ghcm: Functional Conditional Independence Testing with the GHCM

A statistical hypothesis test for conditional independence. Given residuals from a sufficiently powerful regression, it tests whether the covariance of the residuals is vanishing. It can be applied to both discretely-observed functional data and multivariate data. Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas Peters (2021) <arXiv:2101.07108>.

Version: 3.0.0
Depends: R (≥ 4.0.0)
Imports: graphics, MASS, refund, stats, utils, CompQuadForm, Rcpp, splines
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, bookdown, GeneralisedCovarianceMeasure, ggplot2, reshape2, dplyr, tidyr
Published: 2022-02-20
Author: Anton Rask Lundborg [aut, cre], Rajen D. Shah [aut], Jonas Peters [aut]
Maintainer: Anton Rask Lundborg <a.lundborg at statslab.cam.ac.uk>
BugReports: https://github.com/arlundborg/ghcm/issues
License: MIT + file LICENSE
URL: https://github.com/arlundborg/ghcm
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: ghcm results

Documentation:

Reference manual: ghcm.pdf
Vignettes: ghcm

Downloads:

Package source: ghcm_3.0.0.tar.gz
Windows binaries: r-devel: ghcm_3.0.0.zip, r-release: ghcm_3.0.0.zip, r-oldrel: ghcm_3.0.0.zip
macOS binaries: r-release (arm64): ghcm_3.0.0.tgz, r-release (x86_64): ghcm_3.0.0.tgz, r-oldrel: ghcm_3.0.0.tgz
Old sources: ghcm archive

Linking:

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