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 (2022) <doi:10.1111/rssb.12544>.
| Version: |
3.0.1 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
CompQuadForm, Rcpp, splines |
| LinkingTo: |
Rcpp |
| Suggests: |
graphics, stats, utils, refund, testthat, knitr, rmarkdown, bookdown, ggplot2, reshape2, dplyr, tidyr |
| Published: |
2023-11-02 |
| DOI: |
10.32614/CRAN.package.ghcm |
| Author: |
Anton Rask Lundborg [aut, cre],
Rajen D. Shah [aut],
Jonas Peters [aut] |
| Maintainer: |
Anton Rask Lundborg <arl at math.ku.dk> |
| 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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=ghcm
to link to this page.