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 | Computes Akaike Information Criterion (AIC) from residual sum of squares, number of observations, and number of parameters. | NULL | [aic(rss, n, k)] |
| aid_agg | aggregate | Classifies demand patterns (smooth, intermittent, erratic, lumpy) using Automatic Identification of Demand (AID). | NULL | [aid_agg(y)] |
| aid_agg | aggregate | Classifies demand patterns using AID with a MAP of options (intermittent_threshold, outlier_method). | NULL | [aid_agg(y, {'intermittent_threshold': 0.3})] |
| aid_anomaly_agg | aggregate | Identifies anomalies in demand time series using AID with a MAP of options (intermittent_threshold, outlier_method). | NULL | [aid_anomaly_agg(y, {'outlier_method': 'iqr'})] |
| aid_anomaly_agg | aggregate | Identifies anomalies in demand time series using the AID classification framework. | NULL | [aid_anomaly_agg(y)] |
| aid_anomaly_by | table_macro | NULL | NULL | |
| aid_by | table_macro | NULL | NULL | |
| alm_fit_agg | aggregate | Fits an Additive Linear Model (ALM) and returns coefficients and fit statistics. | NULL | [alm_fit_agg(y, x)] |
| alm_fit_agg | aggregate | Fits an Additive Linear Model (ALM) and returns coefficients and fit statistics. | NULL | [alm_fit_agg(y, x, {'fit_intercept': true})] |
| alm_fit_predict_agg | aggregate | Fits an Additive Linear Model on training rows with a MAP of options and predicts all rows. | NULL | [alm_fit_predict_agg(y, x, split_col, {'distribution': 'laplace'})] |
| alm_fit_predict_agg | aggregate | Fits an Additive Linear Model over a partition and returns per-row predictions. | NULL | [alm_fit_predict_agg(y, x)] |
| alm_fit_predict_agg | aggregate | Fits an Additive Linear Model over a partition with a MAP of options and returns per-row predictions. | NULL | [alm_fit_predict_agg(y, x, {'distribution': 'laplace'})] |
| alm_fit_predict_agg | aggregate | Fits an Additive Linear Model using only training rows (split_col='train') and predicts all rows. | NULL | [alm_fit_predict_agg(y, x, split_col)] |
| alm_fit_predict_by | table_macro | NULL | NULL | |
| bic | scalar | Computes Bayesian Information Criterion (BIC) from residual sum of squares, number of observations, and number of parameters. | NULL | [bic(rss, n, k)] |
| binom_test_agg | aggregate | Performs an exact binomial test comparing an observed success count to a hypothesized probability, using default options. | NULL | [binom_test_agg(value)] |
| binom_test_agg | aggregate | Performs an exact binomial test comparing an observed success count to a hypothesized probability. | NULL | [binom_test_agg(value, {'p0': 0.5, 'alternative': 'two_sided'})] |
| 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 | [binomial_fit_agg(y, x)] |
| 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 | [binomial_fit_agg(y, x, {'binomial_link': 'logit', 'fit_intercept': true})] |
| bls_fit_agg | aggregate | Fits a Bounded Least Squares (BLS) regression with coefficient bounds and returns fit statistics. | NULL | [bls_fit_agg(y, x)] |
| bls_fit_agg | aggregate | Fits a Bounded Least Squares (BLS) regression with coefficient bounds and returns fit statistics. | NULL | [bls_fit_agg(y, x, {'lower_bound': 0.0, 'upper_bound': 1.0})] |
| bls_fit_predict_agg | aggregate | Fits a Bounded Least Squares model on training rows with a MAP of options and predicts all rows. | NULL | [bls_fit_predict_agg(y, x, split_col, {'null_policy': 'drop'})] |
| bls_fit_predict_agg | aggregate | Fits a Bounded Least Squares model over a partition and returns per-row predictions. | NULL | [bls_fit_predict_agg(y, x)] |
| 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 | [bls_fit_predict_agg(y, x, {'null_policy': 'drop'})] |
| bls_fit_predict_agg | aggregate | Fits a Bounded Least Squares model using only training rows (split_col='train') and predicts all rows. | NULL | [bls_fit_predict_agg(y, x, split_col)] |
| bls_fit_predict_by | table_macro | NULL | NULL | |
| brown_forsythe_agg | aggregate | Tests equality of variances across groups using the Brown-Forsythe test. | NULL | [brown_forsythe_agg(value, group_id)] |
| brunner_munzel_agg | aggregate | Performs the Brunner-Munzel test for stochastic equality of two independent samples, using default options. | NULL | [brunner_munzel_agg(value, group_id)] |
