ad

You might be interested in Edward Kmett's automatic differentiation[0] package, ad[1]. It is one of my favorite non-trivial blendings of computer science techniques and more traditional mathematics. In specific, it uses Haskell's type system to prevent subtle numerical errors which other automatic differentiation libraries require the user to prevent by being very careful. The resulting code is much more trustworthy. [0] http://en.wikipedia.org/wiki/Automatic_differentiation [1] https://github.com/ekmett/ad/

the answer is: it depends! Shoot me an email at Wellposed and I can try to better answer your question. I am quite literally building a full data analysis stack (as a product) in haskell, some parts of which will be available as a sort of proprietary augmented version of the haskell platform, and some parts are / will be open source. I do think that there are compelling reasons to consider Haskell / GHC for analytical workloads, but depending on the details it really depends. The principal cliff is just the HUGE number of (mostly poorly designed) libraries for many standard analyses written in R. Theres some nice engineering approaches to circumvent this, and theres some really exciting libs that a uniquely awesome and handy in haskell land. A notable example is AD, a really easy to use auto differentiation lib by Edward Kmett, which has