Package: sparseVCBART 1.0.0

Sameer K. Deshpande

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>.

Authors:Soham Ghosh [aut], Sameer K. Deshpande [cre, aut]

sparseVCBART_1.0.0.tar.gz
sparseVCBART_1.0.0.tar.gz(r-4.7-arm64)sparseVCBART_1.0.0.tar.gz(r-4.7-x86_64)sparseVCBART_1.0.0.tar.gz(r-4.6-arm64)sparseVCBART_1.0.0.tar.gz(r-4.6-x86_64)
sparseVCBART_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
sparseVCBART/json (API)

# Install 'sparseVCBART' in R:
install.packages('sparseVCBART', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ghoshstats/sparsevcbart/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

1.00 score 4 exports 3 dependencies

Last updated from:7b13f1bb4c. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK204
linux-devel-x86_64OK173
source / vignettesOK245
linux-release-arm64OK230
linux-release-x86_64OK174
wasm-releaseOK146

Exports:predict_betassparseVCBART_cssparseVCBART_indsummarize_beta

Dependencies:MASSRcppRcppArmadillo