statsmodels.genmod.families.links.Power.deriv2 Power.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.miscmodels.count.PoissonZiGMLE.score_obs PoissonZiGMLE.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.miscmodels.count.PoissonZiGMLE.score PoissonZiGMLE.score(params) Gradient of log-likelihood evaluated at params

statsmodels.miscmodels.count.PoissonZiGMLE.nloglikeobs PoissonZiGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.miscmodels.count.PoissonZiGMLE.predict PoissonZiGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.miscmodels.count.PoissonZiGMLE.nloglike PoissonZiGMLE.nloglike(params)

statsmodels.miscmodels.count.PoissonZiGMLE.reduceparams PoissonZiGMLE.reduceparams(params)

statsmodels.miscmodels.count.PoissonZiGMLE.loglikeobs PoissonZiGMLE.loglikeobs(params)

statsmodels.miscmodels.count.PoissonZiGMLE.loglike PoissonZiGMLE.loglike(params)

statsmodels.miscmodels.count.PoissonZiGMLE.information PoissonZiGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.