---
title: "C++ API"
author: "Ralf Stubner"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{C++ API}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
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:

* 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`.

## Setting seed and RNG type

```cpp
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`

## Random variates with uniform distribution

```cpp
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

## Random variates with normal distribution

```cpp
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

## Random variates with exponential distribution

```cpp
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

## Random sampling

```cpp
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.
