Yes, that's a good point on the plots. I'll try to make them more prominent in future docs. These are some things I've included in the library which aren't im

Yes, that's a good point on the plots. I'll try to make them more prominent in future docs. These are some things I've included in the library which aren't implemented in Statsmodels: - Breusch-Pagan studentized test of heteroskedasticity (available in R) - Standardized / beta coefficients (still an open feature request in https://github.com/statsmodels/statsmodels/issues/3857 https://github.com/statsmodels/statsmodels/issues/3857 ) - Leverage vs residuals squared plot (there's an influence plot but not something similar to Stata's lvr2plot) Even the most common metric I use for assessing models – root MSE – isn't stored in the Statsmodels object summary. To assess an OLS model in Statsmodels I'd find I do so much repetitive code, yet in Stata the commands are fairly succinct. Other things I also added to make encoding of variables easier: - InteractionEncoder - DummyEncoder (to cover different ways of treating missing values) The more I thought about these missing features, the more I thought they can be wrapped up in a more coherent way. :-)