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anofox_statistics

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Extension repository on GitHub

A DuckDB extension for statistical regression and inference in SQL - OLS, Ridge, Elastic Net, LARS/LassoLars, WLS, recursive least squares, robust estimators (Huber, RANSAC, Theil-Sen), GLMs (Poisson, Negative Binomial, Binomial, Tweedie, Gamma, Logistic) with offset support, mixed-effects/hierarchical GLMs with random slopes and crossed/nested factors, AFT survival regression, explicit priors with Laplace intervals, empirical-Bayes shrinkage, and time-series regression, all with full diagnostics and inference.

Maintainer(s): sipemu

Installing and Loading

INSTALL anofox_statistics FROM community;
LOAD anofox_statistics;

Added Functions

function_name function_type description comment examples
aft_cdf scalar NULL NULL
aft_fit_agg aggregate NULL NULL
aft_quantile scalar NULL NULL
aic scalar NULL NULL
aid_agg aggregate NULL NULL
aid_anomaly_agg aggregate NULL NULL
aid_anomaly_by table_macro NULL NULL
aid_by table_macro NULL NULL
alm_fit_agg aggregate NULL NULL
alm_fit_predict_agg aggregate NULL NULL
alm_fit_predict_by table_macro NULL NULL
anofox_stats_aft_cdf scalar NULL NULL
anofox_stats_aft_fit_agg aggregate NULL NULL
anofox_stats_aft_quantile scalar NULL NULL
anofox_stats_aic scalar Computes Akaike Information Criterion (AIC) from residual sum of squares, number of observations, and number of parameters. NULL [anofox_stats_aic(rss, n, k)]
anofox_stats_aid_agg aggregate Classifies demand patterns (smooth, intermittent, erratic, lumpy) using Automatic Identification of Demand (AID). NULL [anofox_stats_aid_agg(y)]
anofox_stats_aid_agg aggregate Classifies demand patterns using AID with a MAP of options (intermittent_threshold, outlier_method). NULL [anofox_stats_aid_agg(y, {‘intermittent_threshold’: 0.3})]
anofox_stats_aid_anomaly_agg aggregate Identifies anomalies in demand time series using AID with a MAP of options (intermittent_threshold, outlier_method). NULL [anofox_stats_aid_anomaly_agg(y, {‘outlier_method’: ‘iqr’})]
anofox_stats_aid_anomaly_agg aggregate Identifies anomalies in demand time series using the AID classification framework. NULL [anofox_stats_aid_anomaly_agg(y)]
anofox_stats_alm_fit_agg aggregate Fits an Additive Linear Model (ALM) and returns coefficients and fit statistics. NULL [anofox_stats_alm_fit_agg(y, x)]
anofox_stats_alm_fit_agg aggregate Fits an Additive Linear Model (ALM) and returns coefficients and fit statistics. NULL [anofox_stats_alm_fit_agg(y, x, {‘fit_intercept’: true})]
anofox_stats_alm_fit_predict_agg aggregate Fits an Additive Linear Model on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_alm_fit_predict_agg(y, x, split_col, {‘distribution’: ‘laplace’})]
anofox_stats_alm_fit_predict_agg aggregate Fits an Additive Linear Model over a partition and returns per-row predictions. NULL [anofox_stats_alm_fit_predict_agg(y, x)]
anofox_stats_alm_fit_predict_agg aggregate Fits an Additive Linear Model over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_alm_fit_predict_agg(y, x, {‘distribution’: ‘laplace’})]
anofox_stats_alm_fit_predict_agg aggregate Fits an Additive Linear Model using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_alm_fit_predict_agg(y, x, split_col)]
anofox_stats_bic scalar Computes Bayesian Information Criterion (BIC) from residual sum of squares, number of observations, and number of parameters. NULL [anofox_stats_bic(rss, n, k)]
anofox_stats_binom_test_agg aggregate Performs an exact binomial test comparing an observed success count to a hypothesized probability, using default options. NULL [anofox_stats_binom_test_agg(value)]
anofox_stats_binom_test_agg aggregate Performs an exact binomial test comparing an observed success count to a hypothesized probability. NULL [anofox_stats_binom_test_agg(value, {‘p0’: 0.5, ‘alternative’: ‘two_sided’})]
anofox_stats_binomial_fit_agg aggregate Fits a Binomial GLM (logit link by default) and returns coefficients, deviance, AIC, dispersion, and fit statistics. y is the success rate in [0, 1]. NULL [anofox_stats_binomial_fit_agg(y, x)]
anofox_stats_binomial_fit_agg aggregate Fits a Binomial GLM (user-selectable link: logit / probit / cloglog; default logit) and returns coefficients, deviance, AIC, dispersion (= 1 for canonical binomial), and fit statistics. y is the success rate in [0, 1]. NULL [anofox_stats_binomial_fit_agg(y, x, {‘binomial_link’: ‘logit’, ‘fit_intercept’: true})]
anofox_stats_bls_fit_agg aggregate Fits a Bounded Least Squares (BLS) regression with coefficient bounds and returns fit statistics. NULL [anofox_stats_bls_fit_agg(y, x)]
anofox_stats_bls_fit_agg aggregate Fits a Bounded Least Squares (BLS) regression with coefficient bounds and returns fit statistics. NULL [anofox_stats_bls_fit_agg(y, x, {‘lower_bounds’: [-1.0], ‘upper_bounds’: [1.0]})]
anofox_stats_bls_fit_predict_agg aggregate Fits a Bounded Least Squares model on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_bls_fit_predict_agg(y, x, split_col, {‘null_policy’: ‘drop’})]
anofox_stats_bls_fit_predict_agg aggregate Fits a Bounded Least Squares model over a partition and returns per-row predictions. NULL [anofox_stats_bls_fit_predict_agg(y, x)]
anofox_stats_bls_fit_predict_agg aggregate Fits a Bounded Least Squares model over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_bls_fit_predict_agg(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_bls_fit_predict_agg aggregate Fits a Bounded Least Squares model using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_bls_fit_predict_agg(y, x, split_col)]
anofox_stats_brown_forsythe_agg aggregate Tests equality of variances across groups using the Brown-Forsythe test. NULL [anofox_stats_brown_forsythe_agg(value, group_id)]
