title: Transformed beta regression in python
body: <p>Has anyone tried implementing beta transformed regression python? It is used to model values that lie between 0 and 1 and has a distribution with heteroskedasticity ineherently present. Essentially you first transform the dependent variable to a beta distribution and apply a second transformation or a link function such as logit or probit to restrict its values bw 0 and 1. The link below explains in detail the code for R:
  <a href="https://cran.r-project.org/web/packages/betareg/vignettes/betareg.pdf" rel="noreferrer">https://cran.r-project.org/web/packages/betareg/vignettes/betareg.pdf</a></p>
  
  <p>However, I'm having a hard time finding functions/libraries to do this in python. Even if i try using brute force method, once we have the predicted transformed Ys, transforming it back to original Ys is challenging. Moreover I don't want to go the brute force route and was wondering if there is a sophisticated way to implement this in python. Thanks a lot!</p>