Package: multivariance 2.4.1

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>.

Authors:Björn Böttcher [aut, cre], Martin Keller-Ressel [ctb]

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NEWS

# Install 'multivariance' in R:
install.packages('multivariance', repos = c('https://bb-diesunddas.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.36 score 1 stars 23 scripts 262 downloads 32 exports 13 dependencies

Last updated 3 years agofrom:223488fe47. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-win-x86_64OKOct 25 2024
R-4.5-linux-x86_64OKOct 25 2024
R-4.4-win-x86_64OKOct 25 2024
R-4.4-mac-x86_64OKOct 25 2024
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R-4.3-win-x86_64OKOct 25 2024
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Exports:cdmcdmsclean.graphCMcorcoinscopula.multicorrelationcopula.multicorrelation.testcopula.multivariancedependence.structureemp.transffastdistfastEuclideanCdmindependence.testlayout_on_circlesm.multivarianceMcormulticorrelationmultivariancemultivariance.pvaluemultivariance.testmultivariance.timingmultivariances.allpearson.pvaluepearson.qfrejection.levelresample.multivarianceresample.pvalueresample.rejection.levelsample.cdmssample.colstetrahedrontotal.multivariance

Dependencies:clicpp11glueigraphlatticelifecyclemagrittrMatrixmicrobenchmarkpkgconfigRcpprlangvctrs

Readme and manuals

Help Manual

Help pageTopics
multivariance: Measuring Multivariate Dependence Using Distance Multivariancemultivariance-package
Extended Anscombe's Quartettanscombe.extended
computes a doubly centered distance matrixcdm
computes the doubly centered distance matricescdms
cleanup dependence structure graphclean.graph
dependence example: k-independent coin samplingcoins
coupla versions of distance multicorrelationCMcor copula.multicorrelation
independence tests using the copula versions of distance multivariancecopula.multicorrelation.test
copula version of distance multivariancecopula.multivariance
example dataset for 'dependence.structure'dep_struct_iterated_13_100
example dataset for 'dependence.structure'dep_struct_ring_15_100
example dataset for 'dependence.structure'dep_struct_several_26_100
example dataset for 'dependence.structure'dep_struct_star_9_100
determines the dependence structuredependence.structure
Monte Carlo empirical transformemp.transf
fast Euclidean distance matrixfastdist
fast centered Euclidean distance matrixfastEuclideanCdm
cluster detectionfind.cluster
test for independenceindependence.test
special igraph layout for the dependence structure visualizationlayout_on_circles
m distance multivariancem.multivariance
distance multicorrelationMcor multicorrelation
distance multivariancemultivariance
transform multivariance to p-valuemultivariance.pvalue
independence tests based on (total-/2-/3-) multivariancemultivariance.test
estimate of the computation timemultivariance.timing
simultaneous computation of multivariance and total/ 2-/ 3-multivariancemultivariances.all
fast p-value approximationpearson.pvalue
approximate distribution function of a Gaussian quadratic formpearson.qf
rejection level for the test statisticrejection.level
resampling (total /m-) multivarianceresample.multivariance
p-value via resamplingresample.pvalue
rejection level via resamplingresample.rejection.level
resamples doubly centered distance matricessample.cdms
resample the columns of a matrixsample.cols
dependence example: tetrahedron samplingtetrahedron
total distance multivariancetotal.multivariance