stats_ols | Ordinary least squares regression. | core |
stats_wls | Weighted least squares: each row weighted by the weights column (use 1/variance). | |
stats_gls | Generalized least squares with a known diagonal error covariance (one variance per row). | |
stats_glsar | Feasible GLS with AR(p) errors (iterated Cochrane-Orcutt): regression for time series whose residuals are autocorrelated. | |
stats_quantile_regression | Quantile regression at one or more quantiles (0.5 = median/least absolute deviation): how the regressors move each part of the response’s distribution. | |
stats_robust_regression | Robust linear regression by M-estimation (iteratively reweighted least squares): coefficients that outliers cannot drag. | |
stats_recursive_ls | Recursive least squares: the coefficients re-estimated as each observation is added, with the CUSUM and CUSUM-of-squares statistics for parameter stability. | |
stats_rolling_regression | Rolling-window least squares: coefficients (e.g. a stock’s beta) and R² re-estimated over a moving window. | |
stats_beta_regression | Beta regression for a response strictly between 0 and 1 (rates, shares, recovery rates): a logit-link mean model with a precision parameter. | |
