[eigenspace]

The nuance is that for someone who mainly just calls functions from packages, they probably won’t notice any real speed difference since performance sensitive packages in python and R are typically written in C or C++. Additionally, there are various tools like Numba for accelerating Python code that will make certain restricted subsets of Python just as fast (or sometimes faster) than Julia.

However, as soon as you try to do something a bit more complicated then you’ll notice the speed and flexibility differences.

[disgruntledphd2]

I dunno about this. At work, we have an exhaustive model fitting procedure that takes a looooonnnng time.

I prototyped a quick julia implementation of a simple glm (almost identical code in Julia and R), and the julia code was approximately 10-20 times faster depending on the model.

This is definitely worth looking at (mind you, the costs of redevelopment of our code in Julia is probably prohibitive). That being said, this would encourage me to call out to julia from R for some of my more computationally heavy workloads.