Package: kendallknight 0.6.0

Mauricio Vargas Sepulveda

kendallknight: Efficient Implementation of Kendall's Correlation Coefficient Computation

The computational complexity of the implemented algorithm for Kendall's correlation is O(n log(n)), which is faster than the base R implementation with a computational complexity of O(n^2). For small vectors (i.e., less than 100 observations), the time difference is negligible. However, for larger vectors, the speed difference can be substantial and the numerical difference is minimal. The references are Knight (1966) <doi:10.2307/2282833>, Abrevaya (1999) <doi:10.1016/S0165-1765(98)00255-9>, Christensen (2005) <doi:10.1007/BF02736122> and Emara (2024) <https://learningcpp.org/>. This implementation is described in Vargas Sepulveda (2024) <doi:10.48550/arXiv.2408.09618>.

Authors:Mauricio Vargas Sepulveda [aut, cre], Loader Catherine [ctb], Ross Ihaka [ctb]

kendallknight_0.6.0.tar.gz
kendallknight_0.6.0.tar.gz(r-4.5-noble)kendallknight_0.6.0.tar.gz(r-4.4-noble)
kendallknight_0.6.0.tgz(r-4.4-emscripten)kendallknight_0.6.0.tgz(r-4.3-emscripten)
kendallknight.pdf |kendallknight.html✨
kendallknight/json (API)
NEWS

# Install 'kendallknight' in R:
install.packages('kendallknight', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/pachadotdev/kendallknight/issues0 issues

Pkgdown site:https://pacha.dev

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • cigarettes - Life expectancy and cigarettes per day

On CRAN:

Conda:

cppopenmp

3.18 score 291 downloads 2 exports 1 dependencies

Last updated 1 months agofrom:f3878eb0a2. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 23 2025
R-4.5-linux-x86_64OKMar 23 2025
R-4.4-linux-x86_64OKMar 23 2025

Exports:kendall_corkendall_cor_test

Dependencies:cpp11

Basic 'kendallknight' usage

Rendered fromusage.Rmdusingknitr::rmarkdownon Mar 23 2025.

Last update: 2025-02-20
Started: 2024-11-21

Citation

To cite package ‘kendallknight’ in publications use:

Vargas Sepulveda M (2025). kendallknight: Efficient Implementation of Kendall's Correlation Coefficient Computation. R package version 0.6.0, https://CRAN.R-project.org/package=kendallknight.

Corresponding BibTeX entry: 

  @Manual{,
    title = {kendallknight: Efficient Implementation of Kendall's
      Correlation Coefficient Computation},
    author = {Mauricio {Vargas Sepulveda}},
    year = {2025},
    note = {R package version 0.6.0},
    url = {https://CRAN.R-project.org/package=kendallknight},
  }

Readme and manuals

kendallknight

About

tldr; This package implements a different algorithm from the one implemented in base R, and it reduces the complexity of the Kendall’s correlation coefficient from O(n^2) to O(n log n) resulting in a runtime of nano seconds or minutes instead of minutes or hours. This package is written in C++ and uses cpp11 to export the functions to R. See the vignette for the mathematical details.

If this software is useful to you, please consider donating on Buy Me A Coffee. All donations will be used to continue improving kendallknight.

Installation

You can install the released version of kendallknight from CRAN with:

install.packages("kendallknight")

You can install the development version of kendallknight like so:

remotes::install_github("pachadotdev/kendallknight")

Examples

See the documentation and vignette: https://pacha.dev/kendallknight/.

Code of Conduct

Please note that the kendallknight project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.