R has no native bayesian library like Pymc 3 (Must use stan which is c++). Also Python is better for ad hoc and agent based modeling and for out of core data with blaze and dask.
Unless you can help here, I see alot of pre coded models and older samplers, but nothing with a flexible JIT for user extensible variab;es and autodiff for newer HMC and NUTS type samplers. Exception being STAN, but that is its own C++ modeling language, can't talk to R functions , is more verbose than PYMC 3 and doesn't do discrete variables (unlike pymc3).
So no, R is not ahead in everything.