bengHN

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 (if they weren't using some specialized MCMC algorithm for their problem).

And programmatic manipulation (while not impossible) of a Stan program was not high on our priority list. Since the underlying language is C++, you can bypass the Stan language and write (or generate) the C++ directly with a lot of effort. It uses standard (including Eigen) data structures.

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 Metropolis-Hastings or Gibbs samplers. Since it is often difficult to answer the question "How much dependence is too much in practice?", it is prudent to use the algorithm that yields the least dependence because the effective sample size (from the posterior distribution) per unit of wall time will usually be greater. PyMC3 has started to incorporate some of Stan's algorithms, although their implementations are not as far along.

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 few popular regression-type models) using conventional R syntax and utilize Stan's algorithms and optional priors on the parameters to draw from the posterior distribution. In the demos/ directory of [1], we have replicated most of the first half of Gelman & Hill's textbook and are starting on the second half, which heavily utilizes our stan_glmer() function that is compatible with the syntax of the glmer() function in the lme4 R package.

[1] https://github.com/stan-dev/rstanarm/