statsmodels.genmod.families.links.cauchy.inverse cauchy.inverse(z) The inverse of the CDF link Parameters: z : array-like The value of the inverse of the link function at p Returns: p : array Mean probabilities. The value of the inverse of CDF link of z Notes g^(-1)(z) = dbn.cdf(z)

statsmodels.genmod.families.links.CLogLog.inverse_deriv CLogLog.inverse_deriv(z) [source] Derivative of the inverse of the C-Log-Log transform link function Parameters: z : array-like The value of the inverse of the CLogLog link function at p Returns: The derivative of the inverse of the CLogLog link function :

statsmodels.genmod.families.links.identity.deriv identity.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.regression.mixed_linear_model.MixedLM.hessian_sqrt MixedLM.hessian_sqrt(params) [source] Returns the Hessian matrix of the log-likelihood evaluated at a given point, calculated with respect to the parameterization in which the random effects covariance matrix is represented through its Cholesky square root. Parameters: params : MixedLMParams or array-like The model parameters. If array-like, must contain packed parameters that are compatible with this model. Returns: The Hes

statsmodels.discrete.discrete_model.CountResults.save CountResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove

statsmodels.iolib.foreign.StataReader.dataset StataReader.dataset(as_dict=False) [source] Returns a Python generator object for iterating over the dataset. Parameters: as_dict : bool, optional If as_dict is True, yield each row of observations as a dict. If False, yields each row of observations as a list. Returns: Generator object for iterating over the dataset. Yields each row of : observations as a list by default. : Notes If missing_values is True during instantiation of StataReade

statsmodels.sandbox.regression.gmm.IVGMM.fitgmm_cu IVGMM.fitgmm_cu(start, optim_method='bfgs', optim_args=None) estimate parameters using continuously updating GMM Parameters: start : array_like starting values for minimization Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin

statsmodels.regression.quantile_regression.QuantReg.whiten QuantReg.whiten(data) [source] QuantReg model whitener does nothing: returns data.

statsmodels.discrete.discrete_model.MNLogit.from_formula classmethod MNLogit.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :