ion effectively shrinks the eigenvalues of the sample
covariance matrix towards the identity. This estimator is more accurate
than the sample covariance matrix in high-dimensional settings under fairly
loose assumptions. For more information, consider reviewing the manuscript
by Ledoit and Wolf (2004)
.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">linearShrinkLWEst</span><span class="op">(</span><span class="va">dat</span><span c