statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fitgmm_cu NonlinearIVGMM.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.stats.sandwich_covariance.cov_hc2 statsmodels.stats.sandwich_covariance.cov_hc2(results) [source] See statsmodels.RegressionResults

statsmodels.sandbox.stats.multicomp.multipletests statsmodels.sandbox.stats.multicomp.multipletests(pvals, alpha=0.05, method='hs', is_sorted=False, returnsorted=False) test results and p-value correction for multiple tests Parameters: pvals : array_like uncorrected p-values alpha : float FWER, family-wise error rate, e.g. 0.1 method : string Method used for testing and adjustment of pvalues. Can be either the full name or initial letters. Available methods are `bonferroni` : one-step

statsmodels.sandbox.stats.multicomp.GroupsStats.groupvarwithin GroupsStats.groupvarwithin() [source]

statsmodels.iolib.table.SimpleTable.get_colwidths SimpleTable.get_colwidths(output_format, **fmt_dict) [source] Return list, the widths of each column.

statsmodels.duration.hazard_regression.PHRegResults.baseline_cumulative_hazard_function static PHRegResults.baseline_cumulative_hazard_function() [source] A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function.

statsmodels.duration.hazard_regression.PHReg.initialize PHReg.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.discrete.discrete_model.NegativeBinomial.information NegativeBinomial.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.regression.gmm.IVGMMResults.pvalues static IVGMMResults.pvalues()

statsmodels.tsa.varma_process.VarmaPoly.vstack VarmaPoly.vstack(a=None, name='ar') [source] stack lagpolynomial vertically in 2d array