anofox_stats_brunner_munzel_agg aggregate Performs the Brunner-Munzel test for stochastic equality of two independent samples, using default options. NULL [anofox_stats_brunner_munzel_agg(value, group_id)]
anofox_stats_brunner_munzel_agg aggregate Performs the Brunner-Munzel test for stochastic equality of two independent samples. NULL [anofox_stats_brunner_munzel_agg(value, group_id, {‘alternative’: ‘two_sided’})]
anofox_stats_chisq_gof_agg aggregate Performs a chi-squared goodness-of-fit test comparing observed frequencies to expected probabilities. NULL [anofox_stats_chisq_gof_agg(observed, expected_prob)]
anofox_stats_chisq_test_agg aggregate Performs a chi-squared test of independence on a 2×2 contingency table from two categorical columns. NULL [anofox_stats_chisq_test_agg(row_var, col_var)]
anofox_stats_chisq_test_agg aggregate Performs a chi-squared test of independence on a 2×2 contingency table from two categorical columns. NULL [anofox_stats_chisq_test_agg(row_var, col_var, {‘correction’: true})]
anofox_stats_clark_west_agg aggregate Performs the Clark-West test to compare a nested forecast model against an encompassing model, using default options. NULL [anofox_stats_clark_west_agg(actual, forecast_restricted, forecast_unrestricted)]
anofox_stats_clark_west_agg aggregate Performs the Clark-West test to compare a nested forecast model against an encompassing model. NULL [anofox_stats_clark_west_agg(actual, forecast_restricted, forecast_unrestricted, {‘horizon’: 1})]
anofox_stats_cohen_kappa_agg aggregate Computes Cohen’s kappa, a measure of inter-rater agreement for categorical classifications. NULL [anofox_stats_cohen_kappa_agg(rater1, rater2)]
anofox_stats_cohen_kappa_agg aggregate Computes Cohen’s kappa, a measure of inter-rater agreement for categorical classifications. NULL [anofox_stats_cohen_kappa_agg(rater1, rater2, {‘weighted’: false})]
anofox_stats_contingency_coef_agg aggregate Computes the contingency coefficient (C), a measure of association for categorical variables. NULL [anofox_stats_contingency_coef_agg(row_var, col_var)]
anofox_stats_cramers_v_agg aggregate Computes Cramér’s V, a measure of association strength for nominal categorical variables. NULL [anofox_stats_cramers_v_agg(row_var, col_var)]
anofox_stats_dagostino_k2_agg aggregate Performs the D’Agostino-Pearson K² omnibus normality test based on skewness and kurtosis. NULL [anofox_stats_dagostino_k2_agg(value)]
anofox_stats_diebold_mariano_agg aggregate Performs the Diebold-Mariano test to compare predictive accuracy of two forecast models, using default options. NULL [anofox_stats_diebold_mariano_agg(actual, forecast1, forecast2)]
anofox_stats_diebold_mariano_agg aggregate Performs the Diebold-Mariano test to compare predictive accuracy of two forecast models. NULL [anofox_stats_diebold_mariano_agg(actual, forecast1, forecast2, {‘loss’: ‘squared’})]
anofox_stats_distance_cor_agg aggregate Computes the distance correlation between two variables, detecting both linear and nonlinear dependence, using default options. NULL [anofox_stats_distance_cor_agg(x, y)]
anofox_stats_distance_cor_agg aggregate Computes the distance correlation between two variables, detecting both linear and nonlinear dependence. NULL [anofox_stats_distance_cor_agg(x, y, {‘n_permutations’: 1000})]
anofox_stats_eb_shrink_agg aggregate NULL NULL
anofox_stats_elasticnet_fit scalar Fits an ElasticNet regression model (L1+L2 regularization) with optional MAP of settings (fit_intercept, alpha, l1_ratio, max_iterations, tolerance). NULL [anofox_stats_elasticnet_fit(y, x, {‘alpha’: 1.0, ‘l1_ratio’: 0.5})]
anofox_stats_elasticnet_fit scalar Fits an ElasticNet regression model combining L1 and L2 regularization to the given response and feature data. NULL [anofox_stats_elasticnet_fit(y, x)]
anofox_stats_elasticnet_fit_agg aggregate Fits an ElasticNet model combining L1 and L2 regularization and returns coefficients and fit statistics. NULL [anofox_stats_elasticnet_fit_agg(y, x)]
anofox_stats_elasticnet_fit_agg aggregate Fits an ElasticNet model combining L1 and L2 regularization and returns coefficients and fit statistics. NULL [anofox_stats_elasticnet_fit_agg(y, x, {‘alpha’: 1.0, ‘l1_ratio’: 0.5})]
anofox_stats_elasticnet_fit_predict aggregate Fits an ElasticNet regression model over a window partition and returns predictions with confidence intervals. NULL [anofox_stats_elasticnet_fit_predict(y, x)]
anofox_stats_elasticnet_fit_predict aggregate Fits an ElasticNet regression model over a window partition and returns predictions with confidence intervals. NULL [anofox_stats_elasticnet_fit_predict(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_elasticnet_fit_predict_agg aggregate Fits ElasticNet regression on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_elasticnet_fit_predict_agg(y, x, split_col, {‘null_policy’: ‘drop’})]
anofox_stats_elasticnet_fit_predict_agg aggregate Fits ElasticNet regression over a partition and returns per-row predictions with confidence intervals. NULL [anofox_stats_elasticnet_fit_predict_agg(y, x)]
anofox_stats_elasticnet_fit_predict_agg aggregate Fits ElasticNet regression over a partition with a MAP of options and returns per-row predictions with confidence intervals. NULL [anofox_stats_elasticnet_fit_predict_agg(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_elasticnet_fit_predict_agg aggregate Fits ElasticNet regression using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_elasticnet_fit_predict_agg(y, x, split_col)]
anofox_stats_elasticnet_predict_agg aggregate NULL NULL
anofox_stats_energy_distance_agg aggregate Computes the energy distance between two samples as a measure of distributional difference, using default options. NULL [anofox_stats_energy_distance_agg(value, group_id)]
