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:
sparseVCBART_1.0.0.tar.gz
sparseVCBART_1.0.0.zip(r-4.7)sparseVCBART_1.0.0.zip(r-4.6)sparseVCBART_1.0.0.zip(r-4.5)
sparseVCBART_1.0.0.tgz(r-4.6-x86_64)sparseVCBART_1.0.0.tgz(r-4.6-arm64)sparseVCBART_1.0.0.tgz(r-4.5-x86_64)sparseVCBART_1.0.0.tgz(r-4.5-arm64)
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://ghoshstats.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ghoshstats/sparsevcbart/issues
Last updated from:eaad5d26f9. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 171 | ||
| linux-devel-x86_64 | OK | 151 | ||
| source / vignettes | OK | 223 | ||
| linux-release-arm64 | OK | 182 | ||
| linux-release-x86_64 | OK | 154 | ||
| macos-release-arm64 | OK | 207 | ||
| macos-release-x86_64 | OK | 501 | ||
| macos-oldrel-arm64 | OK | 162 | ||
| macos-oldrel-x86_64 | OK | 321 | ||
| windows-devel | OK | 178 | ||
| windows-release | OK | 165 | ||
| windows-oldrel | OK | 152 | ||
| wasm-release | OK | 137 |
Exports:predict_betassparseVCBART_cssparseVCBART_indsummarize_beta
Dependencies:MASSRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute posterior predictive evaluates of covariate effect functions. | predict_betas |
| Fit a sparse VCBART model with compound symmetry error structure | sparseVCBART_cs |
| Fit a sparse VCBART model with independent error structure | sparseVCBART_ind |
| Compute posterior mean and 95% credible interval for evaluations of each coefficient function. | summarize_beta |