statsmodels.sandbox.distributions.extras.ACSkewT_gen.logcdf ACSkewT_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: logcdf : array_like Lo

statsmodels.iolib.table.SimpleTable.sort SimpleTable.sort() L.sort(cmp=None, key=None, reverse=False) ? stable sort IN PLACE; cmp(x, y) -> -1, 0, 1

statsmodels.genmod.cov_struct.CovStruct.summary CovStruct.summary() [source] Returns a text summary of the current estimate of the dependence structure.

statsmodels.robust.robust_linear_model.RLMResults class statsmodels.robust.robust_linear_model.RLMResults(model, params, normalized_cov_params, scale) [source] Class to contain RLM results Returns: **Attributes** : bcov_scaled : array p x p scaled covariance matrix specified in the model fit method. The default is H1. H1 is defined as k**2 * (1/df_resid*sum(M.psi(sresid)**2)*scale**2)/ ((1/nobs*sum(M.psi_deriv(sresid)))**2) * (X.T X)^(-1) where k = 1 + (df_model +1)/nobs * var_psiprime/m**

statsmodels.sandbox.distributions.extras.SkewNorm2_gen class statsmodels.sandbox.distributions.extras.SkewNorm2_gen(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None) [source] univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds)

statsmodels.regression.linear_model.OLSResults.scale static OLSResults.scale()

statsmodels.sandbox.regression.gmm.IVRegressionResults.HC3_se static IVRegressionResults.HC3_se() See statsmodels.RegressionResults

statsmodels.regression.linear_model.OLSResults.bic static OLSResults.bic()

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

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.get_error NonlinearIVGMM.get_error(params)