Sorry for the late reply.  I've been extremely busy, out of town, etc.
lately.  I wrote the core algorithm for this function back in 2010, but did
not make any attempt to integrate it into R-base, for several reasons:

1.  I was having difficulty figuring out what all the missing value options
(which I never use) are supposed to do and how to efficiently adapt my code
to them.

2.  The cor() function (IIRC; this is from memory from over a year ago)
implements Pearson, Spearman and Kendall very messily all in one function.
I felt that this could use some refactoring to make the integration of a
new Kendall algorithm sane, but didn't know the codebase well enough to do
it myself w/o a significant learning curve.

3.  Testing the integration would require setting up a build environment
for R.

Basically, I wanted to contribute this algorithm but didn't want to go
through the necessary learning curve to become a regular R-base
contributor.  I was hoping the integration work would be trivial to someone
who contributes to this codebase regularly.

On Tue, Jun 26, 2012 at 5:44 PM, Duncan Murdoch <murdoch.duncan@gmail.com>wrote:

> On 12-06-25 2:48 PM, Adler, Avraham wrote:
>
>> Hello.
>>
>> Has any further action been taken regarding implementing David Simcha's
>> fast Kendall tau code (now found in the package pcaPP as cor.fk) into
>> R-base? It is literally hundreds of times faster, although I am uncertain
>> as to whether he wrote code for testing the significance of the parameter.
>> The last mention I have seen of this was in 2010<https://stat.ethz.ch/**
>> pipermail/r-devel/2010-**February/056745.html<https://stat.ethz.ch/pipermail/r-devel/2010-February/056745.html>
>> >.
>>
>
> You could check the NEWS file, but I don't remember anything being done
> along these lines.  If the code is in a CRAN package, there doesn't seem to
> be any need to move it to base R.
>
> Duncan Murdoch
>

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