Fits sparse linear varying coefficient models (VCMs),
which assert a linear relationship between an outcome and several covariates
that is allowed to change as functions of additional variables known as effect modifiers.
Designed for high-dimensional settings where the number of covariates (i.e., number of slopes)
is comparable to or larger than the number of observations.
Approximates the coefficient functions using a version of Bayesian Additive Regression Trees
that can perform global-local shrinkage.
For more details see Ghosh, Bhogale, and Deshpande (2026+)