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Regression models with varying coefficients changing over certain underlying covariates offer great flexibility in capturing a functional relationship between the response and other covariates.This article extends such regression models to include random effects and to allow for correlation and heteroscedasticity in within-group variation,and proposes an efficient new data-driven method to estimate varying regression coefficients via reparameterization and partial collapse.