TuringLang / JuliaBUGS.jl / 17547593305
84%

DEFAULT BRANCH: main
Ran
Jobs 1
Files 31
Run time 1min
Badge
Embed ▾
README BADGES
x

If you need to use a raster PNG badge, change the '.svg' to '.png' in the link

Markdown

Textile

RDoc

HTML

Rst
coverage: 83.204%. Remained the same
17547593305

push

github

web-flow 
R interface for JuliaBUGS.jl from the `rjuliabugs` package (#389)

# Final Report: GSoC '25  
**Contributor Name**: Mateus Maia  
**Organization**: Turing.jl  
**Mentors**: Xianda Sun, Robert Goudie  
**Project**: `rjuliabugs` – R Interface for JuliaBUGS.jl  

## Projects  
This PR has been created as part of the Google Summer of Code in the
"rjuliabugs – R Interface for JuliaBUGS.jl" project.
I have developed the `rjuliabugs` R package, which provides a bridge
between R and JuliaBUGS, the BUGS-style Bayesian modeling interface
developed in Julia. With this package, R users can run BUGS models
through JuliaBUGS, leveraging advanced inference engines such as
Hamiltonian Monte Carlo (HMC) while staying within the R environment.

The package integrates seamlessly with R’s post-processing ecosystem,
including tools like `bayesplot`, `posterior`, and `coda`, allowing for
diagnostics and visualization of Bayesian models directly from R.

The final result can be find in this repository:

https://github.com/MateusMaiaDS/rjuliabugs

and with a clear documentation through:

https://mateusmaiads.github.io/rjuliabugs/

## Features  
Able to perform the following:  
- Run BUGS models using JuliaBUGS from R  
- Interface with R visualization and post-processing tools (`bayesplot`,
`posterior`, `coda`)
- Seamless translation of BUGS code into Julia for efficient execution  

## Constraints and Future Improvement  
Currently, `rjuliabugs` requires an initial setup (`setup_juliaBUGS()`)
for Julia dependencies and is optimized for running one model at a time.
Future improvements could include:
- Improved progress bars for the sampler for running `rjuliabugs` inside
the R console inside the R studio.
- Keep adding more examples of models that can be used using JuliaBUGS
within the R library.
- Although `JuliaCall` can be efficient, a better integration between
and Julia may upcome in the future which may speed-up the initialization
when doing (`setup_juliaBUGS()`).

## ... (continued)

3106 of 3733 relevant lines covered (83.2%)

131800.01 hits per line

Jobs
ID Job ID Ran Files Coverage
1 17547593305.1 31
83.2
 GitHub Action Run
Source Files on build 17547593305