[Haskell-cafe] [ANNOUNCE] A statistics library

[Bryan O'Sullivan]

I'm pleased to announce a new Haskell statistics library, imaginatively
named statistics <http://hackage.haskell.org/package/statistics>:
http://hackage.haskell.org/package/statistics

   - Support for common discrete and continuous probability distributions
   (binomial, gamma, exponential, geometric, hypergeometric, normal, and
   Poisson)
   - Kernel density estimation
   - Autocorrelation analysis
   - Functions over sample data
   - Quantile estimation
   - Resampling techniques: jackknife and bootstrap estimation

The statistics library certainly isn't yet comprehensive, but it has some
features that I think make it very attractive as a base for further work:

   - It's very fast, building on some of the fantastic software that's
   available on Hackage these days. I make heavy use of Don Stewart's uvector
   library <http://hackage.haskell.org/package/uvector> (itself a port of
   Roman Leshchinskiy's vector library), which means that many functions
   allocate no memory and execute tight loops using only machine registers. I
   use Dan Doel's uvector-algorithms
library<http://hackage.haskell.org/package/uvector-algorithms>to
perform fast partial sorts. I also use Don's mersenne-random
   library <http://hackage.haskell.org/package/mersenne-random> for fast
   random number generation when doing bootstrap analysis.
   - I've put a fair amount of effort into finding and using algorithms that
   are numerically stable (trying to avoid problems like catastrophic
   cancellation). Whenever possible, I indicate which methods are used in the
   documentation. (For more information on numerical stability, see What
   Every Scientist Should Know About Floating-Point
Arithmetic<http://docs.sun.com/app/docs/doc/800-7895>
   ).

If you want to contribute, please get the source code and hack away:

darcs get http://darcs.serpentine.com/statistics

For more details, see http://bit.ly/ykOeK
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