anofox_stats_energy_distance_agg aggregate Computes the energy distance between two samples as a measure of distributional difference. NULL [anofox_stats_energy_distance_agg(value, group_id, {‘n_permutations’: 1000})]
anofox_stats_fisher_exact_agg aggregate Performs Fisher’s exact test for association in a 2×2 contingency table. NULL [anofox_stats_fisher_exact_agg(row_var, col_var)]
anofox_stats_fisher_exact_agg aggregate Performs Fisher’s exact test for association in a 2×2 contingency table. NULL [anofox_stats_fisher_exact_agg(row_var, col_var, {‘alternative’: ‘two_sided’})]
anofox_stats_g_test_agg aggregate Performs a G-test (log-likelihood ratio test) for goodness of fit or independence. NULL [anofox_stats_g_test_agg(row_var, col_var)]
anofox_stats_gamma_fit_agg aggregate Fits a Gamma GLM (log link, var_power = 2.0 fixed) and returns coefficients, deviance, AIC, dispersion, and fit statistics. NULL [anofox_stats_gamma_fit_agg(y, x)]
anofox_stats_gamma_fit_agg aggregate Fits a Gamma GLM (log link, var_power = 2.0 fixed) and returns coefficients, deviance, AIC, dispersion, and fit statistics. y must be strictly positive. NULL [anofox_stats_gamma_fit_agg(y, x, {‘fit_intercept’: true})]
anofox_stats_glmm_fit_agg aggregate NULL NULL
anofox_stats_huber_fit scalar Fits a Huber M-estimator regression model with optional MAP of settings (epsilon, alpha, fit_intercept, compute_inference, confidence_level, max_iterations, tolerance). NULL [anofox_stats_huber_fit(y, x, {‘epsilon’: 1.35, ‘alpha’: 0.01})]
anofox_stats_huber_fit scalar Fits a Huber M-estimator robust regression model. Returns coefficients, fit statistics, the MAD-based scale, and the outlier count as a struct. NULL [anofox_stats_huber_fit(y, x)]
anofox_stats_huber_fit_agg aggregate Fits a Huber M-estimator robust regression model and returns coefficients, fit statistics, the MAD-based scale, and the outlier count as a struct. NULL [anofox_stats_huber_fit_agg(y, x)]
anofox_stats_huber_fit_agg aggregate Fits a Huber M-estimator robust regression model and returns coefficients, fit statistics, the MAD-based scale, and the outlier count as a struct. NULL [anofox_stats_huber_fit_agg(y, x, {‘epsilon’: 1.35, ‘fit_intercept’: true})]
anofox_stats_huber_fit_predict aggregate Fits a Huber M-estimator robust regression over a window partition and returns the prediction for the current row with confidence intervals. NULL [anofox_stats_huber_fit_predict(y, x) OVER (PARTITION BY g ORDER BY t)]
anofox_stats_huber_fit_predict aggregate Fits a Huber M-estimator robust regression over a window with a MAP of options. NULL [anofox_stats_huber_fit_predict(y, x, {‘epsilon’: 1.5}) OVER (…)]
anofox_stats_huber_fit_predict_agg aggregate Fits Huber regression on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_huber_fit_predict_agg(y, x, split_col, {‘epsilon’: 1.5})]
anofox_stats_huber_fit_predict_agg aggregate Fits Huber regression over a partition with a MAP of options and returns per-row predictions with confidence intervals. NULL [anofox_stats_huber_fit_predict_agg(y, x, {‘epsilon’: 1.35, ‘null_policy’: ‘drop’})]
anofox_stats_huber_fit_predict_agg aggregate Fits Huber regression using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_huber_fit_predict_agg(y, x, split_col)]
anofox_stats_huber_fit_predict_agg aggregate Fits a Huber M-estimator robust regression over a partition and returns per-row predictions with confidence intervals. NULL [anofox_stats_huber_fit_predict_agg(y, x)]
anofox_stats_icc_agg aggregate Computes the Intraclass Correlation Coefficient (ICC) to measure rater or measurement consistency. NULL [anofox_stats_icc_agg(value, subject_id, rater_id)]
anofox_stats_icc_agg aggregate Computes the Intraclass Correlation Coefficient (ICC) to measure rater or measurement consistency. NULL [anofox_stats_icc_agg(value, subject_id, rater_id, {‘type’: ‘single’})]
anofox_stats_isotonic_fit_predict_agg aggregate Fits an isotonic regression model on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_isotonic_fit_predict_agg(y, x, split_col, {‘increasing’: true})]
anofox_stats_isotonic_fit_predict_agg aggregate Fits an isotonic regression model over a partition and returns per-row predictions. NULL [anofox_stats_isotonic_fit_predict_agg(y, x)]
anofox_stats_isotonic_fit_predict_agg aggregate Fits an isotonic regression model over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_isotonic_fit_predict_agg(y, x, {‘increasing’: true})]
anofox_stats_isotonic_fit_predict_agg aggregate Fits an isotonic regression model using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_isotonic_fit_predict_agg(y, x, split_col)]
anofox_stats_jarque_bera scalar Tests whether a sample has skewness and kurtosis consistent with a normal distribution (Jarque-Bera test). NULL [anofox_stats_jarque_bera(values)]
anofox_stats_jarque_bera_agg aggregate Aggregate version of the Jarque-Bera normality test, applied to a column of values. NULL [anofox_stats_jarque_bera_agg(value)]
anofox_stats_kendall_agg aggregate Computes Kendall’s tau rank correlation coefficient and tests its significance. NULL [anofox_stats_kendall_agg(x, y)]
anofox_stats_kendall_agg aggregate Computes Kendall’s tau rank correlation coefficient and tests its significance. NULL [anofox_stats_kendall_agg(x, y, {‘alternative’: ‘two_sided’})]
anofox_stats_kruskal_wallis_agg aggregate Performs the Kruskal-Wallis H-test, a nonparametric alternative to one-way ANOVA. NULL [anofox_stats_kruskal_wallis_agg(value, group_id)]
anofox_stats_lars_fit_agg aggregate Fits a Least Angle Regression (LARS / LassoLars) model and returns coefficients and fit statistics. NULL [anofox_stats_lars_fit_agg(y, x, {‘fit_intercept’: true, ‘alpha’: 0.0})]
