Alternative to Numerical Recipes

On Mon, 13 Nov 2000, Kim Horn wrote:

We have had problems with licensing with Numerical Recipes.
We wanted to use the code in our product but never got a
reply from them about licensing. So we built our own.
It is a pity that such a resource cannot be used :-)

The original algorithms are mostly published in Appl. Statistics
journal. You can down load the Fortran code from StatLib web
site and then use F2C to translate to C code.

R uses some Appl Statistics algorithms.  They too have conditions, and we
obtained permission to include code (even translated code) in R.

From the Statlib site:

   The Royal Statistical Society holds the copyright to these routines, but
   has given its permission for their distribution provided that no fee is
   charged.

R itself is a source of routines for this sort of thing, we
believe with documented sources, and of course with strong
usage conditions (GPL).

The ones I have used are:

Algorithm As 109  Appl. Statist. (1977) Vol.26, P.111

Algorithm As 63  appl. statist. (1973), vol.22, no.3

Natural Log of the Complete Gamma Function
Lanczos, C. 'A precision approximation of the gamma
function', J. SIAM Numer. Anal., B, 1, 86-96, 1964.

All the best
Kim

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Kim Horn
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Clive Jenkins wrote:

Some time ago I mentioned the online version of the book
"Numerical Recipes in C"
http://www.ulib.org/webRoot/Books/Numerical_Recipes/
and inadvertantly stirred up a hornets' nest.
I obtained from this book code to calculate the Incomplete beta
function. It appears in:
6.4 Incomplete Beta Function, Student?s Distribution, F-Distribution,
Cumulative Binomial Distribution, p.226,
and it calls the log-gamma function that appears in:
6.1 Gamma, Beta, and Related Functions, p.213.

I wish to attribute this material to its source, but would prefer to
cite a more "respectable" source than Numerical Recipes. Can anyone
suggest the original source, or a source of something very similar, or a
better place to ask the question?

Thanks,
Clive Jenkins

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--
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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