User-facing R functions are provided to parse, compile, test,
estimate, and analyze Stan models by accessing the header-only Stan library
provided by the 'StanHeaders' package. The Stan project develops a probabilistic
programming language that implements full Bayesian statistical inference
via Markov Chain Monte Carlo, rough Bayesian inference via 'variational'
approximation, and (optionally penalized) maximum likelihood estimation via
optimization. In all three cases, automatic differentiation is used to quickly
and accurately evaluate gradients without burdening the user with the need to
derive the partial derivatives.