Variance-Guided Regression Improving Upon OLS and ANOVA // Fits variance-guided linear regression models that provide an alternative to ordinary least squares (OLS) for general linear- model design matrices, including ANOVA-style encodings. The methods use an iteratively reweighted least squares estimator or an iteratively reweighted lasso estimator and implement the global linear mean-variance model from the associated 2026 Statistics in Medicine article doi:10.1002/sim.70632. Under the assumptions in that paper, the estimator matches the homoscedastic baseline in population predictive quasi-risk when variance is constant and improves on it when the variance depends on covariates. The grouping-based nonlinear prediction extension from Section 3 is available in the development version on GitHub.