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 |
| 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 |
| Reference manual: | BayesForge.html , BayesForge.pdf |
| Package source: | BayesForge_0.0.1.tar.gz |
| Windows binaries: | r-devel: BayesForge_0.0.1.zip, r-release: BayesForge_0.0.1.zip, r-oldrel: BayesForge_0.0.1.zip |
| 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 |
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