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<div class="titlepage"><div><div><h2 class="title" style="clear: both">
<a name="math_toolkit.univariate_statistics"></a><a class="link" href="univariate_statistics.html" title="Univariate Statistics">Univariate Statistics</a>
</h2></div></div></div>
<h4>
<a name="math_toolkit.univariate_statistics.h0"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.synopsis"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.synopsis">Synopsis</a>
    </h4>
<pre class="programlisting"><span class="preprocessor">#include</span> <span class="special">&lt;</span><span class="identifier">boost</span><span class="special">/</span><span class="identifier">math</span><span class="special">/</span><span class="identifier">statistics</span><span class="special">/</span><span class="identifier">univariate_statistics</span><span class="special">.</span><span class="identifier">hpp</span><span class="special">&gt;</span>

<span class="keyword">namespace</span> <span class="identifier">boost</span><span class="special">{</span> <span class="keyword">namespace</span> <span class="identifier">math</span><span class="special">{</span> <span class="keyword">namespace</span> <span class="identifier">statistics</span> <span class="special">{</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">mean</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">mean</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">variance</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">variance</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">sample_variance</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">sample_variance</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">mean_and_sample_variance</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">skewness</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">skewness</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">kurtosis</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">kurtosis</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">excess_kurtosis</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">excess_kurtosis</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">first_four_moments</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">first_four_moments</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">median</span><span class="special">(</span><span class="identifier">Container</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">median</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">RandomAccessIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">median_absolute_deviation</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">,</span> <span class="keyword">typename</span> <span class="identifier">std</span><span class="special">::</span><span class="identifier">iterator_traits</span><span class="special">&lt;</span><span class="identifier">RandomAccessIterator</span><span class="special">&gt;::</span><span class="identifier">value_type</span> <span class="identifier">center</span><span class="special">=</span><span class="identifier">std</span><span class="special">::</span><span class="identifier">numeric_limits</span><span class="special">&lt;</span><span class="identifier">Real</span><span class="special">&gt;::</span><span class="identifier">quiet_NaN</span><span class="special">());</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">RandomAccessContainer</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">median_absolute_deviation</span><span class="special">(</span><span class="identifier">RandomAccessContainer</span> <span class="identifier">v</span><span class="special">,</span> <span class="keyword">typename</span> <span class="identifier">RandomAccessContainer</span><span class="special">::</span><span class="identifier">value_type</span> <span class="identifier">center</span><span class="special">=</span><span class="identifier">std</span><span class="special">::</span><span class="identifier">numeric_limits</span><span class="special">&lt;</span><span class="identifier">Real</span><span class="special">&gt;::</span><span class="identifier">quiet_NaN</span><span class="special">());</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">gini_coefficient</span><span class="special">(</span><span class="identifier">Container</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">gini_coefficient</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">sample_gini_coefficient</span><span class="special">(</span><span class="identifier">Container</span> <span class="special">&amp;</span> <span class="identifier">c</span><span class="special">);</span>

    <span class="keyword">template</span><span class="special">&lt;</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">&gt;</span>
    <span class="keyword">auto</span> <span class="identifier">sample_gini_coefficient</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>

<span class="special">}}}</span>
</pre>
<h4>
<a name="math_toolkit.univariate_statistics.h1"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.description"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.description">Description</a>
    </h4>
<p>
      The file <code class="computeroutput"><span class="identifier">boost</span><span class="special">/</span><span class="identifier">math</span><span class="special">/</span><span class="identifier">statistics</span><span class="special">/</span><span class="identifier">univariate_statistics</span><span class="special">.</span><span class="identifier">hpp</span></code> is a
      set of facilities for computing scalar values from vectors.
    </p>
<p>
      Many of these functionals have trivial naive implementations, but experienced
      programmers will recognize that even trivial algorithms are easy to screw up,
      and that numerical instabilities often lurk in corner cases. We have attempted
      to do our "due diligence" to root out these problems-scouring the
      literature for numerically stable algorithms for even the simplest of functionals.
