statsmodels.stats.moment_helpers.mnc2mc statsmodels.stats.moment_helpers.mnc2mc(mnc, wmean=True) [source] convert non-central to central moments, uses recursive formula optionally adjusts first moment to return mean

statsmodels.sandbox.regression.gmm.GMMResults.f_test GMMResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be

statsmodels.discrete.discrete_model.ProbitResults.f_test ProbitResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test

statsmodels.tsa.vector_ar.var_model.VAR.initialize VAR.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.miscmodels.tmodel.TLinearModel.score TLinearModel.score(params) Gradient of log-likelihood evaluated at params

statsmodels.discrete.discrete_model.Logit.cov_params_func_l1 Logit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.stats.proportion.proportions_chisquare statsmodels.stats.proportion.proportions_chisquare(count, nobs, value=None) [source] test for proportions based on chisquare test Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. value : None or float or

statsmodels.regression.mixed_linear_model.MixedLMResults.initialize MixedLMResults.initialize(model, params, **kwd)

statsmodels.sandbox.distributions.transformed.invdnormalg statsmodels.sandbox.distributions.transformed.invdnormalg = a class for non-linear monotonic transformation of a continuous random variable