BayesForge: Bayesian Inference using 'numpyro' and 'XLA'

A high-performance probabilistic programming library that aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU (Central Processing Unit), GPU (Graphics Processing Unit), or TPU (Tensor Processing Unit) using 'JAX' (Just-In-Time compiled Accelerated linear algebra) as the computational backend: Sosa (2026) <doi:10.64898/2026.01.19.700318>.

Version:	0.0.1
Depends:	R (≥ 3.5.0)
Imports:	reticulate, abind, methods
Suggests:	testthat (≥ 3.0.0), brms, rstan
Published:	2026-06-08
DOI:	10.32614/CRAN.package.BayesForge (may not be active yet)
Author:	Sebastian Sosa [aut, cre]
Maintainer:	Sebastian Sosa <bf at s-sosa.com>
License:	GPL (≥ 3)
URL:	https://s-sosa.com/BF/
NeedsCompilation:	no
SystemRequirements:	Python (>= 3.6), 'bayesforge' python library.
Materials:	README
CRAN checks:	BayesForge results

Documentation:

Reference manual:

BayesForge.html , BayesForge.pdf

Downloads:

Package source:	BayesForge_0.0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: BayesForge_0.0.1.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): BayesForge_0.0.1.tgz, r-oldrel (arm64): BayesForge_0.0.1.tgz, r-release (x86_64): BayesForge_0.0.1.tgz, r-oldrel (x86_64): BayesForge_0.0.1.tgz

Linking:

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