Behavioral macromodels have gained considerable attention due to the difficulty to perform numerical simulations of complex systems, both due to the long runtime and the excessive memory requirements. Suitable Model Order Reduction techniques, either via truncation or projection of large-scale first-principle formulations, or via data-driven identification from input-output response measurements, have been proven adequate for the derivation of compact macromodels, especially in the field of Electronic Design Automation. Recently, also the use of parametrized macromodels has become popular, because it enables analysis and optimization with respect to physical or geometrical parameters. Macromodel extration in this case is particularly challenging, since model complexity scales exponentially with the number of parameters, resulting in an expensive increase of the costs.