| brunner_munzel_agg | aggregate | Performs the Brunner-Munzel test for stochastic equality of two independent samples. | NULL | [brunner_munzel_agg(value, group_id, {'alternative': 'two_sided'})] |
| chisq_gof_agg | aggregate | Performs a chi-squared goodness-of-fit test comparing observed frequencies to expected probabilities. | NULL | [chisq_gof_agg(observed, expected_prob)] |
| chisq_test_agg | aggregate | Performs a chi-squared test of independence on a 2×2 contingency table from two categorical columns. | NULL | [chisq_test_agg(row_var, col_var)] |
| chisq_test_agg | aggregate | Performs a chi-squared test of independence on a 2×2 contingency table from two categorical columns. | NULL | [chisq_test_agg(row_var, col_var, {'correction': true})] |
| clark_west_agg | aggregate | Performs the Clark-West test to compare a nested forecast model against an encompassing model, using default options. | NULL | [clark_west_agg(actual, forecast_restricted, forecast_unrestricted)] |
| clark_west_agg | aggregate | Performs the Clark-West test to compare a nested forecast model against an encompassing model. | NULL | [clark_west_agg(actual, forecast_restricted, forecast_unrestricted, {'horizon': 1})] |
| cohen_kappa_agg | aggregate | Computes Cohen's kappa, a measure of inter-rater agreement for categorical classifications. | NULL | [cohen_kappa_agg(rater1, rater2)] |
| cohen_kappa_agg | aggregate | Computes Cohen's kappa, a measure of inter-rater agreement for categorical classifications. | NULL | [cohen_kappa_agg(rater1, rater2, {'weighted': false})] |
| contingency_coef_agg | aggregate | Computes the contingency coefficient (C), a measure of association for categorical variables. | NULL | [contingency_coef_agg(row_var, col_var)] |
| cramers_v_agg | aggregate | Computes Cramér's V, a measure of association strength for nominal categorical variables. | NULL | [cramers_v_agg(row_var, col_var)] |
| dagostino_k2_agg | aggregate | Performs the D'Agostino-Pearson K² omnibus normality test based on skewness and kurtosis. | NULL | [dagostino_k2_agg(value)] |
| diebold_mariano_agg | aggregate | Performs the Diebold-Mariano test to compare predictive accuracy of two forecast models, using default options. | NULL | [diebold_mariano_agg(actual, forecast1, forecast2)] |
| diebold_mariano_agg | aggregate | Performs the Diebold-Mariano test to compare predictive accuracy of two forecast models. | NULL | [diebold_mariano_agg(actual, forecast1, forecast2, {'loss': 'squared'})] |
| distance_cor_agg | aggregate | Computes the distance correlation between two variables, detecting both linear and nonlinear dependence, using default options. | NULL | [distance_cor_agg(x, y)] |
| distance_cor_agg | aggregate | Computes the distance correlation between two variables, detecting both linear and nonlinear dependence. | NULL | [distance_cor_agg(x, y, {'n_permutations': 1000})] |
| eb_shrink_agg | aggregate | NULL | NULL | |
| eb_shrink_by | table_macro | NULL | NULL | |
| 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 | [elasticnet_fit(y, x, {'alpha': 1.0, 'l1_ratio': 0.5})] |
| elasticnet_fit | scalar | Fits an ElasticNet regression model combining L1 and L2 regularization to the given response and feature data. | NULL | [elasticnet_fit(y, x)] |
| elasticnet_fit_agg | aggregate | Fits an ElasticNet model combining L1 and L2 regularization and returns coefficients and fit statistics. | NULL | [elasticnet_fit_agg(y, x)] |
| elasticnet_fit_agg | aggregate | Fits an ElasticNet model combining L1 and L2 regularization and returns coefficients and fit statistics. | NULL | [elasticnet_fit_agg(y, x, {'alpha': 1.0, 'l1_ratio': 0.5})] |
| elasticnet_fit_predict | aggregate | Fits an ElasticNet regression model over a window partition and returns predictions with confidence intervals. | NULL | [elasticnet_fit_predict(y, x)] |
| elasticnet_fit_predict | aggregate | Fits an ElasticNet regression model over a window partition and returns predictions with confidence intervals. | NULL | [elasticnet_fit_predict(y, x, {'null_policy': 'drop'})] |
| elasticnet_fit_predict_agg | aggregate | Fits ElasticNet regression on training rows with a MAP of options and predicts all rows. | NULL | [elasticnet_fit_predict_agg(y, x, split_col, {'null_policy': 'drop'})] |
| elasticnet_fit_predict_agg | aggregate | Fits ElasticNet regression over a partition and returns per-row predictions with confidence intervals. | NULL | [elasticnet_fit_predict_agg(y, x)] |
