text: Title: Parameter standard errors for ridge regression and logistic regression with ridge penalty
  Body: #### Is your feature request related to a problem? Please describe
  
  I would like to get standard error estimates when using OLS or GLM with a L2 penalty.
  
  #### Describe the solution you'd like
  
  for OLS, there is a well-known expression that accounts for the ridge penalty
  
  https://online.stat.psu.edu/stat857/node/155/
  
  for GLM, it is even simpler as we should just add $\lambda$ to the hessian before inverting it
  
  I'm not sure however how to interface this with the rest of the code as `.fit` and `.fit_regularized` don't quite work the same.