multivariance: Measuring Multivariate Dependence Using Distance Multivariance

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>.

Version: 2.3.0
Depends: R (≥ 3.3.0)
Imports: igraph, graphics, stats, Rcpp, microbenchmark
LinkingTo: Rcpp
Suggests: testthat
Published: 2020-04-23
Author: Björn Böttcher [aut, cre], Martin Keller-Ressel [ctb]
Maintainer: Björn Böttcher <bjoern.boettcher at tu-dresden.de>
License: GPL-3
NeedsCompilation: yes
Materials: NEWS
CRAN checks: multivariance results

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Reference manual: multivariance.pdf
Package source: multivariance_2.3.0.tar.gz
Windows binaries: r-prerelease: multivariance_2.2.0.zip, r-release: multivariance_2.2.0.zip, r-oldrel: multivariance_2.2.0.zip
macOS binaries: r-prerelease: multivariance_2.2.0.tgz, r-release: multivariance_2.3.0.tgz, r-oldrel: multivariance_2.2.0.tgz
Old sources: multivariance archive

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