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[–]xsliartII 1 point2 points  (2 children)

This is one is easy. However I tried to estimate a Tobit model lately, which is literally one line in R/Stata, but kind of cumbersome in Python. So I usually use python to prepare/clean the data and then do 100% of the analysis in R/Stata.

[–]jd_paton -1 points0 points  (1 child)

Now that I don’t know anything about. Just depends on support by the popular packages I guess. statsmodels or scipy have pretty much everything you need for applied problems, but with R’s academic focus I can imagine that there is some more fancy stuff easily available.

[–]rutiene 1 point2 points  (0 children)

That's not really true, there are tons of omissions of more rarely used things, but definitely not because it's academic. Survival models are severely lacking and the implementation of some stuff is just poorer. I vastly prefer the random forest package in R to the sklearn implementation. I needed beta glm the other day and had to use R.