rbartpackages

Python wrappers of R BART (Bayesian Additive Regression Trees) packages, built on rpy2.

rbartpackages lets you call several R BART implementations from Python with a uniform, lightly-typed interface: arguments are converted to R, the fitted R object’s components become Python attributes, and the original R documentation is attached to each wrapper class. It currently wraps:

• BART

• BART3 (the development superset of BART)

• bartMachine

• dbarts

• missBART (multivariate BART with non-ignorable missing responses)

Installation

pip install rbartpackages

You also need R with the package(s) you want to use installed (BART, dbarts, bartMachine from CRAN; BART3 from rsparapa/bnptools and missBART from yongchengoh/missBART on GitHub). bartMachine additionally requires Java. Optional extras pandas, polars, and jax enable passing those array/frame types directly. See the documentation for details.

Usage

import numpy as np
from rbartpackages import BART3

x_train = np.random.randn(100, 5)
y_train = x_train[:, 0] + 0.1 * np.random.randn(100)

bart = BART3.gbart(x_train=x_train, y_train=y_train, ndpost=200)
y_pred = bart.predict(x_train)  # shape (ndpost, n)

R argument names with dots are passed with underscores (x.train → x_train).

Links

• Documentation (latest release)

• Documentation (development version)

• Repository

• List of BART packages (maintained in the bartz docs)

These wrappers originated in the bartz project, where they are used to validate against reference R implementations.