For huge datasets, Python has distributed and out of core data structures: http://dask.pydata.org/en/latest/ https://github.com/ContinuumIO/blaze

This is pretty unique, and works better than spark for out of core on one machine...(and easier to set up.)

For stats not in the statsmodels and scikitlearn packages, you can easily whip up the bayesian generalization in pymc3.

Then if there is another R package you need, you can use Rpy2 to call it.

Not sure if this would be relevant to your usecase.