statsmodels.sandbox.distributions.extras.ACSkewT_gen.std ACSkewT_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: 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: std : float standard deviation of the distribution

statsmodels.sandbox.distributions.extras.SkewNorm_gen.cdf SkewNorm_gen.cdf(x, *args, **kwds) Cumulative distribution function 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: cdf : ndarray Cumulative distribution func

statsmodels.emplike.descriptive.DescStatUV.test_var DescStatUV.test_var(sig2_0, return_weights=False) [source] Returns -2 x log-likelihoog ratio and the p-value for the hypothesized variance Parameters: sig2_0 : float Hypothesized variance to be tested return_weights : bool If True, returns the weights that maximize the likelihood of observing sig2_0. Default is False Returns: test_results : tuple The log-likelihood ratio and the p_value of sig2_0 Examples >>> random_numbe

statsmodels.sandbox.regression.gmm.IVRegressionResults.predict IVRegressionResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can p

statsmodels.miscmodels.count.PoissonGMLE.initialize PoissonGMLE.initialize()

statsmodels.tsa.vector_ar.var_model.VARProcess.forecast VARProcess.forecast(y, steps) [source] Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y Parameters: y : ndarray (p x k) steps : int Returns: forecasts : ndarray (steps x neqs) Notes Lutkepohl pp 37-38

statsmodels.genmod.generalized_estimating_equations.GEEResults.summary GEEResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the GEE regression results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals co

statsmodels.discrete.discrete_model.DiscreteModel.from_formula classmethod DiscreteModel.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.DataF

statsmodels.sandbox.stats.multicomp.rankdata statsmodels.sandbox.stats.multicomp.rankdata(x) [source] rankdata, equivalent to scipy.stats.rankdata just a different implementation, I have not yet compared speed

statsmodels.iolib.table.csv2st statsmodels.iolib.table.csv2st(csvfile, headers=False, stubs=False, title=None) [source] Return SimpleTable instance, created from the data in csvfile, which is in comma separated values format. The first row may contain headers: set headers=True. The first column may contain stubs: set stubs=True. Can also supply headers and stubs as tuples of strings.