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`

.

```
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.