anofox_stats_lars_fit_agg aggregate Fits a Least Angle Regression (LARS) model and returns coefficients and fit statistics. NULL [anofox_stats_lars_fit_agg(y, x)]
anofox_stats_logistic_fit_agg aggregate Fits a binary Logistic regression (binomial GLM with logit link; classifier API). y must be binary (0 or 1). Result struct extends the standard GLM shape with accuracy (on training data, at the configured threshold) and the threshold echo. Optional L2 (ridge) penalty. NULL [anofox_stats_logistic_fit_agg(y, x, {‘glm_lambda’: 0.1, ‘threshold’: 0.5})]
anofox_stats_logistic_fit_agg aggregate Fits a binary Logistic regression (binomial GLM with logit link; classifier API). y must be binary (0 or 1). Result struct extends the standard GLM shape with accuracy and the threshold echo. NULL [anofox_stats_logistic_fit_agg(y, x)]
anofox_stats_mann_whitney_u_agg aggregate Performs the Mann-Whitney U test (Wilcoxon rank-sum) for two independent samples, using default options. NULL [anofox_stats_mann_whitney_u_agg(value, group_id)]
anofox_stats_mann_whitney_u_agg aggregate Performs the Mann-Whitney U test (Wilcoxon rank-sum) for two independent samples. NULL [anofox_stats_mann_whitney_u_agg(value, group_id, {‘alternative’: ‘two_sided’})]
anofox_stats_mcnemar_agg aggregate Performs McNemar’s test for marginal homogeneity in paired categorical data. NULL [anofox_stats_mcnemar_agg(var1, var2)]
anofox_stats_mcnemar_agg aggregate Performs McNemar’s test for marginal homogeneity in paired categorical data. NULL [anofox_stats_mcnemar_agg(var1, var2, {‘correction’: true})]
anofox_stats_mmd_agg aggregate Computes the Maximum Mean Discrepancy (MMD) between two samples to test distributional similarity, using default options. NULL [anofox_stats_mmd_agg(value, group_id)]
anofox_stats_mmd_agg aggregate Computes the Maximum Mean Discrepancy (MMD) between two samples to test distributional similarity. NULL [anofox_stats_mmd_agg(value, group_id, {‘n_permutations’: 1000})]
anofox_stats_negbinom_fit_agg aggregate Fits a Negative Binomial GLM (log link, overdispersion parameter estimated jointly) and returns coefficients, deviance, AIC, dispersion (= alpha), and fit statistics. NULL [anofox_stats_negbinom_fit_agg(y, x)]
anofox_stats_negbinom_fit_agg aggregate Fits a Negative Binomial GLM (log link, overdispersion parameter estimated jointly) and returns coefficients, deviance, AIC, dispersion (= alpha), and fit statistics. NULL [anofox_stats_negbinom_fit_agg(y, x, {‘fit_intercept’: true})]
anofox_stats_nnls_fit_agg aggregate Fits a Non-Negative Least Squares (NNLS) regression with non-negativity constraints. NULL [anofox_stats_nnls_fit_agg(y, x)]
anofox_stats_nnls_fit_agg aggregate Fits a Non-Negative Least Squares (NNLS) regression with non-negativity constraints. NULL [anofox_stats_nnls_fit_agg(y, x, {‘tolerance’: 1e-6})]
anofox_stats_ols_fit scalar Fits an OLS regression model with optional MAP of settings (fit_intercept, compute_inference, confidence_level, solver, hc_type). NULL [anofox_stats_ols_fit(y, x, {‘compute_inference’: true, ‘confidence_level’: 0.95})]
anofox_stats_ols_fit scalar Fits an Ordinary Least Squares (OLS) regression model to the given response and feature data. NULL [anofox_stats_ols_fit(y, x)]
anofox_stats_ols_fit_agg aggregate Fits an OLS regression model and returns coefficients and fit statistics as a struct. NULL [anofox_stats_ols_fit_agg(y, x)]
anofox_stats_ols_fit_agg aggregate Fits an OLS regression model and returns coefficients and fit statistics as a struct. NULL [anofox_stats_ols_fit_agg(y, x, {‘fit_intercept’: true})]
anofox_stats_ols_fit_predict aggregate Fits an OLS model over a window partition and returns predictions for each row, including confidence intervals. NULL [anofox_stats_ols_fit_predict(y, x)]
anofox_stats_ols_fit_predict aggregate Fits an OLS model over a window partition and returns predictions for each row, including confidence intervals. NULL [anofox_stats_ols_fit_predict(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_ols_fit_predict_agg aggregate Fits OLS regression on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_ols_fit_predict_agg(y, x, split_col, {‘null_policy’: ‘drop’})]
anofox_stats_ols_fit_predict_agg aggregate Fits OLS regression over a partition and returns per-row predictions with confidence intervals. NULL [anofox_stats_ols_fit_predict_agg(y, x)]
anofox_stats_ols_fit_predict_agg aggregate Fits OLS regression over a partition with a MAP of options and returns per-row predictions with confidence intervals. NULL [anofox_stats_ols_fit_predict_agg(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_ols_fit_predict_agg aggregate Fits OLS regression using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_ols_fit_predict_agg(y, x, split_col)]
anofox_stats_ols_predict_agg aggregate NULL NULL
anofox_stats_one_way_anova_agg aggregate Performs a one-way ANOVA F-test to compare means across multiple groups. NULL [anofox_stats_one_way_anova_agg(value, group_id)]
anofox_stats_pearson_agg aggregate Computes Pearson’s product-moment correlation coefficient and tests its significance. NULL [anofox_stats_pearson_agg(x, y)]
anofox_stats_pearson_agg aggregate Computes Pearson’s product-moment correlation coefficient and tests its significance. NULL [anofox_stats_pearson_agg(x, y, {‘alternative’: ‘two_sided’})]
anofox_stats_permutation_t_test_agg aggregate Performs a permutation-based two-sample t-test using resampling, using default options. NULL [anofox_stats_permutation_t_test_agg(value, group_id)]
anofox_stats_permutation_t_test_agg aggregate Performs a permutation-based two-sample t-test using resampling. NULL [anofox_stats_permutation_t_test_agg(value, group_id, {‘alternative’: ‘two_sided’, ‘n_permutations’: 10000})]
