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
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-prerel: iBART_1.0.0.zip, r-release: iBART_1.0.0.zip, r-oldrel: iBART_1.0.0.zip
macOS binaries: r-prerel (arm64): iBART_1.0.0.tgz, r-release (arm64): iBART_1.0.0.tgz, r-oldrel (arm64): iBART_1.0.0.tgz, r-prerel (x86_64): iBART_1.0.0.tgz, r-release (x86_64): iBART_1.0.0.tgz

Linking:

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