text: Title: Big performance difference between statsmodels MixedLM and R lmer
  Body: Hi,
  
  Using statsmodels version 0.14.0 I notice a big difference in performance when running the same model in R. Using statsmodels' `mixedlm`, the model takes 41 minutes to run while only 1-2 seconds using R's `lmer` package on the same machine. Why is there such a large difference in performance and is there anything I can do speed things up?
  
  I have tried different `method` arguments, but most of them have trouble converging.
  
  My dataset contains 125,066 groups (pairs), has 2 categorical variables and 1 numerical. I am comparing the following code:
  
  ```
  lmm_model = smf.mixedlm(f'value ~ C(cat_var1) + C(cat_var2) + numerical_var', data=pdf, groups=pdf['unit'])
  lmm_results = lmm_model.fit()
  
  print(lmm_results.summary())
  ```
  
  And in R:
  ```
  summary(lmer('value ~ cat_var1 + cat_var2 + numerical_var + (1|unit)', data=data_r))
  ```
  
  Thanks in advance!