In recent years, numerical simulations have become a key component of the design workflow in the electronics industry, driven by the need for reduced time-to-market and the growing complexity of modern systems. However, those simulations are increasingly becoming the bottleneck of the workflow since a system-level, fully detailed simulation from the physics laws is now prohibitively expensive both from the computational and required time standpoint. Motivated by this industry problem, the use of macromodels, reduced-complexity behavioral descriptions of systems, has attracted growing interest from the academic community. In particular, research efforts have focused on incorporating an explicit parametric dependency into those reduced-order models in a way that preserves their computational efficiency while mitigating, at least partially, the effects of the curse of dimensionality.