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varGuid: R and Python Implementations of Variance-Guided Regression for Robust Effect-Size Estimation in Linear
Sibei Liu1, Zihao Wang1, Min Lu1
1Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA.
Applied Psychological Measurement
|July 23, 2026
Summary
Variance-guided regression offers improved statistical efficiency for linear models when outcome variance changes. New R and Python packages (varGuid, varguid) provide accessible tools for this robust analysis in measurement research.
Area of Science:
- Statistics
- Psychometrics
- Data Analysis
Background:
- Linear regression and ANOVA are standard in psychological measurement.
- Heteroscedasticity (changing outcome variance) can reduce statistical efficiency and compromise inference.
- Existing methods may struggle when variance is not constant across groups or covariates.
Purpose of the Study:
- Introduce varGuid (R) and varguid (Python) for variance-guided regression.
- Provide open-source implementations for robust linear modeling.
- Enhance statistical efficiency and inference under heteroscedasticity.
Main Methods:
- Variance-guided regression estimates a covariate-dependent mean-variance relationship.
- Iterative reweighting adjusts the original mean model.
- Uses iteratively reweighted least squares or lasso for analysis.
- Retains original design matrix and outcome scale for comparable coefficients.
Main Results:
- The method improves upon the homoscedastic baseline when variance depends on covariates.
- Regression coefficients and ANOVA contrasts remain directly comparable to conventional estimates.
- Provides baseline and variance-guided predictions and heteroscedasticity-consistent summaries.
Conclusions:
- varGuid and varguid offer accessible tools for variance-guided regression.
- These packages are valuable for measurement research where homogeneity of variance is uncertain.
- The approach enhances robustness by relaxing the homoscedasticity assumption.
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