| 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 | [elasticnet_fit_predict_agg(y, x, {'null_policy': 'drop'})] |
| elasticnet_fit_predict_agg | aggregate | Fits ElasticNet regression using only training rows (split_col='train') and predicts all rows. | NULL | [elasticnet_fit_predict_agg(y, x, split_col)] |
| elasticnet_fit_predict_by | table_macro | NULL | NULL | |
| energy_distance_agg | aggregate | Computes the energy distance between two samples as a measure of distributional difference, using default options. | NULL | [energy_distance_agg(value, group_id)] |
| energy_distance_agg | aggregate | Computes the energy distance between two samples as a measure of distributional difference. | NULL | [energy_distance_agg(value, group_id, {'n_permutations': 1000})] |
| fisher_exact_agg | aggregate | Performs Fisher's exact test for association in a 2×2 contingency table. | NULL | [fisher_exact_agg(row_var, col_var)] |
| fisher_exact_agg | aggregate | Performs Fisher's exact test for association in a 2×2 contingency table. | NULL | [fisher_exact_agg(row_var, col_var, {'alternative': 'two_sided'})] |
| g_test_agg | aggregate | Performs a G-test (log-likelihood ratio test) for goodness of fit or independence. | NULL | [g_test_agg(row_var, col_var)] |
| 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 | [gamma_fit_agg(y, x)] |
| 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 | [gamma_fit_agg(y, x, {'fit_intercept': true})] |
| glmm_fit_agg | aggregate | NULL | NULL | |
| glmm_fit_by | table_macro | NULL | NULL | |
| 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 | [huber_fit(y, x, {'epsilon': 1.35, 'alpha': 0.01})] |
| 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 | [huber_fit(y, x)] |
| 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 | [huber_fit_agg(y, x)] |
| 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 | [huber_fit_agg(y, x, {'epsilon': 1.35, 'fit_intercept': true})] |
| 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 | [huber_fit_predict(y, x) OVER (PARTITION BY g ORDER BY t)] |
| huber_fit_predict | aggregate | Fits a Huber M-estimator robust regression over a window with a MAP of options. | NULL | [huber_fit_predict(y, x, {'epsilon': 1.5}) OVER (…)] |
| huber_fit_predict_agg | aggregate | Fits Huber regression on training rows with a MAP of options and predicts all rows. | NULL | [huber_fit_predict_agg(y, x, split_col, {'epsilon': 1.5})] |
| 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 | [huber_fit_predict_agg(y, x, {'epsilon': 1.35, 'null_policy': 'drop'})] |
| huber_fit_predict_agg | aggregate | Fits Huber regression using only training rows (split_col='train') and predicts all rows. | NULL | [huber_fit_predict_agg(y, x, split_col)] |
| huber_fit_predict_agg | aggregate | Fits a Huber M-estimator robust regression over a partition and returns per-row predictions with confidence intervals. | NULL | [huber_fit_predict_agg(y, x)] |
| huber_fit_predict_by | table_macro | NULL | NULL | |
| icc_agg | aggregate | Computes the Intraclass Correlation Coefficient (ICC) to measure rater or measurement consistency. | NULL | [icc_agg(value, subject_id, rater_id)] |
| icc_agg | aggregate | Computes the Intraclass Correlation Coefficient (ICC) to measure rater or measurement consistency. | NULL | [icc_agg(value, subject_id, rater_id, {'type': 'single'})] |
| isotonic_fit_predict_agg | aggregate | Fits an isotonic regression model on training rows with a MAP of options and predicts all rows. | NULL | [isotonic_fit_predict_agg(y, x, split_col, {'increasing': true})] |
| isotonic_fit_predict_agg | aggregate | Fits an isotonic regression model over a partition and returns per-row predictions. | NULL | [isotonic_fit_predict_agg(y, x)] |
| isotonic_fit_predict_agg | aggregate | Fits an isotonic regression model over a partition with a MAP of options and returns per-row predictions. | NULL | [isotonic_fit_predict_agg(y, x, {'increasing': true})] |
| isotonic_fit_predict_agg | aggregate | Fits an isotonic regression model using only training rows (split_col='train') and predicts all rows. | NULL | [isotonic_fit_predict_agg(y, x, split_col)] |
| isotonic_fit_predict_by | table_macro | NULL | NULL | |
| jarque_bera | scalar | Tests whether a sample has skewness and kurtosis consistent with a normal distribution (Jarque-Bera test). | NULL | [jarque_bera(values)] |
