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sparseVCBART  

Sparse Varying Coefficient BART with Global-Local Priors"
View on CRAN: Click here


Download and install sparseVCBART package within the R console
Install from CRAN:
install.packages("sparseVCBART")

Install from Github:
library("remotes")
install_github("cran/sparseVCBART")

Install by package version:
library("remotes")
install_version("sparseVCBART", "1.0.0")



Attach the package and use:
library("sparseVCBART")
Maintained by
Sameer K. Deshpande
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-19
Latest Update: 2026-05-19
Description:
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>.
How to cite:
Sameer K. Deshpande (2026). sparseVCBART: Sparse Varying Coefficient BART with Global-Local Priors". R package version 1.0.0, https://cran.r-project.org/web/packages/sparseVCBART. Accessed 30 Jul. 2026.
Previous versions and publish date:
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Complete documentation for sparseVCBART
Functions, R codes and Examples using the sparseVCBART R package
Full sparseVCBART package functions and examples
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