Hbimod

from hbimod import HierarchicalBayesModel model = HierarchicalBayesModel( formula="y ~ x + (1 + x | group)", data=df, chains=4, iter=2000 ) results = model.fit() If hbimod is a real internal module, it likely includes:

Would you like a deeper exploration of the statistical theory behind Lewbel’s method or hierarchical Bayes, to help you build or reverse-engineer hbimod further? hbimod

# hbimod.py import numpy as np import statsmodels.api as sm class HBIMod: def (self, endog, exog, instrument=None): self.endog = endog self.exog = exog self.instrument = instrument hbimod