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  <refnamediv>
    <refname>schurrc</refname>
    <refpurpose>Schur algorithm for computing reflection coefficients and prediction error variance.</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Syntax</title>
   <synopsis>
   [k, e] = schurrc(R)
   
   </synopsis>
</refsynopsisdiv>

<refsection>
   <title>Parameters</title>
   <variablelist>
        <varlistentry>
            <term>R:</term>
            <listitem><para> Autocorrelation vector or matrix. If `R` is a matrix, the function computes the reflection coefficients</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>k:</term>
            <listitem><para> Reflection coefficients (matrix). Each column corresponds to the reflection coefficients for the respective column of `R`.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>e:</term>
            <listitem><para> Prediction error variance (vector). Each row corresponds to the prediction error variance for the respective column of `R`.</para></listitem>
        </varlistentry>
   </variablelist>
</refsection>

<refsection>
   <title>Description</title>
   <para>
The `schurrc` function computes the reflection coefficients and prediction error variance using Schur's algorithm.
If the input `R` is a matrix, the function processes each column independently.
   </para>
   <para>
</para>
</refsection>

<refsection>
   <title>Examples</title>
   <programlisting role="example"><![CDATA[
// Compute reflection coefficients and prediction error variance:
m = linspace(1, 100);
r = xcorr(m(1:5), 'unbiased') // Autocorrelation vector
[k, e] = schurrc(r(5:$))
   ]]></programlisting>
</refsection>
</refentry>
