> The main problem I've found so far is that python is quite bad with advanced statistical analysis, like producing regressions with the right standard errors
Can you tell me an example? Scipy, NumPy and statsmodels etc. pretty much handle these.
"Google Summer of Code 2013: We have had two students accepted to work on statsmodels as part of the Google Summer of Code 2013. The first project will focus on improving the discrete choice models, adding, for example, Conditional Logit, Nested Logit, and Mixed Logit models. The second project will focus on time series analysis, including regime-switching models such as SETAR, STAR, and Markov Switching models."
These are basics that I would expect any decent statistical language to have. I can see potential, but not for many years do I see the kind of support that I'm looking for in python.
Hardcore python user here. It sounds like you haven't used R. The OP is a social scientist and researcher. These things are vastly easier and more comprehensively implemented and documented in R. (upvoted you accidentally in the android app...) statsmodels is WIP and has an emphasis on econometrics.
Especially with more cutting edge and Not Machine Learning based statistics, R has packages where Python does not. I've found SciPy/NumPy and statsmodels to be an adequate "What if I need a GLM in my Python code?" solution, but less of a great solution to heavy stats work.
Can you tell me an example? Scipy, NumPy and statsmodels etc. pretty much handle these.