    </p>
<p>
      <span class="emphasis"><em>Nota bene</em></span>: Some similar functionality is provided in
      <a href="https://www.boost.org/doc/libs/1_68_0/doc/html/accumulators/user_s_guide.html" target="_top">Boost
      Accumulators Framework</a>. These accumulators should be used in real-time
      applications; <code class="computeroutput"><span class="identifier">univariate_statistics</span><span class="special">.</span><span class="identifier">hpp</span></code> should
      be used when CPU vectorization is needed. As a reminder, remember that to actually
      <span class="emphasis"><em>get</em></span> vectorization, compile with <code class="computeroutput"><span class="special">-</span><span class="identifier">march</span><span class="special">=</span><span class="identifier">native</span>
      <span class="special">-</span><span class="identifier">O3</span></code>
      flags.
    </p>
<p>
      We now describe each functional in detail. Our examples use <code class="computeroutput"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span></code>
      to hold the data, but this not required. In general, you can store your data
      in an Eigen array, and Armadillo vector, <code class="computeroutput"><span class="identifier">std</span><span class="special">::</span><span class="identifier">array</span></code>,
      and for many of the routines, a <code class="computeroutput"><span class="identifier">std</span><span class="special">::</span><span class="identifier">forward_list</span></code>.
      These routines are usable in float, double, long double, and Boost.Multiprecision
      precision, as well as their complex extensions whenever the computation is
      well-defined. For certain operations (total variation, for example) integer
      inputs are supported.
    </p>
<h4>
<a name="math_toolkit.univariate_statistics.h2"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.mean"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.mean">Mean</a>
    </h4>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="keyword">double</span> <span class="identifier">mu</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">mean</span><span class="special">(</span><span class="identifier">v</span><span class="special">.</span><span class="identifier">cbegin</span><span class="special">(),</span> <span class="identifier">v</span><span class="special">.</span><span class="identifier">cend</span><span class="special">());</span>
<span class="comment">// Alternative syntax if you want to use entire container:</span>
<span class="identifier">mu</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">mean</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
</pre>
<p>
      The implementation follows <a href="https://doi.org/10.1137/1.9780898718027" target="_top">Higham
      1.6a</a>. The data is not modified and must be forward iterable. Works
      with real and integer data. If the input is an integer type, the output is
      a double precision float.
    </p>
<h4>
<a name="math_toolkit.univariate_statistics.h3"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.variance"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.variance">Variance</a>
    </h4>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="identifier">Real</span> <span class="identifier">sigma_sq</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">variance</span><span class="special">(</span><span class="identifier">v</span><span class="special">.</span><span class="identifier">cbegin</span><span class="special">(),</span> <span class="identifier">v</span><span class="special">.</span><span class="identifier">cend</span><span class="special">());</span>
</pre>
<p>
      If you don't need to calculate on a subset of the input, then the range call
      is more terse:
    </p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="identifier">Real</span> <span class="identifier">sigma_sq</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">variance</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
</pre>
<p>
      The implementation follows <a href="https://doi.org/10.1137/1.9780898718027" target="_top">Higham
      1.6b</a>. The input data must be forward iterable and the range <code class="computeroutput"><span class="special">[</span><span class="identifier">first</span><span class="special">,</span>
      <span class="identifier">last</span><span class="special">)</span></code>
      must contain at least two elements. It is <span class="emphasis"><em>not</em></span> in general
      sensible to pass complex numbers to this routine. If integers are passed as
      input, then the output is a double precision float.
    </p>
<p>
      <code class="computeroutput"><span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">variance</span></code>
      returns the population variance. If you want a sample variance, use
    </p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="identifier">Real</span> <span class="identifier">sn_sq</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">sample_variance</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
</pre>
<h4>
<a name="math_toolkit.univariate_statistics.h4"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.skewness"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.skewness">Skewness</a>
    </h4>
<p>
      Computes the skewness of a dataset:
    </p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="keyword">double</span> <span class="identifier">skewness</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">skewness</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
<span class="comment">// skewness = 0.</span>
</pre>
<p>
      The input vector is not modified, works with integral and real data. If the
      input data is integral, the output is a double precision float.
    </p>
<p>
      For a dataset consisting of a single constant value, we take the skewness to
      be zero by definition.
    </p>
<p>
      The implementation follows <a href="https://prod.sandia.gov/techlib-noauth/access-control.cgi/2008/086212.pdf" target="_top">Pebay</a>.
    </p>
<h4>
<a name="math_toolkit.univariate_statistics.h5"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.kurtosis"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.kurtosis">Kurtosis</a>
    </h4>
<p>
      Computes the kurtosis of a dataset:
    </p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="keyword">double</span> <span class="identifier">kurtosis</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">kurtosis</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
<span class="comment">// kurtosis = 17/10</span>
</pre>
<p>
      The implementation follows <a href="https://prod.sandia.gov/techlib-noauth/access-control.cgi/2008/086212.pdf" target="_top">Pebay</a>.
      The input data must be forward iterable and must consist of real or integral
      values. If the input data is integral, the output is a double precision float.
      Note that this is <span class="emphasis"><em>not</em></span> the excess kurtosis. If you require
      the excess kurtosis, use <code class="computeroutput"><span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">excess_kurtosis</span></code>. This function simply subtracts
      3 from the kurtosis, but it makes eminently clear our definition of kurtosis.
    </p>
<h4>
<a name="math_toolkit.univariate_statistics.h6"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.first_four_moments"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.first_four_moments">First
      four moments</a>
    </h4>
<p>
      Simultaneously computes the first four <a href="https://en.wikipedia.org/wiki/Central_moment" target="_top">central
      moments</a> in a single pass through the data:
    </p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="keyword">auto</span> <span class="special">[</span><span class="identifier">M1</span><span class="special">,</span> <span class="identifier">M2</span><span class="special">,</span> <span class="identifier">M3</span><span class="special">,</span> <span class="identifier">M4</span><span class="special">]</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">first_four_moments</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
</pre>
<h4>
<a name="math_toolkit.univariate_statistics.h7"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.median"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.median">Median</a>
    </h4>
<p>
      Computes the median of a dataset:
    </p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="keyword">double</span> <span class="identifier">m</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">median</span><span class="special">(</span><span class="identifier">v</span><span class="special">.</span><span class="identifier">begin</span><span class="special">(),</span> <span class="identifier">v</span><span class="special">.</span><span class="identifier">end</span><span class="special">());</span>
</pre>
<p>
      <span class="emphasis"><em>Nota bene: The input vector is modified.</em></span> The calculation
      of the median is a thin wrapper around the C++11 <a href="https://en.cppreference.com/w/cpp/algorithm/nth_element" target="_top"><code class="computeroutput"><span class="identifier">nth_element</span></code></a>. Therefore, all requirements
      of <code class="computeroutput"><span class="identifier">std</span><span class="special">::</span><span class="identifier">nth_element</span></code> are inherited by the median calculation.
      In particular, the container must allow random access.
    </p>
<h4>
<a name="math_toolkit.univariate_statistics.h8"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.median_absolute_deviation"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.median_absolute_deviation">Median
      Absolute Deviation</a>
    </h4>
<p>
      Computes the <a href="https://en.wikipedia.org/wiki/Median_absolute_deviation" target="_top">median
      absolute deviation</a> of a dataset:
    </p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">2</span><span class="special">,</span><span class="number">3</span><span class="special">,</span><span class="number">4</span><span class="special">,</span><span class="number">5</span><span class="special">};</span>
<span class="keyword">double</span> <span class="identifier">mad</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">median_absolute_deviation</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
</pre>
<p>
      By default, the deviation from the median is used. If you have some prior that
      the median is zero, or wish to compute the median absolute deviation from the
      mean, use the following:
    </p>
<pre class="programlisting"><span class="comment">// prior is that center is zero:</span>
<span class="keyword">double</span> <span class="identifier">center</span> <span class="special">=</span> <span class="number">0</span><span class="special">;</span>
<span class="keyword">double</span> <span class="identifier">mad</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">median_absolute_deviation</span><span class="special">(</span><span class="identifier">v</span><span class="special">,</span> <span class="identifier">center</span><span class="special">);</span>

<span class="comment">// compute median absolute deviation from the mean:</span>
<span class="keyword">double</span> <span class="identifier">mu</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">mean</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
<span class="keyword">double</span> <span class="identifier">mad</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">median_absolute_deviation</span><span class="special">(</span><span class="identifier">v</span><span class="special">,</span> <span class="identifier">mu</span><span class="special">);</span>
</pre>
<p>
      <span class="emphasis"><em>Nota bene:</em></span> The input vector is modified. Again the vector
      is passed into a call to <a href="https://en.cppreference.com/w/cpp/algorithm/nth_element" target="_top"><code class="computeroutput"><span class="identifier">nth_element</span></code></a>.
    </p>
<h4>
<a name="math_toolkit.univariate_statistics.h9"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.gini_coefficient"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.gini_coefficient">Gini
      Coefficient</a>
    </h4>
<p>
      Compute the Gini coefficient of a dataset:
    </p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">0</span><span class="special">,</span><span class="number">0</span><span class="special">,</span><span class="number">0</span><span class="special">};</span>
<span class="keyword">double</span> <span class="identifier">gini</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">gini_coefficient</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
<span class="comment">// gini = 3/4</span>
<span class="keyword">double</span> <span class="identifier">s_gini</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">sample_gini_coefficient</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
<span class="comment">// s_gini = 1.</span>
<span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special">&lt;</span><span class="keyword">double</span><span class="special">&gt;</span> <span class="identifier">w</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">1</span><span class="special">,</span><span class="number">1</span><span class="special">,</span><span class="number">1</span><span class="special">};</span>
<span class="identifier">gini</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">gini_coefficient</span><span class="special">(</span><span class="identifier">w</span><span class="special">.</span><span class="identifier">begin</span><span class="special">(),</span> <span class="identifier">w</span><span class="special">.</span><span class="identifier">end</span><span class="special">());</span>
<span class="comment">// gini = 0, as all elements are now equal.</span>
</pre>
<p>
      <span class="emphasis"><em>Nota bene</em></span>: The input data is altered: in particular, it
      is sorted. Makes a call to <code class="computeroutput"><span class="identifier">std</span><span class="special">::</span><span class="identifier">sort</span></code>, and
      as such requires random access iterators.
    </p>
<p>
      The sample Gini coefficient lies in the range [0,1], whereas the population
      Gini coefficient is in the range [0, 1 - 1/ <span class="emphasis"><em>n</em></span>].
    </p>
<p>
      <span class="emphasis"><em>Nota bene:</em></span> There is essentially no reason to pass negative
      values to the Gini coefficient function. However, a use case (measuring wealth
      inequality when some people have negative wealth) exists, so we do not throw
      an exception when negative values are encountered. You should have <span class="emphasis"><em>very</em></span>
      good cause to pass negative values to the Gini coefficient calculator. Another
      use case is found in signal processing, but the sorting is by magnitude and
      hence has a different implementation. See <code class="computeroutput"><span class="identifier">absolute_gini_coefficient</span></code>
      for details.
    </p>
<h4>
<a name="math_toolkit.univariate_statistics.h10"></a>
      <span class="phrase"><a name="math_toolkit.univariate_statistics.references"></a></span><a class="link" href="univariate_statistics.html#math_toolkit.univariate_statistics.references">References</a>
    </h4>
<div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; ">
<li class="listitem">
          Higham, Nicholas J. <span class="emphasis"><em>Accuracy and stability of numerical algorithms.</em></span>
          Vol. 80. Siam, 2002.
        </li>
<li class="listitem">
          Philippe P. P&#233;bay: <span class="quote">&#8220;<span class="quote">Formulas for Robust, One-Pass Parallel Computation
          of Covariances and Arbitrary-Order Statistical Moments.</span>&#8221;</span> Technical
          Report SAND2008-6212, Sandia National Laboratories, September 2008.
        </li>
</ul></div>
</div>
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<td align="right"><div class="copyright-footer">Copyright &#169; 2006-2019 Nikhar
      Agrawal, Anton Bikineev, Paul A. Bristow, Marco Guazzone, Christopher Kormanyos,
      Hubert Holin, Bruno Lalande, John Maddock, Jeremy Murphy, Matthew Pulver, Johan
      R&#229;de, Gautam Sewani, Benjamin Sobotta, Nicholas Thompson, Thijs van den Berg,
      Daryle Walker and Xiaogang Zhang<p>
        Distributed under the Boost Software License, Version 1.0. (See accompanying
        file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
      </p>
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