""" Statsmodels Wrapper for Fincept Terminal Comprehensive wrapper for statsmodels library covering: - Regression models (OLS, WLS, GLS, GLSAR, RecursiveLS) - Time series models (ARIMA, SARIMAX, VARMAX, AutoReg, etc.) - Time series functions (ACF, PACF, ADF, KPSS, cointegration tests) - GLM models (Logit, Probit, Poisson, NegativeBinomial, QuantReg, GEE) - Statistical tests (t-tests, ANOVA, proportions, diagnostics) - Power analysis (t-test, F-test, proportions, ANOVA) - Multivariate analysis (PCA, Factor Analysis, MANOVA, Canonical Correlation) - Robust regression (RLM, MAD, Huber scale) - Survival analysis (Cox PH, Kaplan-Meier, Log-rank test) - Nonparametric methods (KDE, Kernel Regression, LOWESS) - GAM (Generalized Additive Models) """ # Import stats_extended module (100+ additional statistical functions) from . import stats_extended __all__ = [ # Regression 'fit_ols', 'fit_wls', 'fit_gls', 'fit_glsar', 'fit_recursive_ls', 'predict_ols', 'regression_diagnostics', 'fit_rolling_ols', 'fit_rolling_wls', # Time series models 'fit_arima', 'forecast_arima', 'fit_sarimax', 'fit_varmax', 'fit_autoreg', 'fit_ardl', 'fit_exponential_smoothing', 'stl_decompose', 'fit_dynamic_factor', 'fit_unobserved_components', 'fit_markov_regression', 'fit_vecm', 'fit_holt', 'fit_simple_exp_smoothing', 'fit_markov_autoregression', 'stl_forecast', # Time series functions 'calculate_acf', 'calculate_pacf', 'calculate_ccf', 'adf_test', 'kpss_test', 'coint_test', 'bds_test', 'q_stat', 'acorr_ljungbox', 'seasonal_decompose_additive', 'arma_order_select', 'detrend_linear', 'add_trend_to_data', # GLM models 'fit_glm', 'fit_logit', 'predict_logit', 'fit_probit', 'fit_poisson', 'fit_negative_binomial', 'fit_mnlogit', 'fit_quantreg', 'fit_gee', 'fit_zero_inflated_poisson', 'fit_conditional_logit', 'fit_conditional_poisson', 'fit_generalized_poisson', 'fit_zero_inflated_generalized_poisson', # Statistical tests 'ttest_ind', 'ttest_1samp', 'ztest', 'anova_lm', 'proportions_ztest', 'proportion_confint', 'chisquare_test', 'jarque_bera_test', 'omnibus_normtest', 'durbin_watson_test', 'het_breuschpagan', 'het_white', 'het_arch', 'acorr_breusch_godfrey', 'linear_reset', 'linear_harvey_collier', 'multipletests_correction', 'fdrcorrection_twostage', 'compare_means', 'descr_stats', # Power analysis 'ttest_power', 'ftest_power', 'proportion_power', 'calculate_effect_size_cohens_d', 'calculate_effect_size_proportion', 'sample_size_proportion_confint', 'chisquare_power', 'anova_power', # Multivariate 'perform_pca', 'perform_factor_analysis', 'perform_manova', 'canonical_correlation', # Robust 'fit_rlm', 'mad', 'huber_scale', 'huber_location_scale', # Survival 'fit_cox_ph', 'survdiff', 'kaplan_meier', # Nonparametric 'kernel_density_estimation', 'kernel_regression', 'lowess_smoothing', 'multivariate_kde', # GAM 'fit_glm_gam', 'fit_gam_formula', # Extended stats module (100+ functions - access via stats_extended.function_name) 'stats_extended' ] # ── Lazy attribute resolution (PEP 562) ───────────────────────────────────── # Submodules below have an `if __name__ == "__main__":` block and may be # invoked via `python -m`. Eagerly importing them here would put each in # sys.modules before Python re-executes them as __main__, triggering a # RuntimeWarning ("found in sys.modules ... prior to execution"). The lazy # loader keeps the public API intact while deferring import to first access. _LAZY_ATTRS: dict[str, tuple[str, str]] = { "fit_ols": ("regression", "fit_ols"), "fit_wls": ("regression", "fit_wls"), "fit_gls": ("regression", "fit_gls"), "fit_glsar": ("regression", "fit_glsar"), "fit_recursive_ls": ("regression", "fit_recursive_ls"), "predict_ols": ("regression", "predict_ols"), "regression_diagnostics": ("regression", "regression_diagnostics"), "fit_rolling_ols": ("regression", "fit_rolling_ols"), "fit_rolling_wls": ("regression", "fit_rolling_wls"), "fit_arima": ("timeseries_models", "fit_arima"), "forecast_arima": ("timeseries_models", "forecast_arima"), "fit_sarimax": ("timeseries_models", "fit_sarimax"), "fit_varmax": ("timeseries_models", "fit_varmax"), "fit_autoreg": ("timeseries_models", "fit_autoreg"), "fit_ardl": ("timeseries_models", "fit_ardl"), "fit_exponential_smoothing": ("timeseries_models", "fit_exponential_smoothing"), "stl_decompose": ("timeseries_models", "stl_decompose"), "fit_dynamic_factor": ("timeseries_models", "fit_dynamic_factor"), "fit_unobserved_components": ("timeseries_models", "fit_unobserved_components"), "fit_markov_regression": ("timeseries_models", "fit_markov_regression"), "fit_vecm": ("timeseries_models", "fit_vecm"), "fit_holt": ("