statsmodels.miscmodels.tmodel.TLinearModel.initialize TLinearModel.initialize() [source]

statsmodels.discrete.discrete_model.CountResults.initialize CountResults.initialize(model, params, **kwd)

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.est_loc_scale TransfTwo_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.sandbox.distributions.transformed.Transf_gen.entropy Transf_gen.entropy(*args, **kwds) Differential entropy of the RV. 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).

Distributions This section collects various additional functions and methods for statistical distributions. Empirical Distributions ECDF(x[, side]) Return the Empirical CDF of an array as a step function. StepFunction(x, y[, ival, sorted, side]) A basic step function. Distribution Extras Skew Distributions SkewNorm_gen() univariate Skew-Normal distribution of Azzalini SkewNorm2_gen([momtype, a, b, xtol, ...]) univariate Skew-Normal distribution of Azzalini ACSkewT_gen() univariate Skew-

statsmodels.sandbox.regression.try_catdata.labelmeanfilter statsmodels.sandbox.regression.try_catdata.labelmeanfilter(y, x) [source]

statsmodels.discrete.discrete_model.MultinomialModel.pdf MultinomialModel.pdf(X) The probability density (mass) function of the model.

statsmodels.discrete.discrete_model.CountResults.llf static CountResults.llf()

statsmodels.stats.weightstats._tstat_generic statsmodels.stats.weightstats._tstat_generic(value1, value2, std_diff, dof, alternative, diff=0) [source] generic ttest to save typing

statsmodels.discrete.discrete_model.NegativeBinomialResults.normalized_cov_params NegativeBinomialResults.normalized_cov_params()