knitr::opts_chunk$set(echo = TRUE)
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:
// [[Rcpp::depends(dqrng)]]
together with Rcpp::sourceCpp()
Rcpp::cppFunction(depends = "dqrng", ...)
LinkingTo: dqrng
The following functions are also available if you include dqrng.h
.
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+", "Xoshiro256+" or "Threefry"
normal-kind
: ignored; included for compatibility with RNGkind
Rcpp::NumericVector dqrng::dqrunif(size_t n, double min = 0.0, double max = 1.0) 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
Rcpp::NumericVector dqrng::dqrnorm(size_t n, double mean = 0.0, double sd = 1.0) 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
Rcpp::NumericVector dqrng::dqrexp(size_t n, double rate = 1.0) double dqrng::rexp(double rate = 1.0)
n
: number of observations
rate
: rate of the exponential distribution
Rcpp::IntegerVector dqrng::dqsample_int(int m, int n, bool replace = false, Rcpp::Nullable<Rcpp::NumericVector> probs = R_NilValue, int offset = 0) Rcpp::NumericVector dqrng::dqsample_num(double m, double n, bool replace = false, Rcpp::Nullable<Rcpp::NumericVector> probs = R_NilValue, int offset = 0)
m
: a positive number, the number of items to choose from
n
: 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.
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