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 libraryhttp://hackage.haskell.org/package/uvector-algorithmsto 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 Arithmetichttp://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