| jarque_bera_agg | aggregate | Aggregate version of the Jarque-Bera normality test, applied to a column of values. | NULL | [jarque_bera_agg(value)] |
| kendall_agg | aggregate | Computes Kendall's tau rank correlation coefficient and tests its significance. | NULL | [kendall_agg(x, y)] |
| kendall_agg | aggregate | Computes Kendall's tau rank correlation coefficient and tests its significance. | NULL | [kendall_agg(x, y, {'alternative': 'two_sided'})] |
| kruskal_wallis_agg | aggregate | Performs the Kruskal-Wallis H-test, a nonparametric alternative to one-way ANOVA. | NULL | [kruskal_wallis_agg(value, group_id)] |
| lars_fit_agg | aggregate | Fits a Least Angle Regression (LARS / LassoLars) model and returns coefficients and fit statistics. | NULL | [lars_fit_agg(y, x, {'fit_intercept': true, 'alpha': 0.0})] |
| lars_fit_agg | aggregate | Fits a Least Angle Regression (LARS) model and returns coefficients and fit statistics. | NULL | [lars_fit_agg(y, x)] |
| 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 | [logistic_fit_agg(y, x, {'glm_lambda': 0.1, 'threshold': 0.5})] |
| 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 | [logistic_fit_agg(y, x)] |
| mann_whitney_u_agg | aggregate | Performs the Mann-Whitney U test (Wilcoxon rank-sum) for two independent samples, using default options. | NULL | [mann_whitney_u_agg(value, group_id)] |
| mann_whitney_u_agg | aggregate | Performs the Mann-Whitney U test (Wilcoxon rank-sum) for two independent samples. | NULL | [mann_whitney_u_agg(value, group_id, {'alternative': 'two_sided'})] |
| mcnemar_agg | aggregate | Performs McNemar's test for marginal homogeneity in paired categorical data. | NULL | [mcnemar_agg(var1, var2)] |
| mcnemar_agg | aggregate | Performs McNemar's test for marginal homogeneity in paired categorical data. | NULL | [mcnemar_agg(var1, var2, {'correction': true})] |
| mmd_agg | aggregate | Computes the Maximum Mean Discrepancy (MMD) between two samples to test distributional similarity, using default options. | NULL | [mmd_agg(value, group_id)] |
| mmd_agg | aggregate | Computes the Maximum Mean Discrepancy (MMD) between two samples to test distributional similarity. | NULL | [mmd_agg(value, group_id, {'n_permutations': 1000})] |
| 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 | [negbinom_fit_agg(y, x)] |
| 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 | [negbinom_fit_agg(y, x, {'fit_intercept': true})] |
| nnls_fit_agg | aggregate | Fits a Non-Negative Least Squares (NNLS) regression with non-negativity constraints. | NULL | [nnls_fit_agg(y, x)] |
| nnls_fit_agg | aggregate | Fits a Non-Negative Least Squares (NNLS) regression with non-negativity constraints. | NULL | [nnls_fit_agg(y, x, {'tolerance': 1e-6})] |
| ols_fit | scalar | Fits an OLS regression model with optional MAP of settings (fit_intercept, compute_inference, confidence_level, solver, hc_type). | NULL | [ols_fit(y, x, {'compute_inference': true, 'confidence_level': 0.95})] |
| ols_fit | scalar | Fits an Ordinary Least Squares (OLS) regression model to the given response and feature data. | NULL | [ols_fit(y, x)] |
| ols_fit_agg | aggregate | Fits an OLS regression model and returns coefficients and fit statistics as a struct. | NULL | [ols_fit_agg(y, x)] |
| ols_fit_agg | aggregate | Fits an OLS regression model and returns coefficients and fit statistics as a struct. | NULL | [ols_fit_agg(y, x, {'fit_intercept': true})] |
| ols_fit_predict | aggregate | Fits an OLS model over a window partition and returns predictions for each row, including confidence intervals. | NULL | [ols_fit_predict(y, x)] |
| ols_fit_predict | aggregate | Fits an OLS model over a window partition and returns predictions for each row, including confidence intervals. | NULL | [ols_fit_predict(y, x, {'null_policy': 'drop'})] |
| ols_fit_predict_agg | aggregate | Fits OLS regression on training rows with a MAP of options and predicts all rows. | NULL | [ols_fit_predict_agg(y, x, split_col, {'null_policy': 'drop'})] |
| ols_fit_predict_agg | aggregate | Fits OLS regression over a partition and returns per-row predictions with confidence intervals. | NULL | [ols_fit_predict_agg(y, x)] |
| 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 | [ols_fit_predict_agg(y, x, {'null_policy': 'drop'})] |
