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>.
| Version: |
0.7.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats |
| LinkingTo: |
cpp11 |
| Suggests: |
knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Published: |
2025-05-16 |
| DOI: |
10.32614/CRAN.package.kendallknight |
| Author: |
Mauricio Vargas Sepulveda
 [aut, cre],
Loader Catherine [ctb] (original stirlerr implementations in C (2000)),
Ross Ihaka [ctb] (original chebyshev_eval, gammafn and lgammacor
implementations in C (1998)),
Statistics Canada [dtc] (manufactured goods dataset) |
| Maintainer: |
Mauricio Vargas Sepulveda <m.sepulveda at mail.utoronto.ca> |
| BugReports: |
https://github.com/pachadotdev/kendallknight/issues |
| License: |
Apache License (≥ 2) |
| URL: |
https://pacha.dev/kendallknight/,
https://github.com/pachadotdev/kendallknight |
| NeedsCompilation: |
yes |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
kendallknight results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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