Kendallknight: An R package for efficient implementation of Kendall’s correlation coefficient computation

The kendallknight package introduces an efficient implementation of Kendall's correlation coefficient computation, significantly improving the processing time for large datasets without sacrificing accuracy. The kendallknight package, following Knight (1966) and posterior literature, reduces the time complexity resulting in drastic reductions in computation time, transforming operations that would take minutes or hours into milliseconds or minutes, while maintaining precision and correctly handling edge cases and errors. The package is particularly advantageous in econometric and statistical contexts where rapid and accurate calculation of Kendall's correlation coefficient is desirable. Benchmarks demonstrate substantial performance gains over the Base R implementation, especially for large datasets.

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Publication Details

Journal
PLoS ONE
Published
2025-06-18
DOI
https://doi.org/10.1371/journal.pone.0326090
Citations
1
Primary Topic
Advanced Statistical Methods and Models
Type
article
Field-Weighted Citation Impact
2.63
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article

Kendallknight: An R package for efficient implementation of Kendall’s correlation coefficient computation

Mauricio Vargas
1 citations
PLoS ONE
Advanced Statistical Methods and Models
2.63
article

Kendallknight: An R package for efficient implementation of Kendall’s correlation coefficient computation

Mauricio Vargas
article en
1 citations

Abstract

The kendallknight package introduces an efficient implementation of Kendall's correlation coefficient computation, significantly improving the processing time for large datasets without sacrificing accuracy. The kendallknight package, following Knight (1966) and posterior literature, reduces the time complexity resulting in drastic reductions in computation time, transforming operations that would take minutes or hours into milliseconds or minutes, while maintaining precision and correctly handling edge cases and errors. The package is particularly advantageous in econometric and statistical contexts where rapid and accurate calculation of Kendall's correlation coefficient is desirable. Benchmarks demonstrate substantial performance gains over the Base R implementation, especially for large datasets.

PLoS ONEVol. 20(6)
University of Toronto (CA)
Openalex Percentile: Top 11%
Advanced Statistical Methods and Models
2.63
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