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<h1 class="title toc-ignore">C++ API</h1>
<h4 class="author">Ralf Stubner</h4>
<h4 class="date">2021-05-01</h4>
<p>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:</p>
<ul>
<li>use <code>// [[Rcpp::depends(dqrng)]]</code> together with <code>Rcpp::sourceCpp()</code></li>
<li>use <code>Rcpp::cppFunction(depends = "dqrng", ...)</code></li>
<li>use an R package with <code>LinkingTo: dqrng</code></li>
</ul>
<p>The following functions are also available if you include <code>dqrng.h</code>.</p>
<div id="setting-seed-and-rng-type" class="section level2">
<h2>Setting seed and RNG type</h2>
<div class="sourceCode" id="cb1"><pre class="sourceCode cpp"><code class="sourceCode cpp"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="dt">void</span> dqrng::dqset_seed(Rcpp::IntegerVector seed, </span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a> Rcpp::Nullable<Rcpp::IntegerVector> stream = R_NilValue)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="dt">void</span> dqrng::dqRNGkind(<span class="bu">std::</span>string kind, <span class="at">const</span> <span class="bu">std::</span>string& normal_kind = <span class="st">"ignored"</span>)</span></code></pre></div>
<dl>
<dt><code>seed</code></dt>
<dd>seed for the RNG; length 1 or 2
</dd>
<dt><code>stream</code></dt>
<dd>RNG stream to use; length 1 or 2
</dd>
<dt><code>kind</code></dt>
<dd>string specifying the RNG, One of “pcg64”, “Xoroshiro128+”, “Xoshiro256+” or “Threefry”
</dd>
<dt><code>normal-kind</code></dt>
<dd>ignored; included for compatibility with <code>RNGkind</code>
</dd>
</dl>
</div>
<div id="random-variates-with-uniform-distribution" class="section level2">
<h2>Random variates with uniform distribution</h2>
<div class="sourceCode" id="cb2"><pre class="sourceCode cpp"><code class="sourceCode cpp"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>Rcpp::NumericVector dqrng::dqrunif(<span class="dt">size_t</span> n, <span class="dt">double</span> min = <span class="fl">0.0</span>, <span class="dt">double</span> max = <span class="fl">1.0</span>)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="dt">double</span> dqrng::runif(<span class="dt">double</span> min = <span class="fl">0.0</span>, <span class="dt">double</span> max = <span class="fl">1.0</span>)</span></code></pre></div>
<dl>
<dt><code>n</code></dt>
<dd>number of observations
</dd>
<dt><code>min</code></dt>
<dd>lower limit of the uniform distribution
</dd>
<dt><code>max</code></dt>
<dd>upper limit of the uniform distribution
</dd>
</dl>
</div>
<div id="random-variates-with-normal-distribution" class="section level2">
<h2>Random variates with normal distribution</h2>
<div class="sourceCode" id="cb3"><pre class="sourceCode cpp"><code class="sourceCode cpp"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>Rcpp::NumericVector dqrng::dqrnorm(<span class="dt">size_t</span> n, <span class="dt">double</span> mean = <span class="fl">0.0</span>, <span class="dt">double</span> sd = <span class="fl">1.0</span>)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="dt">double</span> dqrng::rnorm(<span class="dt">double</span> mean = <span class="fl">0.0</span>, <span class="dt">double</span> sd = <span class="fl">1.0</span>)</span></code></pre></div>
<dl>
<dt><code>n</code></dt>
<dd>number of observations
</dd>
<dt><code>mean</code></dt>
<dd>mean value of the normal distribution
</dd>
<dt><code>sd</code></dt>
<dd>standard deviation of the normal distribution
</dd>
</dl>
</div>
<div id="random-variates-with-exponential-distribution" class="section level2">
<h2>Random variates with exponential distribution</h2>
<div class="sourceCode" id="cb4"><pre class="sourceCode cpp"><code class="sourceCode cpp"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>Rcpp::NumericVector dqrng::dqrexp(<span class="dt">size_t</span> n, <span class="dt">double</span> rate = <span class="fl">1.0</span>)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="dt">double</span> dqrng::rexp(<span class="dt">double</span> rate = <span class="fl">1.0</span>)</span></code></pre></div>
<dl>
<dt><code>n</code></dt>
<dd>number of observations
</dd>
<dt><code>rate</code></dt>
<dd>rate of the exponential distribution
</dd>
</dl>
</div>
<div id="random-sampling" class="section level2">
<h2>Random sampling</h2>
<div class="sourceCode" id="cb5"><pre class="sourceCode cpp"><code class="sourceCode cpp"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>Rcpp::IntegerVector dqrng::dqsample_int(<span class="dt">int</span> m, <span class="dt">int</span> n, <span class="dt">bool</span> replace = <span class="kw">false</span>,</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> Rcpp::Nullable<Rcpp::NumericVector> probs = R_NilValue,</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">int</span> offset = <span class="dv">0</span>)</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>Rcpp::NumericVector dqrng::dqsample_num(<span class="dt">double</span> m, <span class="dt">double</span> n, <span class="dt">bool</span> replace = <span class="kw">false</span>,</span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> Rcpp::Nullable<Rcpp::NumericVector> probs = R_NilValue,</span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> <span class="dt">int</span> offset = <span class="dv">0</span>)</span></code></pre></div>
<dl>
<dt><code>m</code></dt>
<dd>a positive number, the number of items to choose from
</dd>
<dt><code>n</code></dt>
<dd>a non-negative number giving the number of items to choose
</dd>
<dt><code>replace</code></dt>
<dd>should sampling be with replacement?
</dd>
<dt><code>prob</code></dt>
<dd>a vector of probability weights for obtaining the elements of the vector being sampled (currently ignored)
</dd>
<dt><code>offset</code></dt>
<dd>sample from range <code>[offset, offset + m)</code>
</dd>
</dl>
<p>The two functions are used for “normal” and “long-vector” support in R.</p>
</div>
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