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kendallknight  

Efficient Implementation of Kendall's Correlation Coefficient Computation
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Download and install kendallknight package within the R console
Install from CRAN:
install.packages("kendallknight")

Install from Github:
library("remotes")
install_github("cran/kendallknight")

Install by package version:
library("remotes")
install_version("kendallknight", "0.5.0")



Attach the package and use:
library("kendallknight")
Maintained by
Mauricio Vargas Sepulveda
[Scholar Profile | Author Map]
First Published: 2024-11-21
Latest Update: 2024-11-21
Description:
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>.
How to cite:
Mauricio Vargas Sepulveda (2024). kendallknight: Efficient Implementation of Kendall's Correlation Coefficient Computation. R package version 0.5.0, https://cran.r-project.org/web/packages/kendallknight. Accessed 19 Feb. 2025.
Previous versions and publish date:
0.4.0 (2024-11-21 18:10)
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