GeneralisedCovarianceMeasure: Test for Conditional Independence Based on the Generalized Covariance Measure (GCM)

A statistical hypothesis test for conditional independence. It performs nonlinear regressions on the conditioning variable and then tests for a vanishing covariance between the resulting residuals. It can be applied to both univariate random variables and multivariate random vectors. Details of the method can be found in Rajen D. Shah and Jonas Peters: The Hardness of Conditional Independence Testing and the Generalised Covariance Measure, Annals of Statistics 48(3), 1514–1538, 2020.

Version:	0.2.0
Imports:	CVST, graphics, kernlab, mgcv, stats, xgboost
Published:	2022-03-24
Author:	Jonas Peters and Rajen D. Shah
Maintainer:	Jonas Peters <jonas.peters at math.ku.dk>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	GeneralisedCovarianceMeasure results

Documentation:

Reference manual:

GeneralisedCovarianceMeasure.pdf

Downloads:

Package source:	GeneralisedCovarianceMeasure_0.2.0.tar.gz
Windows binaries:	r-devel: GeneralisedCovarianceMeasure_0.2.0.zip, r-release: GeneralisedCovarianceMeasure_0.2.0.zip, r-oldrel: GeneralisedCovarianceMeasure_0.2.0.zip
macOS binaries:	r-release (arm64): GeneralisedCovarianceMeasure_0.2.0.tgz, r-oldrel (arm64): GeneralisedCovarianceMeasure_0.2.0.tgz, r-release (x86_64): GeneralisedCovarianceMeasure_0.2.0.tgz, r-oldrel (x86_64): GeneralisedCovarianceMeasure_0.2.0.tgz
Old sources:	GeneralisedCovarianceMeasure archive

Reverse dependencies:

Reverse imports:	weightedGCM
Reverse suggests:	ghcm

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