Variance-Guided Regression Improving Upon OLS and ANOVA R package varGuid version 0.1.5 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. Software Depends: R (>= 3.5.0) Imports: glmnet, lmtest, sandwich Min Lu <luminwin@gmail.com> Comprehensive R Archive Network (CRAN) Sibei Liu [aut], Min Lu [aut, cre] GPL (>= 2) 2026-06-18 application/tgz https://CRAN.R-project.org/package=varGuid doi:10.32614/CRAN.package.varGuid