| ols_fit_predict_agg | aggregate | Fits OLS regression using only training rows (split_col='train') and predicts all rows. | NULL | [ols_fit_predict_agg(y, x, split_col)] |
| ols_fit_predict_by | table_macro | NULL | NULL | |
| one_way_anova_agg | aggregate | Performs a one-way ANOVA F-test to compare means across multiple groups. | NULL | [one_way_anova_agg(value, group_id)] |
| pearson_agg | aggregate | Computes Pearson's product-moment correlation coefficient and tests its significance. | NULL | [pearson_agg(x, y)] |
| pearson_agg | aggregate | Computes Pearson's product-moment correlation coefficient and tests its significance. | NULL | [pearson_agg(x, y, {'alternative': 'two_sided'})] |
| permutation_t_test_agg | aggregate | Performs a permutation-based two-sample t-test using resampling, using default options. | NULL | [permutation_t_test_agg(value, group_id)] |
| permutation_t_test_agg | aggregate | Performs a permutation-based two-sample t-test using resampling. | NULL | [permutation_t_test_agg(value, group_id, {'alternative': 'two_sided', 'n_permutations': 10000})] |
| phi_coefficient_agg | aggregate | Computes the phi coefficient (φ), a measure of association for 2×2 contingency tables. | NULL | [phi_coefficient_agg(row_var, col_var)] |
| pls_fit_predict_agg | aggregate | Fits a Partial Least Squares model on training rows with a MAP of options and predicts all rows. | NULL | [pls_fit_predict_agg(y, x, split_col, {'n_components': 2})] |
| pls_fit_predict_agg | aggregate | Fits a Partial Least Squares model over a partition and returns per-row predictions. | NULL | [pls_fit_predict_agg(y, x)] |
| 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 | [pls_fit_predict_agg(y, x, {'n_components': 2})] |
| pls_fit_predict_agg | aggregate | Fits a Partial Least Squares model using only training rows (split_col='train') and predicts all rows. | NULL | [pls_fit_predict_agg(y, x, split_col)] |
| pls_fit_predict_by | table_macro | NULL | NULL | |
| poisson_fit_agg | aggregate | Fits a Poisson regression (GLM with log link) and returns coefficients, deviance, AIC, and fit statistics. | NULL | [poisson_fit_agg(y, x)] |
| poisson_fit_agg | aggregate | Fits a Poisson regression (GLM with log link) and returns coefficients, deviance, AIC, and fit statistics. | NULL | [poisson_fit_agg(y, x, {'fit_intercept': true})] |
| poisson_fit_predict_agg | aggregate | Fits a Poisson regression on training rows with a MAP of options and predicts all rows. | NULL | [poisson_fit_predict_agg(y, x, split_col, {'link': 'log'})] |
| poisson_fit_predict_agg | aggregate | Fits a Poisson regression over a partition and returns per-row predictions. | NULL | [poisson_fit_predict_agg(y, x)] |
| poisson_fit_predict_agg | aggregate | Fits a Poisson regression over a partition with a MAP of options and returns per-row predictions. | NULL | [poisson_fit_predict_agg(y, x, {'link': 'log'})] |
| poisson_fit_predict_agg | aggregate | Fits a Poisson regression using only training rows (split_col='train') and predicts all rows. | NULL | [poisson_fit_predict_agg(y, x, split_col)] |
| poisson_fit_predict_by | table_macro | NULL | NULL | |
| predict | scalar | Applies pre-fitted coefficients and intercept to feature data to generate predictions. | NULL | [predict(x, coefficients, intercept)] |
| prop_test_one_agg | aggregate | Tests whether an observed proportion differs from a hypothesized value (one-sample proportion test), using default options. | NULL | [prop_test_one_agg(value)] |
| prop_test_one_agg | aggregate | Tests whether an observed proportion differs from a hypothesized value (one-sample proportion test). | NULL | [prop_test_one_agg(value, {'p0': 0.5, 'alternative': 'two_sided'})] |
| prop_test_two_agg | aggregate | Tests whether two observed proportions are equal (two-sample proportion test), using default options. | NULL | [prop_test_two_agg(value, group_id)] |
| prop_test_two_agg | aggregate | Tests whether two observed proportions are equal (two-sample proportion test). | NULL | [prop_test_two_agg(value, group_id, {'alternative': 'two_sided'})] |
| quantile_fit_predict_agg | aggregate | Fits a quantile regression model on training rows with a MAP of options and predicts all rows. | NULL | [quantile_fit_predict_agg(y, x, split_col, {'quantile': 0.5})] |
| quantile_fit_predict_agg | aggregate | Fits a quantile regression model over a partition and returns per-row predictions. | NULL | [quantile_fit_predict_agg(y, x)] |
