def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', p=2, missing='none', **kwargs):
    super(ZeroInflatedNegativeBinomialP, self).__init__(endog, exog, offset=offset, inflation=inflation, exog_infl=exog_infl, exposure=exposure, missing=missing, **kwargs)
    self.model_main = NegativeBinomialP(self.endog, self.exog, offset=offset, exposure=exposure, p=p)
    self.distribution = zinegbin
    self.k_exog += 1
    self.k_extra += 1
    self.exog_names.append('alpha')
    self.result_class = ZeroInflatedNegativeBinomialResults
    self.result_class_wrapper = ZeroInflatedNegativeBinomialResultsWrapper
    self.result_class_reg = L1ZeroInflatedNegativeBinomialResults
    self.result_class_reg_wrapper = L1ZeroInflatedNegativeBinomialResultsWrapper
def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', p=2, missing='none', **kwargs):
    super(ZeroInflatedNegativeBinomialP, self).__init__(endog, exog, offset=offset, inflation=inflation, exog_infl=exog_infl, exposure=exposure, missing=missing, **kwargs)
    self.model_main = NegativeBinomialP(self.endog, self.exog, offset=offset, exposure=exposure, p=p)
    self.distribution = zinegbin
    self.k_exog += 1
    self.k_extra += 1
    self.exog_names.append('alpha')
    self.result_class = ZeroInflatedNegativeBinomialResults
    self.result_class_wrapper = ZeroInflatedNegativeBinomialResultsWrapper
    self.result_class_reg = L1ZeroInflatedNegativeBinomialResults
    self.result_class_reg_wrapper = L1ZeroInflatedNegativeBinomialResultsWrapper