Why is having R a limitation? R is a fantastic data language which arguably beats all others in terms of number of libraries and data manipulation tools.
If GP comes from a more software-dev background, I can understand a general dislike for R. There's lots of things where R can't be beat right now, but for simple data analysis and visualization, there's some damn good python packages like pandas and d3py that GP may find easier to use than dataframes/tables and rCharts in R.
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.
So no, R is not ahead in everything.
I'm not trying to get into a R vs. Python argument, both have their strengths, but is it really true that R doesn't have anything like Pymc?
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).