| quantile_fit_predict_agg | aggregate | Fits a quantile regression model over a partition with a MAP of options and returns per-row predictions. | NULL | [quantile_fit_predict_agg(y, x, {'quantile': 0.5})] |
| quantile_fit_predict_agg | aggregate | Fits a quantile regression model using only training rows (split_col='train') and predicts all rows. | NULL | [quantile_fit_predict_agg(y, x, split_col)] |
| quantile_fit_predict_by | table_macro | NULL | NULL | |
| 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 | [ransac_fit(y, x, {'residual_threshold': 0.5, 'random_state': 42})] |
| 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 | [ransac_fit(y, x)] |
| 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 | [ransac_fit_agg(y, x)] |
| 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 | [ransac_fit_agg(y, x, {'residual_threshold': 0.5, 'random_state': 42})] |
| ransac_fit_predict | aggregate | Fits a RANSAC regression over a window with a MAP of options. | NULL | [ransac_fit_predict(y, x, {'residual_threshold': 0.5}) OVER (…)] |
| ransac_fit_predict | aggregate | Fits a RANSAC robust regression over a window partition and returns the prediction for the current row. | NULL | [ransac_fit_predict(y, x) OVER (PARTITION BY g ORDER BY t)] |
| ransac_fit_predict_agg | aggregate | Fits RANSAC on training rows with a MAP of options and predicts all rows. | NULL | [ransac_fit_predict_agg(y, x, split_col, {'residual_threshold': 0.5})] |
| ransac_fit_predict_agg | aggregate | Fits RANSAC over a partition with a MAP of options and returns per-row predictions. | NULL | [ransac_fit_predict_agg(y, x, {'residual_threshold': 0.5, 'random_state': 42})] |
| ransac_fit_predict_agg | aggregate | Fits RANSAC using only training rows (split_col='train') and predicts all rows. | NULL | [ransac_fit_predict_agg(y, x, split_col)] |
| ransac_fit_predict_agg | aggregate | Fits a RANSAC robust regression over a partition and returns per-row predictions with confidence intervals. | NULL | [ransac_fit_predict_agg(y, x)] |
| ransac_fit_predict_by | table_macro | NULL | NULL | |
| residuals_diagnostics | scalar | Computes raw and standardized residuals, leverage, and Cook's distance from actuals and predictions. | NULL | [residuals_diagnostics(y, y_hat)] |
| residuals_diagnostics | scalar | Computes residual diagnostics including studentized residuals when feature matrix and residual standard error are supplied. | NULL | [residuals_diagnostics(y, y_hat, x, rse, true)] |
| 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 | [residuals_diagnostics_agg(y, y_hat, x)] |
| residuals_diagnostics_agg | aggregate | Aggregate version of residuals diagnostics: computes raw, standardized, studentized residuals and leverage from predicted and actual values. | NULL | [residuals_diagnostics_agg(y, y_hat)] |
| 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 | [ridge_fit(y, x, {'alpha': 1.0, 'compute_inference': true})] |
| ridge_fit | scalar | Fits a Ridge regression model with L2 regularization to the given response and feature data. | NULL | [ridge_fit(y, x)] |
| ridge_fit_agg | aggregate | Fits a Ridge regression model with L2 regularization and returns coefficients and fit statistics. | NULL | [ridge_fit_agg(y, x)] |
| ridge_fit_agg | aggregate | Fits a Ridge regression model with L2 regularization and returns coefficients and fit statistics. | NULL | [ridge_fit_agg(y, x, {'alpha': 1.0})] |
| ridge_fit_predict | aggregate | Fits a Ridge regression model over a window partition and returns predictions with confidence intervals. | NULL | [ridge_fit_predict(y, x)] |
| ridge_fit_predict | aggregate | Fits a Ridge regression model over a window partition and returns predictions with confidence intervals. | NULL | [ridge_fit_predict(y, x, {'null_policy': 'drop'})] |
| ridge_fit_predict_agg | aggregate | Fits Ridge regression on training rows with a MAP of options and predicts all rows. | NULL | [ridge_fit_predict_agg(y, x, split_col, {'null_policy': 'drop'})] |
| ridge_fit_predict_agg | aggregate | Fits Ridge regression over a partition and returns per-row predictions with confidence intervals. | NULL | [ridge_fit_predict_agg(y, x)] |
| 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 | [ridge_fit_predict_agg(y, x, {'null_policy': 'drop'})] |