timeseries_models", "fit_holt"), "fit_simple_exp_smoothing": ("timeseries_models", "fit_simple_exp_smoothing"), "fit_markov_autoregression": ("timeseries_models", "fit_markov_autoregression"), "stl_forecast": ("timeseries_models", "stl_forecast"), "calculate_acf": ("timeseries_funcs", "calculate_acf"), "calculate_pacf": ("timeseries_funcs", "calculate_pacf"), "calculate_ccf": ("timeseries_funcs", "calculate_ccf"), "adf_test": ("timeseries_funcs", "adf_test"), "kpss_test": ("timeseries_funcs", "kpss_test"), "coint_test": ("timeseries_funcs", "coint_test"), "bds_test": ("timeseries_funcs", "bds_test"), "q_stat": ("timeseries_funcs", "q_stat"), "acorr_ljungbox": ("timeseries_funcs", "acorr_ljungbox"), "seasonal_decompose_additive": ("timeseries_funcs", "seasonal_decompose_additive"), "arma_order_select": ("timeseries_funcs", "arma_order_select"), "detrend_linear": ("timeseries_funcs", "detrend_linear"), "add_trend_to_data": ("timeseries_funcs", "add_trend_to_data"), "fit_glm": ("glm", "fit_glm"), "fit_logit": ("glm", "fit_logit"), "predict_logit": ("glm", "predict_logit"), "fit_probit": ("glm", "fit_probit"), "fit_poisson": ("glm", "fit_poisson"), "fit_negative_binomial": ("glm", "fit_negative_binomial"), "fit_mnlogit": ("glm", "fit_mnlogit"), "fit_quantreg": ("glm", "fit_quantreg"), "fit_gee": ("glm", "fit_gee"), "fit_zero_inflated_poisson": ("glm", "fit_zero_inflated_poisson"), "fit_conditional_logit": ("glm", "fit_conditional_logit"), "fit_conditional_poisson": ("glm", "fit_conditional_poisson"), "fit_generalized_poisson": ("glm", "fit_generalized_poisson"), "fit_zero_inflated_generalized_poisson": ("glm", "fit_zero_inflated_generalized_poisson"), "ttest_ind": ("stats_tests", "ttest_ind"), "ttest_1samp": ("stats_tests", "ttest_1samp"), "ztest": ("stats_tests", "ztest"), "anova_lm": ("stats_tests", "anova_lm"), "proportions_ztest": ("stats_tests", "proportions_ztest"), "proportion_confint": ("stats_tests", "proportion_confint"), "chisquare_test": ("stats_tests", "chisquare_test"), "jarque_bera_test": ("stats_tests", "jarque_bera_test"), "omnibus_normtest": ("stats_tests", "omnibus_normtest"), "durbin_watson_test": ("stats_tests", "durbin_watson_test"), "het_breuschpagan": ("stats_tests", "het_breuschpagan"), "het_white": ("stats_tests", "het_white"), "het_arch": ("stats_tests", "het_arch"), "acorr_breusch_godfrey": ("stats_tests", "acorr_breusch_godfrey"), "linear_reset": ("stats_tests", "linear_reset"), "linear_harvey_collier": ("stats_tests", "linear_harvey_collier"), "multipletests_correction": ("stats_tests", "multipletests_correction"), "fdrcorrection_twostage": ("stats_tests", "fdrcorrection_twostage"), "compare_means": ("stats_tests", "compare_means"), "descr_stats": ("stats_tests", "descr_stats"), "ttest_power": ("power_analysis", "ttest_power"), "ftest_power": ("power_analysis", "ftest_power"), "proportion_power": ("power_analysis", "proportion_power"), "calculate_effect_size_cohens_d": ("power_analysis", "calculate_effect_size_cohens_d"), "calculate_effect_size_proportion": ("power_analysis", "calculate_effect_size_proportion"), "sample_size_proportion_confint": ("power_analysis", "sample_size_proportion_confint"), "chisquare_power": ("power_analysis", "chisquare_power"), "anova_power": ("power_analysis", "anova_power"), "perform_pca": ("multivariate", "perform_pca"), "perform_factor_analysis": ("multivariate", "perform_factor_analysis"), "perform_manova": ("multivariate", "perform_manova"), "canonical_correlation": ("multivariate", "canonical_correlation"), "fit_rlm": ("robust", "fit_rlm"), "mad": ("robust", "mad"), "huber_scale": ("robust", "huber_scale"), "huber_location_scale": ("robust", "huber_location_scale"), "fit_cox_ph": ("survival", "fit_cox_ph"), "survdiff": ("survival", "survdiff"), "kaplan_meier": ("survival", "kaplan_meier"), "kernel_density_estimation": ("nonparametric", "kernel_density_estimation"), "kernel_regression": ("nonparametric", "kernel_regression"), "lowess_smoothing": ("nonparametric", "lowess_smoothing"), "multivariate_kde": ("nonparametric", "multivariate_kde"), "fit_glm_gam": ("gam", "fit_glm_gam"), "fit_gam_formula": ("gam", "fit_gam_formula"), } def __getattr__(name: str): # PEP 562 target = _LAZY_ATTRS.get(name) if target is None: raise AttributeError(f"module {__name__!r} has no attribute {name!r}") submodule, original_name = target import importlib mod = importlib.import_module(f".{submodule}", __name__) value = getattr(mod, original_name) globals()[name] = value # cache for subsequent access return value def __dir__() -> list[str]: return sorted(set(globals()) | set(_LAZY_ATTRS))