varGuid: 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.
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