kendall_cor_test

<code>kendall_cor_test()</code> calculates p-value for the the
Kendall correlation using the exact values when the number of observations
is less than 50. For larger samples, it uses an approximation as in base R.

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 (2025) <doi:10.1371/journal.pone.0326090>.

Mauricio Vargas Sepulveda

kendallknight

Efficient Implementation of Kendall's Correlation Coefficient
Computation

Loader Catherine

Ross Ihaka

kendall_cor_test function

<dl><dt>x</dt>
<dd>a numeric vector.</dd>
<dt>y</dt>
<dd>a numeric vector.</dd>
<dt>alternative</dt>
<dd>a character string specifying the alternative hypothesis.
The possible values are <code>"two.sided"</code>, <code>"greater"</code>, and <code>"less"</code>.</dd>
<dt>conf.level</dt>
<dd>confidence level for the returned confidence interval.
Must be a single number between 0 and 1. Default is 0.95.</dd></dl>

Arguments

Kendall Correlation Test — kendall_cor_test

<dl>

<dt>x</dt>
<dd>a numeric vector.</dd>


<dt>y</dt>
<dd>a numeric vector.</dd>


<dt>alternative</dt>
<dd>a character string specifying the alternative hypothesis.
The possible values are <code>"two.sided"</code>, <code>"greater"</code>, and <code>"less"</code>.</dd>


<dt>conf.level</dt>
<dd>confidence level for the returned confidence interval.
Must be a single number between 0 and 1. Default is 0.95.</dd>

</dl>

kendall_cor_test: Kendall Correlation Test

Description

Usage

Value

Arguments

References

Examples