statsmodels.sandbox.regression.gmm.GMMResults class statsmodels.sandbox.regression.gmm.GMMResults(*args, **kwds) [source] just a storage class right now Methods calc_cov_params(moms, gradmoms[, weights, ...]) calculate covariance of parameter estimates compare_j(other) overidentification test for comparing two nested gmm estimates conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params(**kwds) f_test(r_matrix[, cov_p, scale, invcov]) Compute

statsmodels.sandbox.regression.gmm.GMM class statsmodels.sandbox.regression.gmm.GMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds) [source] Class for estimation by Generalized Method of Moments needs to be subclassed, where the subclass defined the moment conditions momcond Parameters: endog : array endogenous variable, see notes exog : array array of exogenous variables, see notes instrument : array array of instruments, see notes nmoms : None or int nu

statsmodels.sandbox.regression.anova_nistcertified.anova_oneway statsmodels.sandbox.regression.anova_nistcertified.anova_oneway(y, x, seq=0) [source]

statsmodels.sandbox.regression.anova_nistcertified.anova_ols statsmodels.sandbox.regression.anova_nistcertified.anova_ols(y, x) [source]

statsmodels.sandbox.distributions.transformed.Transf_gen class statsmodels.sandbox.distributions.transformed.Transf_gen(kls, func, funcinv, *args, **kwargs) [source] a class for non-linear monotonic transformation of a continuous random variable 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) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...]) Calcul

statsmodels.sandbox.distributions.transformed.TransfTwo_gen class statsmodels.sandbox.distributions.transformed.TransfTwo_gen(kls, func, funcinvplus, funcinvminus, derivplus, derivminus, *args, **kwargs) [source] Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s

statsmodels.sandbox.distributions.transformed.squaretg statsmodels.sandbox.distributions.transformed.squaretg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus, der

statsmodels.sandbox.distributions.transformed.squarenormalg statsmodels.sandbox.distributions.transformed.squarenormalg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, deri

statsmodels.sandbox.distributions.transformed.SquareFunc class statsmodels.sandbox.distributions.transformed.SquareFunc [source] class to hold quadratic function with inverse function and derivative using instance methods instead of class methods, if we want extension to parameterized function Methods derivminus(x) derivplus(x) inverseminus(x) inverseplus(x) squarefunc(x)

statsmodels.sandbox.distributions.transformed.negsquarenormalg statsmodels.sandbox.distributions.transformed.negsquarenormalg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus