R for testing and simulation — expose_stan_functions" />

The Stan modeling language allows users to define their own functions in a functions block at the top of a Stan program. The expose_stan_functions utility function uses sourceCpp to export those user-defined functions to the specified environment for testing inside R or for doing posterior predictive simulations in R rather than in the generated quantities block of a Stan program.

expose_stan_functions(stanmodel, includes = NULL,
                        show_compiler_warnings = FALSE, ...)
  get_rng(seed = 0L)



A stanmodel object, a stanfit object, a list produced by stanc or the path to a Stan program (.stan file). In any of these cases, the underlying Stan program should contain a non-empty functions block.


If not NULL (the default), then a character vector of length one (possibly containing one or more "\n") of the form '#include "/full/path/to/my_header.hpp"', which will be inserted into the C++ code in the model's namespace and can be used to provide definitions of functions that are declared but not defined in stanmodel


Logical scalar defaulting to FALSE that controls whether compiler warnings, which can be numerous and have never been relevant, are shown


An integer vector of length one indicating the state of Stan's pseudo-random number generator


Further arguments passed to sourceCpp.


The expose_stan_functions function requires as much compliance with the C++14 standard as is implemented in the RTools toolchain for Windows. On Windows, you will likely need to specify CXX14 = g++ -std=c++1y in the file whose path is normalizePath("~/.R/Makevars") in order for expose_stan_functions to work. Outside of Windows, the necessary compiler flags are set programatically, which is likely to suffice.

There are a few special types of user-defined Stan functions for which some additional details are relevant:

(P)RNG functions

If a user-defined Stan function ends in _rng, then it can use the Boost pseudo-random number generator used by Stan. When exposing such functions to R, base_rng__ and pstream__ arguments will be added to the formals. The base_rng__ argument should be passed the result of a call to get_rng (perhaps specifying its seed argument for reproducibility) and the pstream__ should be passed the result of a call to get_stream, which can be used to see the result of print and reject calls in the user-defined Stan functions. These arguments default to get_stream() and get_rng() respectively.

LP functions

If a user-defined Stan function ends in _lp, then it can modify the log-probability used by Stan to evaluate Metropolis proposals or as an objective function for optimization. When exposing such functions to R, a lp__ argument will be added to the formals. This lp__ argument defaults to zero, but a double precision scalar may be passed to this argument when the function is called from R. Such a user-defined Stan function can terminate with return target(); or can execute print(target()); to verify that the calculation is correct.


The names of the new functions in env are returned invisibly.

See also

sourceCpp and the section in the Stan User Manual on user-defined functions


if (FALSE) { model_code <- ' functions { real standard_normal_rng() { return normal_rng(0,1); } } ' expose_stan_functions(stanc(model_code = model_code)) standard_normal_rng() PRNG <- get_rng(seed = 3) o <- get_stream() standard_normal_rng(PRNG, o) }