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.stats.sandwich_covariance.cov_hc2 statsmodels.stats.sandwich_covariance.cov_hc2(results) [source] See statsmodels.RegressionResults

statsmodels.discrete.discrete_model.DiscreteModel.score DiscreteModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.sandbox.regression.gmm.IVGMMResults.fittedvalues static IVGMMResults.fittedvalues() [source]

statsmodels.stats.moment_helpers.mnc2mvsk statsmodels.stats.moment_helpers.mnc2mvsk(args) [source] convert central moments to mean, variance, skew, kurtosis

statsmodels.discrete.discrete_model.NegativeBinomialResults.llr_pvalue static NegativeBinomialResults.llr_pvalue()

statsmodels.discrete.discrete_model.DiscreteResults.llr_pvalue static DiscreteResults.llr_pvalue() [source]

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.iolib.table.SimpleTable.pad SimpleTable.pad(s, width, align) [source] DEPRECATED: just use the pad function

statsmodels.discrete.discrete_model.DiscreteModel.pdf DiscreteModel.pdf(X) [source] The probability density (mass) function of the model.