sparseVCBART: Sparse Varying Coefficient BART with Global-Local Priors"

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+) <doi:10.48550/arXiv.2510.08204>.

Version:	1.0.0
Imports:	Rcpp, MASS
LinkingTo:	Rcpp, RcppArmadillo
Published:	2026-05-19
DOI:	10.32614/CRAN.package.sparseVCBART (may not be active yet)
Author:	Soham Ghosh [aut], Sameer K. Deshpande [cre, aut]
Maintainer:	Sameer K. Deshpande <sameer.deshpande at wisc.edu>
License:	GPL (≥ 3)
URL:	https://github.com/ghoshstats/sparseVCBART
NeedsCompilation:	yes
CRAN checks:	sparseVCBART results

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