API reference# Base classes and utilities Base classes ModelBase ModelBase.n_params ModelBase.mean ModelBase.cov ModelBase.X ModelBase.endog_names ModelBase.exog_names ModelBase.seed ModelBase.fit() ModelBase.from_csv() ModelBase.from_results() ModelBase.get_index() ModelBase.get_indices() ModelBase.to_csv() ResultsBase ResultsBase.conf_int() ResultsBase.point_plot() ResultsBase.save() ResultsBase.summary() Distributions joint_distribution joint_distribution.logpdf() joint_distribution.pdf() joint_distribution.rvs() mixture mixture.distributions mixture.weights mixture.mean() mixture.std() mixture.var() quantile_unbiased quantile_unbiased.y quantile_unbiased.bounds quantile_unbiased.dx quantile_unbiased.truncnorm_kwargs quantile_unbiased.ppf() truncnorm truncnorm.loc truncnorm.scale truncnorm.lower_bound truncnorm.upper_bound truncnorm.interval_masses truncnorm.n_samples Utilities expected_wasserstein_distance() holm_bonferroni_correction() weighted_quantile() Confidence sets and ranking Simultaneous confidence sets AverageComparison BaselineComparison ConfidenceSet ConfidenceSetResults MarginalRanking MarginalRankingResults PairwiseComparison PairwiseComparisonResults SimultaneousRanking SimultaneousRankingResults Conditional inference Ranking conditions RankCondition RankConditionResults Significance conditions SignificanceCondition SignificanceConditionResults Bayesian inference Base classes BayesBase BayesResults Improper prior Improper Normal prior Normal NormalResults compute_robust_critical_value() Nonparametric prior Nonparametric NonparametricResults