Package: ghcm 3.0.1

Anton Rask Lundborg

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>.

Authors:Anton Rask Lundborg [aut, cre], Rajen D. Shah [aut], Jonas Peters [aut]

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ghcm.pdf |ghcm.html✨
ghcm/json (API)

# Install 'ghcm' in R:
install.packages('ghcm', repos = c('https://arlundborg.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/arlundborg/ghcm/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

3.70 score 5 scripts 209 downloads 1 exports 2 dependencies

Last updated 1 years agofrom:efda811e66. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-win-x86_64OKNov 07 2024
R-4.5-linux-x86_64OKNov 07 2024
R-4.4-win-x86_64OKNov 07 2024
R-4.4-mac-x86_64OKNov 07 2024
R-4.4-mac-aarch64OKNov 07 2024
R-4.3-win-x86_64OKNov 07 2024
R-4.3-mac-x86_64OKNov 07 2024
R-4.3-mac-aarch64OKNov 07 2024

Exports:ghcm_test

Dependencies:CompQuadFormRcpp

Getting started with ghcm

Rendered fromghcm.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2023-11-02
Started: 2020-07-11