| ridge_fit_predict_agg | aggregate | Fits Ridge regression using only training rows (split_col='train') and predicts all rows. | NULL | [ridge_fit_predict_agg(y, x, split_col)] |
| ridge_fit_predict_by | table_macro | NULL | NULL | |
| rls_fit | scalar | Fits a Recursive Least Squares (RLS) model to the given response and feature data. | NULL | [rls_fit(y, x)] |
| rls_fit | scalar | Fits a Recursive Least Squares (RLS) model with optional MAP of settings (fit_intercept, forgetting_factor, initial_p_diagonal). | NULL | [rls_fit(y, x, {'forgetting_factor': 0.99})] |
| rls_fit_agg | aggregate | Fits a Recursive Least Squares model and returns coefficients and fit statistics. | NULL | [rls_fit_agg(y, x)] |
| rls_fit_agg | aggregate | Fits a Recursive Least Squares model and returns coefficients and fit statistics. | NULL | [rls_fit_agg(y, x, {'forgetting_factor': 0.99})] |
| rls_fit_predict | aggregate | Fits a Robust Least Squares model over a window partition and returns predictions with confidence intervals. | NULL | [rls_fit_predict(y, x)] |
| rls_fit_predict | aggregate | Fits a Robust Least Squares model over a window partition and returns predictions with confidence intervals. | NULL | [rls_fit_predict(y, x, {'null_policy': 'drop'})] |
| rls_fit_predict_agg | aggregate | Fits Robust LS regression on training rows with a MAP of options and predicts all rows. | NULL | [rls_fit_predict_agg(y, x, split_col, {'null_policy': 'drop'})] |
| rls_fit_predict_agg | aggregate | Fits Robust LS regression over a partition and returns per-row predictions. | NULL | [rls_fit_predict_agg(y, x)] |
| rls_fit_predict_agg | aggregate | Fits Robust LS regression over a partition with a MAP of options and returns per-row predictions. | NULL | [rls_fit_predict_agg(y, x, {'null_policy': 'drop'})] |
| rls_fit_predict_agg | aggregate | Fits Robust LS regression using only training rows (split_col='train') and predicts all rows. | NULL | [rls_fit_predict_agg(y, x, split_col)] |
| rls_fit_predict_by | table_macro | NULL | NULL | |
| shapiro_wilk_agg | aggregate | Performs the Shapiro-Wilk test for normality on a sample. | NULL | [shapiro_wilk_agg(value)] |
| spearman_agg | aggregate | Computes Spearman's rank correlation coefficient and tests its significance. | NULL | [spearman_agg(x, y)] |
| spearman_agg | aggregate | Computes Spearman's rank correlation coefficient and tests its significance. | NULL | [spearman_agg(x, y, {'alternative': 'two_sided'})] |
| t_test_agg | aggregate | Performs a two-sample t-test (Welch or Student) comparing values between two groups, using default options. | NULL | [t_test_agg(value, group_id)] |
| t_test_agg | aggregate | Performs a two-sample t-test (Welch or Student) comparing values between two groups. | NULL | [t_test_agg(value, group_id, {'alternative': 'two_sided'})] |
| theil_sen_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 | [theil_sen_fit(y, x, {'random_state': 42, 'max_subpopulation': 5000})] |
| theil_sen_fit | scalar | Fits a Theil-Sen robust regression model. Returns coefficients and fit statistics as a struct. | NULL | [theil_sen_fit(y, x)] |
| theil_sen_fit_agg | aggregate | Fits a Theil-Sen robust regression model and returns coefficients and fit statistics as a struct. | NULL | [theil_sen_fit_agg(y, x)] |
| theil_sen_fit_agg | aggregate | Fits a Theil-Sen robust regression model and returns coefficients and fit statistics as a struct. | NULL | [theil_sen_fit_agg(y, x, {'random_state': 42, 'max_subpopulation': 5000})] |
| theil_sen_fit_predict | aggregate | Fits a Theil-Sen regression over a window with a MAP of options. | NULL | [theil_sen_fit_predict(y, x, {'random_state': 42}) OVER (…)] |
| theil_sen_fit_predict | aggregate | Fits a Theil-Sen robust regression over a window partition and returns the prediction for the current row. | NULL | [theil_sen_fit_predict(y, x) OVER (PARTITION BY g ORDER BY t)] |
| theil_sen_fit_predict_agg | aggregate | Fits Theil-Sen on training rows with a MAP of options and predicts all rows. | NULL | [theil_sen_fit_predict_agg(y, x, split_col, {'random_state': 42})] |
| theil_sen_fit_predict_agg | aggregate | Fits Theil-Sen over a partition with a MAP of options and returns per-row predictions. | NULL | [theil_sen_fit_predict_agg(y, x, {'random_state': 42})] |
| theil_sen_fit_predict_agg | aggregate | Fits Theil-Sen using only training rows (split_col='train') and predicts all rows. | NULL | [theil_sen_fit_predict_agg(y, x, split_col)] |