anofox_stats_phi_coefficient_agg aggregate Computes the phi coefficient (φ), a measure of association for 2×2 contingency tables. NULL [anofox_stats_phi_coefficient_agg(row_var, col_var)]
anofox_stats_pls_fit_predict_agg aggregate Fits a Partial Least Squares model on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_pls_fit_predict_agg(y, x, split_col, {‘n_components’: 2})]
anofox_stats_pls_fit_predict_agg aggregate Fits a Partial Least Squares model over a partition and returns per-row predictions. NULL [anofox_stats_pls_fit_predict_agg(y, x)]
anofox_stats_pls_fit_predict_agg aggregate Fits a Partial Least Squares model over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_pls_fit_predict_agg(y, x, {‘n_components’: 2})]
anofox_stats_pls_fit_predict_agg aggregate Fits a Partial Least Squares model using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_pls_fit_predict_agg(y, x, split_col)]
anofox_stats_poisson_fit_agg aggregate Fits a Poisson regression (GLM with log link) and returns coefficients, deviance, AIC, and fit statistics. NULL [anofox_stats_poisson_fit_agg(y, x)]
anofox_stats_poisson_fit_agg aggregate Fits a Poisson regression (GLM with log link) and returns coefficients, deviance, AIC, and fit statistics. NULL [anofox_stats_poisson_fit_agg(y, x, {‘fit_intercept’: true})]
anofox_stats_poisson_fit_predict_agg aggregate Fits a Poisson regression on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_poisson_fit_predict_agg(y, x, split_col, {‘link’: ‘log’})]
anofox_stats_poisson_fit_predict_agg aggregate Fits a Poisson regression over a partition and returns per-row predictions. NULL [anofox_stats_poisson_fit_predict_agg(y, x)]
anofox_stats_poisson_fit_predict_agg aggregate Fits a Poisson regression over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_poisson_fit_predict_agg(y, x, {‘link’: ‘log’})]
anofox_stats_poisson_fit_predict_agg aggregate Fits a Poisson regression using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_poisson_fit_predict_agg(y, x, split_col)]
anofox_stats_predict scalar Applies pre-fitted coefficients and intercept to feature data to generate predictions. NULL [anofox_stats_predict(x, coefficients, intercept)]
anofox_stats_prop_test_one_agg aggregate Tests whether an observed proportion differs from a hypothesized value (one-sample proportion test), using default options. NULL [anofox_stats_prop_test_one_agg(value)]
anofox_stats_prop_test_one_agg aggregate Tests whether an observed proportion differs from a hypothesized value (one-sample proportion test). NULL [anofox_stats_prop_test_one_agg(value, {‘p0’: 0.5, ‘alternative’: ‘two_sided’})]
anofox_stats_prop_test_two_agg aggregate Tests whether two observed proportions are equal (two-sample proportion test), using default options. NULL [anofox_stats_prop_test_two_agg(value, group_id)]
anofox_stats_prop_test_two_agg aggregate Tests whether two observed proportions are equal (two-sample proportion test). NULL [anofox_stats_prop_test_two_agg(value, group_id, {‘alternative’: ‘two_sided’})]
anofox_stats_quantile_fit_predict_agg aggregate Fits a quantile regression model on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_quantile_fit_predict_agg(y, x, split_col, {‘quantile’: 0.5})]
anofox_stats_quantile_fit_predict_agg aggregate Fits a quantile regression model over a partition and returns per-row predictions. NULL [anofox_stats_quantile_fit_predict_agg(y, x)]
anofox_stats_quantile_fit_predict_agg aggregate Fits a quantile regression model over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_quantile_fit_predict_agg(y, x, {‘quantile’: 0.5})]
anofox_stats_quantile_fit_predict_agg aggregate Fits a quantile regression model using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_quantile_fit_predict_agg(y, x, split_col)]
anofox_stats_ransac_fit scalar Fits a RANSAC regression model with optional MAP of settings (residual_threshold, max_trials, min_samples, stop_probability, stop_n_inliers, random_state, fit_intercept, compute_inference, confidence_level). NULL [anofox_stats_ransac_fit(y, x, {‘residual_threshold’: 0.5, ‘random_state’: 42})]
anofox_stats_ransac_fit scalar Fits a RANSAC robust regression model. Returns coefficients, fit statistics, the residual threshold used, and the inlier / trial counts as a struct. NULL [anofox_stats_ransac_fit(y, x)]
anofox_stats_ransac_fit_agg aggregate Fits a RANSAC robust regression model and returns coefficients, fit statistics, the residual threshold used, and the inlier / trial counts as a struct. NULL [anofox_stats_ransac_fit_agg(y, x)]
anofox_stats_ransac_fit_agg aggregate Fits a RANSAC robust regression model and returns coefficients, fit statistics, the residual threshold used, and the inlier / trial counts as a struct. NULL [anofox_stats_ransac_fit_agg(y, x, {‘residual_threshold’: 0.5, ‘random_state’: 42})]
anofox_stats_ransac_fit_predict aggregate Fits a RANSAC regression over a window with a MAP of options. NULL [anofox_stats_ransac_fit_predict(y, x, {‘residual_threshold’: 0.5}) OVER (…)]
anofox_stats_ransac_fit_predict aggregate Fits a RANSAC robust regression over a window partition and returns the prediction for the current row. NULL [anofox_stats_ransac_fit_predict(y, x) OVER (PARTITION BY g ORDER BY t)]
anofox_stats_ransac_fit_predict_agg aggregate Fits RANSAC on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_ransac_fit_predict_agg(y, x, split_col, {‘residual_threshold’: 0.5})]
anofox_stats_ransac_fit_predict_agg aggregate Fits RANSAC over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_ransac_fit_predict_agg(y, x, {‘residual_threshold’: 0.5, ‘random_state’: 42})]
anofox_stats_ransac_fit_predict_agg aggregate Fits RANSAC using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_ransac_fit_predict_agg(y, x, split_col)]
