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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 |
| Reference manual: | GeneralisedCovarianceMeasure.pdf |
| 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 imports: | weightedGCM |
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