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VCBART: Fit Varying Coefficient Models with Bayesian Additive RegressionTrees

Fits linear varying coefficient (VC) models, which assert a linear relationship between an outcome and several covariates but allow that relationship (i.e., the coefficients or slopes in the linear regression) to change as functions of additional variables known as effect modifiers, by approximating the coefficient functions with Bayesian Additive Regression Trees. Implements a Metropolis-within-Gibbs sampler to simulate draws from the posterior over coefficient function evaluations. VC models with independent observations or repeated observations can be fit. For more details see Deshpande et al. (2024) <doi:10.1214/24-BA1470>.

Version:	1.2.4
Imports:	Rcpp,MASS
LinkingTo:	Rcpp,RcppArmadillo
Published:	2025-12-09
DOI:	10.32614/CRAN.package.VCBART
Author:	Sameer K. Deshpande [aut, cre], Ray Bai [aut], Cecilia Balocchi [aut], Jennifer Starling [aut], Jordan Weiss [aut]
Maintainer:	Sameer K. Deshpande <sameer.deshpande at wisc.edu>
License:	GPL (≥ 3)
URL:	https://github.com/skdeshpande91/VCBART
NeedsCompilation:	yes
Citation:	VCBART citation info
CRAN checks:	VCBART results

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