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iBART: Iterative Bayesian Additive Regression Trees Descriptor Selection Method

A statistical method based on Bayesian Additive Regression Trees with Global Standard Error Permutation Test (BART-G.SE) for descriptor selection and symbolic regression. It finds the symbolic formula of the regression function y=f(x) as described in Ye, Senftle, and Li (2023) <doi:10.48550/arXiv.2110.10195>.

Version: 1.0.0
Depends: R (≥ 4.0.0)
Imports: bartMachine (≥ 1.2.6), glmnet (≥ 4.1-1), foreach, stats
Suggests: knitr, rmarkdown, ggplot2, ggpubr
Published: 2023-11-14
DOI: 10.32614/CRAN.package.iBART
Author: Shengbin Ye ORCID iD [aut, cre, cph], Meng Li [aut]
Maintainer: Shengbin Ye <sy53 at rice.edu>
BugReports: https://github.com/mattsheng/iBART/issues
License: GPL (≥ 3)
URL: https://github.com/mattsheng/iBART
NeedsCompilation: no
SystemRequirements: Java (>= 8.0)
Materials: README NEWS
CRAN checks: iBART results

Documentation:

Reference manual: iBART.pdf
Vignettes: Single-Atom Catalysis Data Analysis
Complex Model Simulation

Downloads:

Package source: iBART_1.0.0.tar.gz
Windows binaries: r-devel: iBART_1.0.0.zip, r-release: iBART_1.0.0.zip, r-oldrel: iBART_1.0.0.zip
macOS binaries: r-release (arm64): iBART_1.0.0.tgz, r-oldrel (arm64): iBART_1.0.0.tgz, r-release (x86_64): iBART_1.0.0.tgz, r-oldrel (x86_64): iBART_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=iBART to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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