bartisan: Generalized Bayesian Additive Regression Trees

Fits Bayesian additive regression trees (BART) models for likelihoods outside the conditionally conjugate Gaussian case, using the Laplace-approximation reversible-jump sampler of Linero (2025) <doi:10.1080/01621459.2024.2337156>. Supported response families include Gaussian, binomial, Poisson, negative binomial, gamma, beta, ordered beta, ordinal (cumulative link), multinomial, zero-inflated counts, a Tweedie compound Poisson for a non-negative response with a point mass at zero, accelerated failure time and proportional hazards models for right-censored survival data, location-scale regression, and a Dirichlet process mixture for the error distribution. Decision rules may be hard, as in standard BART, or soft, as in the SoftBart model of Linero and Yang (2018) <doi:10.1111/rssb.12293>, which yields smoother fits. The interface follows that of glm(), so that a model is specified with a formula, a data frame, and a family.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	arg (≥ 0.2.1), cli (≥ 3.6.4), rlang (≥ 1.2.0), Matrix, Rcpp (≥ 1.1.2), parallel, reformulas (≥ 0.4.4), ggplot2 (≥ 4.0.1), stats, utils
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	bayesplot, cobalt, collapse, future, future.apply, insight, knitr, loo, MASS, marginaleffects, nlme, lme4, patrick (≥ 0.3.0), performance, posterior, progressr, rmarkdown, rstanarm, rstantools, survival, testthat (≥ 3.0.0), WeightIt
Published:	2026-10-28
DOI:	10.32614/CRAN.package.bartisan (may not be active yet)
Author:	Noah Greifer [aut, cre, cph], Antonio R. Linero [ctb, cph] (Author of FlexBart, the reference implementation of Linero (2025), from which the MCMC engine is adapted)
Maintainer:	Noah Greifer <noah.greifer at gmail.com>
BugReports:	https://github.com/ngreifer/bartisan/issues
License:	GPL-2
URL:	https://github.com/ngreifer/bartisan, https://ngreifer.github.io/bartisan/
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	bartisan results

Documentation:

Reference manual:

bartisan.html , bartisan.pdf

Vignettes:

Getting Started with bartisan (source) Causal Inference with BART (source) Model Comparison (source) Checking Convergence and Fit (source) Effects, Curves, and Interactions (source) Response Families in bartisan (source) Frequently Asked Questions (source) Generalized BART with bartisan (source) Variable Importance (source) Survival Analysis in bartisan (source) Varying Coefficients and Random Intercepts (source)

Downloads:

Package source:	bartisan_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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