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:Please use the canonical form https://CRAN.R-project.org/package=kendallknight to link to this page.
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