anofox_stats_ransac_fit_predict_agg aggregate Fits a RANSAC robust regression over a partition and returns per-row predictions with confidence intervals. NULL [anofox_stats_ransac_fit_predict_agg(y, x)]
anofox_stats_residuals_diagnostics scalar Computes raw and standardized residuals, leverage, and Cook’s distance from actuals and predictions. NULL [anofox_stats_residuals_diagnostics(y, y_hat)]
anofox_stats_residuals_diagnostics scalar Computes residual diagnostics including studentized residuals when feature matrix and residual standard error are supplied. NULL [anofox_stats_residuals_diagnostics(y, y_hat, x, rse, true)]
anofox_stats_residuals_diagnostics_agg aggregate Aggregate version of residuals diagnostics with feature matrix: computes raw, standardized, studentized residuals and leverage from predicted and actual values. NULL [anofox_stats_residuals_diagnostics_agg(y, y_hat, x)]
anofox_stats_residuals_diagnostics_agg aggregate Aggregate version of residuals diagnostics: computes raw, standardized, studentized residuals and leverage from predicted and actual values. NULL [anofox_stats_residuals_diagnostics_agg(y, y_hat)]
anofox_stats_ridge_fit scalar Fits a Ridge regression model with L2 regularization and optional MAP of settings (fit_intercept, compute_inference, confidence_level, alpha, solver). NULL [anofox_stats_ridge_fit(y, x, {‘alpha’: 1.0, ‘compute_inference’: true})]
anofox_stats_ridge_fit scalar Fits a Ridge regression model with L2 regularization to the given response and feature data. NULL [anofox_stats_ridge_fit(y, x)]
anofox_stats_ridge_fit_agg aggregate Fits a Ridge regression model with L2 regularization and returns coefficients and fit statistics. NULL [anofox_stats_ridge_fit_agg(y, x)]
anofox_stats_ridge_fit_agg aggregate Fits a Ridge regression model with L2 regularization and returns coefficients and fit statistics. NULL [anofox_stats_ridge_fit_agg(y, x, {‘alpha’: 1.0})]
anofox_stats_ridge_fit_predict aggregate Fits a Ridge regression model over a window partition and returns predictions with confidence intervals. NULL [anofox_stats_ridge_fit_predict(y, x)]
anofox_stats_ridge_fit_predict aggregate Fits a Ridge regression model over a window partition and returns predictions with confidence intervals. NULL [anofox_stats_ridge_fit_predict(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_ridge_fit_predict_agg aggregate Fits Ridge regression on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_ridge_fit_predict_agg(y, x, split_col, {‘null_policy’: ‘drop’})]
anofox_stats_ridge_fit_predict_agg aggregate Fits Ridge regression over a partition and returns per-row predictions with confidence intervals. NULL [anofox_stats_ridge_fit_predict_agg(y, x)]
anofox_stats_ridge_fit_predict_agg aggregate Fits Ridge regression over a partition with a MAP of options and returns per-row predictions with confidence intervals. NULL [anofox_stats_ridge_fit_predict_agg(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_ridge_fit_predict_agg aggregate Fits Ridge regression using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_ridge_fit_predict_agg(y, x, split_col)]
anofox_stats_ridge_predict_agg aggregate NULL NULL
anofox_stats_rls_fit scalar Fits a Recursive Least Squares (RLS) model to the given response and feature data. NULL [anofox_stats_rls_fit(y, x)]
anofox_stats_rls_fit scalar Fits a Recursive Least Squares (RLS) model with optional MAP of settings (fit_intercept, forgetting_factor, initial_p_diagonal). NULL [anofox_stats_rls_fit(y, x, {‘forgetting_factor’: 0.99})]
anofox_stats_rls_fit_agg aggregate Fits a Recursive Least Squares model and returns coefficients and fit statistics. NULL [anofox_stats_rls_fit_agg(y, x)]
anofox_stats_rls_fit_agg aggregate Fits a Recursive Least Squares model and returns coefficients and fit statistics. NULL [anofox_stats_rls_fit_agg(y, x, {‘forgetting_factor’: 0.99})]
anofox_stats_rls_fit_predict aggregate Fits a Robust Least Squares model over a window partition and returns predictions with confidence intervals. NULL [anofox_stats_rls_fit_predict(y, x)]
anofox_stats_rls_fit_predict aggregate Fits a Robust Least Squares model over a window partition and returns predictions with confidence intervals. NULL [anofox_stats_rls_fit_predict(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_rls_fit_predict_agg aggregate Fits Robust LS regression on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_rls_fit_predict_agg(y, x, split_col, {‘null_policy’: ‘drop’})]
anofox_stats_rls_fit_predict_agg aggregate Fits Robust LS regression over a partition and returns per-row predictions. NULL [anofox_stats_rls_fit_predict_agg(y, x)]
anofox_stats_rls_fit_predict_agg aggregate Fits Robust LS regression over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_rls_fit_predict_agg(y, x, {‘null_policy’: ‘drop’})]
anofox_stats_rls_fit_predict_agg aggregate Fits Robust LS regression using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_rls_fit_predict_agg(y, x, split_col)]
anofox_stats_rls_predict_agg aggregate NULL NULL
anofox_stats_shapiro_wilk_agg aggregate Performs the Shapiro-Wilk test for normality on a sample. NULL [anofox_stats_shapiro_wilk_agg(value)]
anofox_stats_spearman_agg aggregate Computes Spearman’s rank correlation coefficient and tests its significance. NULL [anofox_stats_spearman_agg(x, y)]
anofox_stats_spearman_agg aggregate Computes Spearman’s rank correlation coefficient and tests its significance. NULL [anofox_stats_spearman_agg(x, y, {‘alternative’: ‘two_sided’})]
anofox_stats_t_test_agg aggregate Performs a two-sample t-test (Welch or Student) comparing values between two groups, using default options. NULL [anofox_stats_t_test_agg(value, group_id)]
