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.