I've been using Mathematica (MMA) for my dissertation's data analysis, and for the most part it's been great. As an environment to manipulate data in, it's by far the best that I've used- once you get the hang of it, the pattern/transformation-rule language is incredibly useful for reformatting, recoding, mixing, slicing, dicing, etc. one's data. If you're coming from Haskell, you'll probably pick this part up way faster than I did at first.
If your stats needs are relatively simple- linear models, glms, logit models, anovas, simple tests of hypotheses, etc.- MMA is more than adequate. The new version looks like it adds some non-parametric stats functions, as well as paired t-tests, both of which would be quite useful to me.
Also, the visualization tool in MMA are fabulous, and don't make me want to tear my beard out every time I have to go off the beaten path (as opposed to those found in certain other one-letter-long stats environments I could name). 'Nuff said. Another thing I really appreciate about MMA is how consistent the syntax and functions are- once you've figured out one function, the odds are good that your knowledge will be useful on the next function you try and figure out. This, again, stands in stark contrast to other packages (R, SAS, I'm looking at you guys).
I have found myself turning to R for certain specific things, though. Mixed-effects models, repeated-measure ANOVA, Fisher's Exact Test, etc. Really, the two work together well- it's easy to use MMA to get your data in exactly the right form for R, export it, and then do whatever you need from there.
If your stats needs are relatively simple- linear models, glms, logit models, anovas, simple tests of hypotheses, etc.- MMA is more than adequate. The new version looks like it adds some non-parametric stats functions, as well as paired t-tests, both of which would be quite useful to me.
Also, the visualization tool in MMA are fabulous, and don't make me want to tear my beard out every time I have to go off the beaten path (as opposed to those found in certain other one-letter-long stats environments I could name). 'Nuff said. Another thing I really appreciate about MMA is how consistent the syntax and functions are- once you've figured out one function, the odds are good that your knowledge will be useful on the next function you try and figure out. This, again, stands in stark contrast to other packages (R, SAS, I'm looking at you guys).
I have found myself turning to R for certain specific things, though. Mixed-effects models, repeated-measure ANOVA, Fisher's Exact Test, etc. Really, the two work together well- it's easy to use MMA to get your data in exactly the right form for R, export it, and then do whatever you need from there.