Using the Stan Math C++ Library

Stan Development Team

2019-01-28

The StanHeaders package contains no R functions. To use the Stan Math Library in other packages, it is sufficient to specify

LinkingTo: StanHeaders (>= 2.18.1)

in the DESCRIPTION file of another package and

CXX_STD = CXX14

in the src/Makevars and src/Makevars.win files. If, in addition, the other package needs to utilize the MCMC, optimization, variational inference, or parsing facilities of the Stan Library, then it is also necessary to include the src directory of StanHeaders in the other package’s PKG_CPPFLAGS in the src/Makevars and src/Makevars.win files with something like

STANHEADERS_SRC = $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "message()" \
    -e "cat(system.file('include', 'src', package = 'StanHeaders', mustWork = TRUE))" \
    -e "message()" | grep "StanHeaders")
PKG_CPPFLAGS = -I"$(STANHEADERS_SRC)"

in which case there should be a SystemRequirements: GNU make in the package’s DESCRIPTION file.

The following is a minimal example of using the Stan Math library via Rcpp::sourceCpp: to minimize the function \(\left(\mathbf{x} - \mathbf{a}\right)^\top \left(\mathbf{x} - \mathbf{a}\right)\)

// [[Rcpp::depends(BH)]]
// [[Rcpp::depends(RcppEigen)]]
// [[Rcpp::depends(StanHeaders)]]
#include <Rcpp.h>
#include <RcppEigen.h>
#include <stan/math.hpp>  // pulls in everything; could be more specific with includes

// [[Rcpp::export]]
double f(Eigen::VectorXd x, Eigen::VectorXd a) {  // objective function in doubles
  return stan::math::dot_self( (x - a).eval() );  // dot_self() is a dot product with self
}
stan::math::var f(Eigen::Matrix<stan::math::var, Eigen::Dynamic, 1> x, Eigen::VectorXd a) {
  return stan::math::dot_self( (x - stan::math::to_var(a)).eval() );  // same but with vars
}
// [[Rcpp::export]]
std::vector<double> g(Eigen::VectorXd x, Eigen::VectorXd a) {  // gradient by AD using Stan
  auto x_var = stan::math::to_var(x);
  std::vector<stan::math::var> theta;
  std::vector<double> grad;
  for (int k = 0; k < x.rows(); k++) theta.push_back(x_var.coeff(k));
  stan::math::var lp = f(x_var, a);
  lp.grad(theta, grad);
  return grad;
}
optim(rnorm(3), fn = f, gr = g, a = c(1, 2, 3), method = "BFGS")$par  # Rcpp exported f and g
#> [1] 1 2 3