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, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors I. Generalized distance covariance and Gaussian covariance. Preprint 2017, <arXiv:1711.07778>. B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors II. Distance multivariance and Gaussian multivariance. Preprint 2017, <arXiv:1711.07775>. B. Böttcher, Dependence Structures - Estimation and Visualization Using Distance Multivariance. Preprint 2017, <arXiv:1712.06532>.

Version: 1.1.0
Depends: R (≥ 3.3.0)
Imports: abind, igraph, graphics, stats
Suggests: testthat
Published: 2018-01-10
Author: Björn Böttcher [aut, cre], Martin Keller-Ressel [ctb]
Maintainer: Björn Böttcher <bjoern.boettcher at tu-dresden.de>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: multivariance results

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Reference manual: multivariance.pdf
Package source: multivariance_1.1.0.tar.gz
Windows binaries: r-devel: multivariance_1.1.0.zip, r-release: multivariance_1.1.0.zip, r-oldrel: multivariance_1.1.0.zip
OS X binaries: r-release: multivariance_1.1.0.tgz, r-oldrel: multivariance_1.1.0.tgz
Old sources: multivariance archive

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