C++ API

The RNGs and distributions functions can also be used from C++ at various levels of abstraction. Technically there are three ways to make use of dqrng at the C++ level:

use // [[Rcpp::depends(dqrng)]] together with Rcpp::sourceCpp()
use Rcpp::cppFunction(depends = "dqrng", ...)
use an R package with LinkingTo: dqrng

The following functions are also available if you include dqrng.h. Note that the scalar function dqrng::runif, dqrng::rnorm, and dqrng::rexp have been deprecated and will be removed in a future release. Please use the more flexible and faster dqrng::random_64bit_accessor together with variate<Dist>() instead.

Setting seed and RNG type

void dqrng::dqset_seed(Rcpp::IntegerVector seed, 
                       Rcpp::Nullable<Rcpp::IntegerVector> stream = R_NilValue)
void dqrng::dqRNGkind(std::string kind, const std::string& normal_kind = "ignored")

seed: seed for the RNG; length 1 or 2
stream: RNG stream to use; length 1 or 2
kind: string specifying the RNG, One of “pcg64”, “Xoroshiro128+”, “Xoroshiro128++”, “Xoshiro256+”, “Xoshiro256++” or “Threefry”
normal-kind: ignored; included for compatibility with RNGkind

Random variates with uniform distribution

Rcpp::NumericVector dqrng::dqrunif(size_t n, double min = 0.0, double max = 1.0)
DEPRECATED double dqrng::runif(double min = 0.0, double max = 1.0)

n: number of observations
min: lower limit of the uniform distribution
max: upper limit of the uniform distribution

Random variates with normal distribution

Rcpp::NumericVector dqrng::dqrnorm(size_t n, double mean = 0.0, double sd = 1.0)
DEPRECATED double dqrng::rnorm(double mean = 0.0, double sd = 1.0)

n: number of observations
mean: mean value of the normal distribution
sd: standard deviation of the normal distribution

Random variates with exponential distribution

Rcpp::NumericVector dqrng::dqrexp(size_t n, double rate = 1.0)
DEPRECATED double dqrng::rexp(double rate = 1.0)

n: number of observations
rate: rate of the exponential distribution

Random variates with Rademacher distribution

Rcpp::IntegerVector dqrng::dqrrademacher(size_t n)

n: number of observations

Random sampling

Rcpp::IntegerVector dqrng::dqsample_int(int n, int size, bool replace = false,
                                        Rcpp::Nullable<Rcpp::NumericVector> probs = R_NilValue,
                                        int offset = 0)
Rcpp::NumericVector dqrng::dqsample_num(double n, double size, bool replace = false,
                                        Rcpp::Nullable<Rcpp::NumericVector> probs = R_NilValue,
                                        int offset = 0)

n: a positive number, the number of items to choose from
size: a non-negative number giving the number of items to choose
replace: should sampling be with replacement?
prob: a vector of probability weights for obtaining the elements of the vector being sampled (currently ignored)
offset: sample from range [offset, offset + m)

The two functions are used for “normal” and “long-vector” support in R.

Getting and setting the RNG state

std::vector<std::string> dqrng::dqrng_get_state()
void dqrng::dqrng_set_state(std::vector<std::string> state)

state: a std::vector<std::string> as produced by dqrng_get_state()

Accessing the global RNG

Rcpp::XPtr<dqrng::random_64bit_generator> dqrng::get_rng()

Direct usage of this method is discouraged. The preferred way of accessing the global RNG is to instantiate dqrng::random_64bit_accessor within your function. Note that you MUST NOT delete the global RNG. Using dqrng::random_64bit_accessor makes this impossible. In addition, you SHOULD NOT store a reference to the RNG permanently, because it can be invalidated by calls to dqRNGkind. Therefore, instances of dqrng::random_64bit_accessor SHOULD be stored as (non-static) variables in functions.

Note that dqrng::random_64bit_accessor supports UniformRandomBitGenerator and can therefore be used together with any C++11 distribution function. In addition, the following functions are supported, since they are inherited from the abstract parent class random_64bit_generator:

// clone RNG and select a different stream
std::unique_ptr<random_64bit_generator> clone(uint64_t stream)
// uniform doubles in [0,1) and double-int-pairs
double uniform01()
std::pair<double, int> generate_double_8bit_pair()
// uniform integers in a range 
uint32_t operator() (uint32_t range)
uint64_t operator() (uint64_t range)
ResultType variate<ResultType, DistTmpl>(param1, ... paramN)
generate<DistTmpl>(container, param1, ... paramN)
generate<DistTmpl>(start, end, param1, ... paramN)
Dist::result_type variate<Dist>(param1, ... paramN)
generate<Dist>(container, param1, ... paramN)
generate<Dist>(start, end, param1, ... paramN)

stream: RNG stream to use for the cloned RNG
range: Integers are generated in closed interval [0, range]
ResultType: Expected result from the distribution template DistTmpl
DistTmpl: Distribution template like std::uniform_distribution. DistTmpl<ResultType> defines the full distribution.
Dist: Full distribution like std::uniform_distribution<double> or dqrng::normal_distriubtion.
param1, ... paramN: Necessary parameters to initialize the distribution.
container: A container that is to be filled with variates from the distribution function. Needs to support std::begin and std::end.
start, end: Forward iterators pointing to start and end of a range to be filled with variates from the distribution function.