With Pymc3, you can code a bayesian generalization of most of those packages pretty easily. For bayesian models, it is easier than calling into C++ with stan in

With Pymc3, you can code a bayesian generalization of most of those packages pretty easily. For bayesian models, it is easier than calling into C++ with stan in R. For everything else, one can call arbitrary R packages with Rpy2, albeit with clunkier syntax. For agent based modeling, there is no comparison. Python with its better and faster class system and Numba is in another league. Finally python has distributed and out of core lists, arrays and dataframes: http://dask.pydata.org/en/latest/ http://dask.pydata.org/en/latest/