariance.pdf
Package source: multivariance_2.2.0.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: multivariance_2.2.0.tgz, r-oldrel: multivariance_2.2.0.tgz
Old sources: multivariance archive


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