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https://github.com/roualdes/bridgestan
BridgeStan provides efficient in-memory access through Python, Julia, and R to the methods of a Stan model.
https://github.com/roualdes/bridgestan
c cpp julia python r stan
Last synced: 4 days ago
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BridgeStan provides efficient in-memory access through Python, Julia, and R to the methods of a Stan model.
- Host: GitHub
- URL: https://github.com/roualdes/bridgestan
- Owner: roualdes
- License: bsd-3-clause
- Created: 2022-09-06T17:26:14.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-13T08:11:13.000Z (9 months ago)
- Last Synced: 2024-04-14T12:48:45.331Z (9 months ago)
- Topics: c, cpp, julia, python, r, stan
- Language: Python
- Homepage: https://roualdes.github.io/bridgestan
- Size: 4.42 MB
- Stars: 79
- Watchers: 6
- Forks: 12
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE-CODE
- Citation: CITATION.cff
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README
# BridgeStan
[](https://roualdes.github.io/bridgestan/) [](https://doi.org/10.21105/joss.05236) [](https://github.com/roualdes/bridgestan/actions/workflows/main.yaml)
BridgeStan provides efficient in-memory access through Python, Julia,
Rust, and R to the methods of a [Stan](https://mc-stan.org) model, including
log densities, gradients, Hessians, and constraining and unconstraining
transforms. The motivation was developing inference algorithms in
higher-level languages for arbitrary Stan models.
Stan is a probabilistic programming language for coding statistical
models. For an introduction to what can be coded in Stan, see the
[*Stan User's Guide*](https://mc-stan.org/docs/stan-users-guide/index.html).
BridgeStan is currently shipping with Stan version 2.36.0
Documentation is available at https://roualdes.github.io/bridgestan/
#### Compatibility
BridgeStan has been tested with the following operating system and C++
compiler combinations.
* Linux: Ubuntu 20.04 with gcc 9.4.0
* Apple: Mac OS X 12.2 with Apple clang 11.0.3
* Microsoft: Windows 10 with gcc MSYS2 5.3.0
## Installing BridgeStan
Installing the core of BridgeStan is as simple as
[installing a C++ toolchain](https://mc-stan.org/docs/cmdstan-guide/installation.html#cpp-toolchain)
(libraries, compiler, and the `make` command), and downloading this
repository. To download the latest development version, you can run
```shell
git clone --recurse-submodules https://github.com/roualdes/bridgestan.git
```
For a full guide on installing, configuring, and using BridgeStan, consult the
[documentation](https://roualdes.github.io/bridgestan/latest/getting-started.html)
## Using BridgeStan
### Compiling a Stan program
To compile the Stan model in `test_models/multi/multi.stan` to a binary
shared object (`.so` file), use the following.
```
$ cd bridgestan
$ make test_models/multi/multi_model.so
```
This will require internet access the first time you run it in order
to download the appropriate Stan compiler for your platform into
`/bin/stanc[.exe]`
### Example programs
This repository includes examples of calling Stan through BridgeStan
in Python, Julia, R, Rust, and C.
* From Python: [`example.py`](python/example.py)
* From Julia: [`example.jl`](julia/example.jl)
* From R: [`example.r`](R/example.R)
* From Rust: [`example.rs`](rust/examples/example.rs)
* From C: [`example.c`](c-example/example.c)
Examples of other functionality can be found in the `test` folder for each interface.
## Software using BridgeStan
We are aware of the following projects using BridgeStan.
### Julia
- https://github.com/sethaxen/StanLogDensityProblems.jl
- https://github.com/Julia-Tempering/Pigeons.jl
- https://github.com/TuringLang/TuringBenchmarking.jl
### Python
- https://github.com/pymc-devs/nutpie
- https://github.com/UoL-SignalProcessingGroup/retrospectr
- https://github.com/UoL-SignalProcessingGroup/SMC-NUTS
### R
- https://github.com/JTorgander/hmc-sandbox
## Research using BridgeStan
If you use BridgeStan in your research, please consider citing [our JOSS paper](https://joss.theoj.org/papers/10.21105/joss.05236)
and letting us know so we can list your project here.
- [*Verified Density Compilation for a Probabilistic Programming Language*](https://doi.org/10.1145/3591245)
- [*Variational Inference with Gaussian Score Matching*](https://arxiv.org/pdf/2307.07849.pdf)
- [*Stein Π-Importance Sampling*](https://arxiv.org/pdf/2305.10068.pdf)
- [*Batch and match: black-box variational inference with a score-based divergence*](https://arxiv.org/abs/2402.14758)
## Acknowledgements
The Julia and Python APIs were derived from the
[Stan Model Server](https://github.com/bob-carpenter/stan-model-server/)
API, which in turn was derived from
[ReddingStan](https://github.com/dmuck/redding-stan).
Thanks to Sebastian Weber (GitHub [@wds15](https://github.com/wds15))
for enabling multi-threaded calls from Julia to a single Stan model instance.
Thanks to Adrian Seyboldt (GitHub [@aseyboldt](https://github.com/aseyboldt))
for providing the Rust wrapper.