anofox_stats_t_test_agg aggregate Performs a two-sample t-test (Welch or Student) comparing values between two groups. NULL [anofox_stats_t_test_agg(value, group_id, {‘alternative’: ‘two_sided’})]
anofox_stats_theilsen_fit scalar Fits a Theil-Sen regression model with optional MAP of settings (max_subpopulation, n_subsamples, max_iterations, tolerance, random_state, fit_intercept, compute_inference, confidence_level). NULL [anofox_stats_theilsen_fit(y, x, {‘random_state’: 42, ‘max_subpopulation’: 5000})]
anofox_stats_theilsen_fit scalar Fits a Theil-Sen robust regression model. Returns coefficients and fit statistics as a struct. NULL [anofox_stats_theilsen_fit(y, x)]
anofox_stats_theilsen_fit_agg aggregate Fits a Theil-Sen robust regression model and returns coefficients and fit statistics as a struct. NULL [anofox_stats_theilsen_fit_agg(y, x)]
anofox_stats_theilsen_fit_agg aggregate Fits a Theil-Sen robust regression model and returns coefficients and fit statistics as a struct. NULL [anofox_stats_theilsen_fit_agg(y, x, {‘random_state’: 42, ‘max_subpopulation’: 5000})]
anofox_stats_theilsen_fit_predict aggregate Fits a Theil-Sen regression over a window with a MAP of options. NULL [anofox_stats_theilsen_fit_predict(y, x, {‘random_state’: 42}) OVER (…)]
anofox_stats_theilsen_fit_predict aggregate Fits a Theil-Sen robust regression over a window partition and returns the prediction for the current row. NULL [anofox_stats_theilsen_fit_predict(y, x) OVER (PARTITION BY g ORDER BY t)]
anofox_stats_theilsen_fit_predict_agg aggregate Fits Theil-Sen on training rows with a MAP of options and predicts all rows. NULL [anofox_stats_theilsen_fit_predict_agg(y, x, split_col, {‘random_state’: 42})]
anofox_stats_theilsen_fit_predict_agg aggregate Fits Theil-Sen over a partition with a MAP of options and returns per-row predictions. NULL [anofox_stats_theilsen_fit_predict_agg(y, x, {‘random_state’: 42})]
anofox_stats_theilsen_fit_predict_agg aggregate Fits Theil-Sen using only training rows (split_col=‘train’) and predicts all rows. NULL [anofox_stats_theilsen_fit_predict_agg(y, x, split_col)]
anofox_stats_theilsen_fit_predict_agg aggregate Fits a Theil-Sen robust regression over a partition and returns per-row predictions with confidence intervals. NULL [anofox_stats_theilsen_fit_predict_agg(y, x)]
anofox_stats_tost_correlation_agg aggregate Tests equivalence of a correlation to a reference value using the TOST procedure, using default options. NULL [anofox_stats_tost_correlation_agg(x, y)]
anofox_stats_tost_correlation_agg aggregate Tests equivalence of a correlation to a reference value using the TOST procedure. NULL [anofox_stats_tost_correlation_agg(x, y, {‘delta’: 0.1})]
anofox_stats_tost_paired_agg aggregate Tests equivalence of paired measurements using the TOST procedure, using default options. NULL [anofox_stats_tost_paired_agg(x, y)]
anofox_stats_tost_paired_agg aggregate Tests equivalence of paired measurements using the TOST procedure. NULL [anofox_stats_tost_paired_agg(x, y, {‘delta’: 0.5})]
anofox_stats_tost_t_test_agg aggregate Tests equivalence of two groups using the Two One-Sided Tests (TOST) procedure with a t-test, using default options. NULL [anofox_stats_tost_t_test_agg(value, group_id)]
anofox_stats_tost_t_test_agg aggregate Tests equivalence of two groups using the Two One-Sided Tests (TOST) procedure with a t-test. NULL [anofox_stats_tost_t_test_agg(value, group_id, {‘delta’: 1.0})]
anofox_stats_tweedie_fit_agg aggregate Fits a Tweedie GLM (log link, default power = 1.5 — compound Poisson-Gamma) and returns coefficients, deviance, AIC, dispersion (= phi), and fit statistics. NULL [anofox_stats_tweedie_fit_agg(y, x)]
anofox_stats_tweedie_fit_agg aggregate Fits a Tweedie GLM (log link, user-specified power 1 < p < 2 for compound Poisson-Gamma) and returns coefficients, deviance, AIC, dispersion (= phi), and fit statistics. Default power is 1.5. NULL [anofox_stats_tweedie_fit_agg(y, x, {‘power’: 1.5, ‘fit_intercept’: true})]
anofox_stats_vif scalar Computes Variance Inflation Factor (VIF) for each column of a feature matrix to detect multicollinearity. NULL [anofox_stats_vif(x)]
anofox_stats_vif_agg aggregate Aggregate version of VIF: computes Variance Inflation Factor for each feature from a column of feature vectors. NULL [anofox_stats_vif_agg(x)]
anofox_stats_wilcoxon_signed_rank_agg aggregate Performs the Wilcoxon signed-rank test for paired samples, using default options. NULL [anofox_stats_wilcoxon_signed_rank_agg(x, y)]
anofox_stats_wilcoxon_signed_rank_agg aggregate Performs the Wilcoxon signed-rank test for paired samples. NULL [anofox_stats_wilcoxon_signed_rank_agg(x, y, {‘alternative’: ‘two_sided’})]
anofox_stats_wls_fit scalar Fits a WLS regression model with optional MAP of settings (fit_intercept, compute_inference, confidence_level, solver, hc_type). NULL [anofox_stats_wls_fit(y, x, weights, {‘compute_inference’: true})]
anofox_stats_wls_fit scalar Fits a Weighted Least Squares (WLS) regression model using per-observation weights. NULL [anofox_stats_wls_fit(y, x, weights)]
anofox_stats_wls_fit_agg aggregate Fits a Weighted Least Squares regression model and returns coefficients and fit statistics. NULL [anofox_stats_wls_fit_agg(y, x, weight)]
anofox_stats_wls_fit_agg aggregate Fits a Weighted Least Squares regression model and returns coefficients and fit statistics. NULL [anofox_stats_wls_fit_agg(y, x, weight, {‘fit_intercept’: true})]
anofox_stats_wls_fit_predict aggregate Fits a WLS regression model over a window partition using per-row weights and returns predictions. NULL [anofox_stats_wls_fit_predict(y, x, weight)]
anofox_stats_wls_fit_predict aggregate Fits a WLS regression model over a window partition using per-row weights and returns predictions. NULL [anofox_stats_wls_fit_predict(y, x, weight, {‘null_policy’: ‘drop’})]
