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Functions for testing randomness for a univariate time series with arbitrary distribution (discrete, continuous, mixture of both types) and for testing independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022).
Version: | 1.2.0 |
Depends: | R (≥ 3.5.0), doParallel, parallel, foreach, stats, copula |
Imports: | ggplot2, survey |
Published: | 2024-02-14 |
DOI: | 10.32614/CRAN.package.MixedIndTests |
Author: | Bouchra R. Nasri [aut, cre, cph], Bruno N Remillard [aut], Johanna G Neslehova [aut], Christian Genest [aut] |
Maintainer: | Bouchra R. Nasri <bouchra.nasri at umontreal.ca> |
License: | GPL-3 |
NeedsCompilation: | yes |
CRAN checks: | MixedIndTests results |
Reference manual: | MixedIndTests.pdf |
Package source: | MixedIndTests_1.2.0.tar.gz |
Windows binaries: | r-devel: MixedIndTests_1.2.0.zip, r-release: MixedIndTests_1.2.0.zip, r-oldrel: MixedIndTests_1.2.0.zip |
macOS binaries: | r-release (arm64): MixedIndTests_1.2.0.tgz, r-oldrel (arm64): MixedIndTests_1.2.0.tgz, r-release (x86_64): MixedIndTests_1.2.0.tgz, r-oldrel (x86_64): MixedIndTests_1.2.0.tgz |
Old sources: | MixedIndTests archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.