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Showing content from http://cran.rstudio.com/web/packages/Rcpp/../RcppZiggurat/../Rcpp/../pg/readme/README.html below:

README

The goal of pg is to provide both R and C++ header access to the Polya Gamma distribution sampling routine.

Installation

You can install the development version of pg from GitHub with:

# install.packages("devtools")
devtools::install_github("tmsalab/pg")

Usage

Let X be a Polya Gamma Distribution denoted by PG(h, z), where h is the âshapeâ parameter and z is the âscaleâ parameter. Presently, the following sampling cases are enabled:

h > 170: Normal approximation method
h <= 170 and h > 13: Saddlepoint method
h = 1 or h = 2: Devroye method
h > 0: Sum of gammas method.
h < 0: Result is automatically set to zero.

Not implemented:

h <= 13 and h > 1: Alternative method (waiting for verification)

The package structure allows for the sampling routines to be accessed either via C++ or through R. The return type can be either a single value or a vector. When repeat sampling is needed with the same b and c, please use the vectorized sampler.

Sampling with C++

Using the sampling routine in C++ through a standalone .cpp file requires either the rpg_scalar_hybrid(), rpg_vector_hybrid(), or rpg_hybrid() function to be accessed in the pg C++ namespace. Each of these functions will automatically select the appropriate algorithm based on criteria discussed previously.

#include <pg.h>
// [[Rcpp::depends(RcppArmadillo, pg)]]

// [[Rcpp::export]]
double rpg_scalar(const double h, const double z) {
  return pg::rpg_scalar_hybrid(h, z);
}

// [[Rcpp::export]]
arma::vec rpg_hybrid(const arma::vec& h, const arma::vec& z) {
  return pg::rpg_hybrid(h, z);
}

// [[Rcpp::export]]
arma::vec rpg_vector(unsigned int n, const double h, const double z) {
  return pg::rpg_vector_hybrid(n, h, z);
}

For use within an R package, include a the pg package name in the DESCRIPTION file. From there, include the pg.h header in a similar manner to the stand-alone C++ example.

LinkingTo: 
    Rcpp,
    RcppArmadillo
    pg

Sampling with R

For use within an R file, you can do:

# Number of observations to sample
n = 4
# Select the PG(h, z) values
h = 1; z = 0.5

# Set a seed for reproducibility
set.seed(141)

# Sample a single observation
pg::rpg_scalar(h, z)
#> [1] 0.05752942

# Set a seed for reproducibility
set.seed(141)

# Sample a vector of observations
pg::rpg_vector(n, h, z)
#>            [,1]
#> [1,] 0.05752942
#> [2,] 0.38752679
#> [3,] 0.38710433
#> [4,] 0.18847913