anofox_stats_wls_fit_predict_agg aggregate Fits WLS regression on training rows with weights and a MAP of options and predicts all rows. NULL [anofox_stats_wls_fit_predict_agg(y, x, weights, split_col, {‘null_policy’: ‘drop’})]
anofox_stats_wls_fit_predict_agg aggregate Fits WLS regression over a partition using weights and returns per-row predictions. NULL [anofox_stats_wls_fit_predict_agg(y, x, weights)]
anofox_stats_wls_fit_predict_agg aggregate Fits WLS regression over a partition using weights with a MAP of options and returns per-row predictions. NULL [anofox_stats_wls_fit_predict_agg(y, x, weights, {‘null_policy’: ‘drop’})]
anofox_stats_wls_fit_predict_agg aggregate Fits WLS regression using only training rows (split_col=‘train’) with weights and predicts all rows. NULL [anofox_stats_wls_fit_predict_agg(y, x, weights, split_col)]
anofox_stats_wls_predict_agg aggregate NULL NULL
anofox_stats_yuen_agg aggregate Performs Yuen’s trimmed-means t-test, robust to outliers and non-normality, using default options. NULL [anofox_stats_yuen_agg(value, group_id)]
anofox_stats_yuen_agg aggregate Performs Yuen’s trimmed-means t-test, robust to outliers and non-normality. NULL [anofox_stats_yuen_agg(value, group_id, {‘trim’: 0.2})]
bic scalar NULL NULL
binom_test_agg aggregate NULL NULL
binomial_fit_agg aggregate NULL NULL
bls_fit_agg aggregate NULL NULL
bls_fit_predict_agg aggregate NULL NULL
bls_fit_predict_by table_macro NULL NULL
brown_forsythe_agg aggregate NULL NULL
brunner_munzel_agg aggregate NULL NULL
chisq_gof_agg aggregate NULL NULL
chisq_test_agg aggregate NULL NULL
clark_west_agg aggregate NULL NULL
cohen_kappa_agg aggregate NULL NULL
contingency_coef_agg aggregate NULL NULL
cramers_v_agg aggregate NULL NULL
dagostino_k2_agg aggregate NULL NULL
diebold_mariano_agg aggregate NULL NULL
distance_cor_agg aggregate NULL NULL
eb_shrink_agg aggregate NULL NULL
eb_shrink_by table_macro NULL NULL
elasticnet_fit scalar NULL NULL
elasticnet_fit_agg aggregate NULL NULL
elasticnet_fit_predict aggregate NULL NULL
elasticnet_fit_predict_agg aggregate NULL NULL
elasticnet_fit_predict_by table_macro NULL NULL
elasticnet_predict_agg aggregate NULL NULL
energy_distance_agg aggregate NULL NULL
fisher_exact_agg aggregate NULL NULL
g_test_agg aggregate NULL NULL
gamma_fit_agg aggregate NULL NULL
glmm_fit_agg aggregate NULL NULL
glmm_fit_by table_macro NULL NULL
huber_fit scalar NULL NULL
huber_fit_agg aggregate NULL NULL
huber_fit_predict aggregate NULL NULL
huber_fit_predict_agg aggregate NULL NULL
huber_fit_predict_by table_macro NULL NULL
icc_agg aggregate NULL NULL
isotonic_fit_predict_agg aggregate NULL NULL
isotonic_fit_predict_by table_macro NULL NULL
jarque_bera scalar NULL NULL
jarque_bera_agg aggregate NULL NULL
kendall_agg aggregate NULL NULL
kruskal_wallis_agg aggregate NULL NULL
lars_fit_agg aggregate NULL NULL
logistic_fit_agg aggregate NULL NULL
mann_whitney_u_agg aggregate NULL NULL
mcnemar_agg aggregate NULL NULL
mmd_agg aggregate NULL NULL
negbinom_fit_agg aggregate NULL NULL
nnls_fit_agg aggregate NULL NULL
ols_fit scalar NULL NULL
ols_fit_agg aggregate NULL NULL
ols_fit_predict aggregate NULL NULL
ols_fit_predict_agg aggregate NULL NULL
ols_fit_predict_by table_macro NULL NULL
ols_predict_agg aggregate NULL NULL
one_way_anova_agg aggregate NULL NULL
pearson_agg aggregate NULL NULL
permutation_t_test_agg aggregate NULL NULL
phi_coefficient_agg aggregate NULL NULL
pls_fit_predict_agg aggregate NULL NULL
pls_fit_predict_by table_macro NULL NULL
poisson_fit_agg aggregate NULL NULL
poisson_fit_predict_agg aggregate NULL NULL
poisson_fit_predict_by table_macro NULL NULL
prop_test_one_agg aggregate NULL NULL
prop_test_two_agg aggregate NULL NULL
quantile_fit_predict_agg aggregate NULL NULL
quantile_fit_predict_by table_macro NULL NULL
ransac_fit scalar NULL NULL
ransac_fit_agg aggregate NULL NULL
ransac_fit_predict aggregate NULL NULL
ransac_fit_predict_agg aggregate NULL NULL
ransac_fit_predict_by table_macro NULL NULL
residuals_diagnostics scalar NULL NULL
residuals_diagnostics_agg aggregate NULL NULL
ridge_fit scalar NULL NULL
ridge_fit_agg aggregate NULL NULL
ridge_fit_predict aggregate NULL NULL
ridge_fit_predict_agg aggregate NULL NULL
ridge_fit_predict_by table_macro NULL NULL
ridge_predict_agg aggregate NULL NULL
rls_fit scalar NULL NULL
rls_fit_agg aggregate NULL NULL
rls_fit_predict aggregate NULL NULL
rls_fit_predict_agg aggregate NULL NULL
rls_fit_predict_by table_macro NULL NULL
rls_predict_agg aggregate NULL NULL
shapiro_wilk_agg aggregate NULL NULL
spearman_agg aggregate NULL NULL
t_test_agg aggregate NULL NULL
theilsen_fit scalar NULL NULL
theilsen_fit_agg aggregate NULL NULL
theilsen_fit_predict aggregate NULL NULL
theilsen_fit_predict_agg aggregate NULL NULL
theilsen_fit_predict_by table_macro NULL NULL
tost_correlation_agg aggregate NULL NULL
tost_paired_agg aggregate NULL NULL
tost_t_test_agg aggregate NULL NULL
tweedie_fit_agg aggregate NULL NULL
vif scalar NULL NULL
vif_agg aggregate NULL NULL
wilcoxon_signed_rank_agg aggregate NULL NULL
wls_fit scalar NULL NULL
wls_fit_agg aggregate NULL NULL
wls_fit_predict aggregate NULL NULL
wls_fit_predict_agg aggregate NULL NULL
wls_fit_predict_by table_macro NULL NULL
wls_predict_agg aggregate NULL NULL
yuen_agg aggregate NULL NULL

Overloaded Functions

This extension does not add any function overloads.

Added Types

This extension does not add any types.

Added Settings

name description input_type scope aliases
anofox_telemetry_enabled Enable or disable anonymous usage telemetry BOOLEAN GLOBAL []
anofox_telemetry_key PostHog API key for telemetry VARCHAR GLOBAL []
datazoo_banner Show the DataZoo feedback banner when an extension is loaded in an interactive terminal (at most once a day per extension). BOOLEAN GLOBAL []