| theil_sen_fit_predict_agg | aggregate | Fits a Theil-Sen robust regression over a partition and returns per-row predictions with confidence intervals. | NULL | [theil_sen_fit_predict_agg(y, x)] |
| theil_sen_fit_predict_by | table_macro | NULL | NULL | |
| tost_correlation_agg | aggregate | Tests equivalence of a correlation to a reference value using the TOST procedure, using default options. | NULL | [tost_correlation_agg(x, y)] |
| tost_correlation_agg | aggregate | Tests equivalence of a correlation to a reference value using the TOST procedure. | NULL | [tost_correlation_agg(x, y, {'delta': 0.1})] |
| tost_paired_agg | aggregate | Tests equivalence of paired measurements using the TOST procedure, using default options. | NULL | [tost_paired_agg(x, y)] |
| tost_paired_agg | aggregate | Tests equivalence of paired measurements using the TOST procedure. | NULL | [tost_paired_agg(x, y, {'delta': 0.5})] |
| 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 | [tost_t_test_agg(value, group_id)] |
| tost_t_test_agg | aggregate | Tests equivalence of two groups using the Two One-Sided Tests (TOST) procedure with a t-test. | NULL | [tost_t_test_agg(value, group_id, {'delta': 1.0})] |
| 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 | [tweedie_fit_agg(y, x)] |
| 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 | [tweedie_fit_agg(y, x, {'power': 1.5, 'fit_intercept': true})] |
| vif | scalar | Computes Variance Inflation Factor (VIF) for each column of a feature matrix to detect multicollinearity. | NULL | [vif(x)] |
| vif_agg | aggregate | Aggregate version of VIF: computes Variance Inflation Factor for each feature from a column of feature vectors. | NULL | [vif_agg(x)] |
| wilcoxon_signed_rank_agg | aggregate | Performs the Wilcoxon signed-rank test for paired samples, using default options. | NULL | [wilcoxon_signed_rank_agg(x, y)] |
| wilcoxon_signed_rank_agg | aggregate | Performs the Wilcoxon signed-rank test for paired samples. | NULL | [wilcoxon_signed_rank_agg(x, y, {'alternative': 'two_sided'})] |
| wls_fit | scalar | Fits a WLS regression model with optional MAP of settings (fit_intercept, compute_inference, confidence_level, solver, hc_type). | NULL | [wls_fit(y, x, weights, {'compute_inference': true})] |
| wls_fit | scalar | Fits a Weighted Least Squares (WLS) regression model using per-observation weights. | NULL | [wls_fit(y, x, weights)] |
| wls_fit_agg | aggregate | Fits a Weighted Least Squares regression model and returns coefficients and fit statistics. | NULL | [wls_fit_agg(y, x, weight)] |
| wls_fit_agg | aggregate | Fits a Weighted Least Squares regression model and returns coefficients and fit statistics. | NULL | [wls_fit_agg(y, x, weight, {'fit_intercept': true})] |
| wls_fit_predict | aggregate | Fits a WLS regression model over a window partition using per-row weights and returns predictions. | NULL | [wls_fit_predict(y, x, weight)] |
| wls_fit_predict | aggregate | Fits a WLS regression model over a window partition using per-row weights and returns predictions. | NULL | [wls_fit_predict(y, x, weight, {'null_policy': 'drop'})] |
| wls_fit_predict_agg | aggregate | Fits WLS regression on training rows with weights and a MAP of options and predicts all rows. | NULL | [wls_fit_predict_agg(y, x, weights, split_col, {'null_policy': 'drop'})] |
| wls_fit_predict_agg | aggregate | Fits WLS regression over a partition using weights and returns per-row predictions. | NULL | [wls_fit_predict_agg(y, x, weights)] |
| wls_fit_predict_agg | aggregate | Fits WLS regression over a partition using weights with a MAP of options and returns per-row predictions. | NULL | [wls_fit_predict_agg(y, x, weights, {'null_policy': 'drop'})] |
| wls_fit_predict_agg | aggregate | Fits WLS regression using only training rows (split_col='train') with weights and predicts all rows. | NULL | [wls_fit_predict_agg(y, x, weights, split_col)] |
| wls_fit_predict_by | table_macro | NULL | NULL | |
| yuen_agg | aggregate | Performs Yuen's trimmed-means t-test, robust to outliers and non-normality, using default options. | NULL | [yuen_agg(value, group_id)] |
| yuen_agg | aggregate | Performs Yuen's trimmed-means t-test, robust to outliers and non-normality. | NULL | [yuen_agg(value, group_id, {'trim': 0.2})] |
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 | [] |