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@ -101,7 +101,16 @@ pyerrors </h1>
<div class="docstring"><h1 id="what-is-pyerrors">What is pyerrors?</h1>
<p><code><a href="">pyerrors</a></code> is a python package for error computation and propagation of Markov chain Monte Carlo data.</p>
<p><code><a href="">pyerrors</a></code> is a python package for error computation and propagation of Markov chain Monte Carlo data.
It is based on the <strong>gamma method</strong> <a href="https://arxiv.org/abs/hep-lat/0306017">arXiv:hep-lat/0306017</a>. Some of its features are:</p>
<ul>
<li><strong>automatic differentiation</strong> as suggested in <a href="https://arxiv.org/abs/1809.01289">arXiv:1809.01289</a> (partly based on the <a href="https://github.com/HIPS/autograd">autograd</a> package)</li>
<li><strong>treatment of slow modes</strong> in the simulation as suggested in <a href="https://arxiv.org/abs/1009.5228">arXiv:1009.5228</a></li>
<li>coherent <strong>error propagation</strong> for data from <strong>different Markov chains</strong></li>
<li><strong>non-linear fits with x- and y-errors</strong> and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289]</li>
<li><strong>real and complex matrix operations</strong> and their error propagation based on automatic differentiation (cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...)</li>
</ul>
<h2 id="getting-started">Getting started</h2>
@ -167,6 +176,12 @@ pyerrors </h1>
<div class="codehilite"><pre><span></span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd"># What is pyerrors?</span>
<span class="sd">`pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.</span>
<span class="sd">It is based on the **gamma method** [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are:</span>
<span class="sd">- **automatic differentiation** as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package)</span>
<span class="sd">- **treatment of slow modes** in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228)</span>
<span class="sd">- coherent **error propagation** for data from **different Markov chains**</span>
<span class="sd">- **non-linear fits with x- and y-errors** and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289]</span>
<span class="sd">- **real and complex matrix operations** and their error propagation based on automatic differentiation (cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...)</span>
<span class="sd">## Getting started</span>