Unfortunately no, but a few brave souls have done so. It is slowly getting easier.
As you mentioned, there are pros and cons to inventing the Stan DSL. Originally, we wanted a language that was not too different from the BUGS language because the BUGS family was what most applied Bayesians were using…
The number one thing in my opinion is that Stan's algorithm(s) for drawing from a posterior distribution produce samples that have much less dependence among adjacent draws than other simpler MCMC algorithms like…
Also, the rstanarm[1] R package (disclaimer: that I co-wrote) will be released this month, which does not require the user to write any code in the Stan language. Instead, you specify the likelihood of the data (for a…
Unfortunately no, but a few brave souls have done so. It is slowly getting easier.
As you mentioned, there are pros and cons to inventing the Stan DSL. Originally, we wanted a language that was not too different from the BUGS language because the BUGS family was what most applied Bayesians were using…
The number one thing in my opinion is that Stan's algorithm(s) for drawing from a posterior distribution produce samples that have much less dependence among adjacent draws than other simpler MCMC algorithms like…
Also, the rstanarm[1] R package (disclaimer: that I co-wrote) will be released this month, which does not require the user to write any code in the Stan language. Instead, you specify the likelihood of the data (for a…