✨ Add updated citations to README

README.md

@@ -467,7 +467,7 @@ recommended as the result of the test will also become less accurate. Technicall
   the number of jobs with `num_jobs` instead is always preferred.
 
 * While we could declare a model stochastically dominant with <img src="svgs/dabed7f05cf133d9eb92631d564a96a8.svg?invert_in_darkmode" align=middle width=72.19750559999999pt height=21.18721440000001pt/>, we found this to have a comparatively high
-Type I error (false positives). Tests in our paper have shown that a more useful threshold that trades of Type I and 
+Type I error (false positives). Tests [in our paper](https://arxiv.org/pdf/2204.06815.pdf) have shown that a more useful threshold that trades of Type I and 
   Type II error between different scenarios might be <img src="svgs/9ac49cb370a5b09fca29068ea18eab63.svg?invert_in_darkmode" align=middle width=51.969107849999986pt height=21.18721440000001pt/>.
 
 * Bootstrap and permutation-randomization are all non-parametric tests, i.e. they don't make any assumptions about 
@@ -481,7 +481,17 @@ the distribution of our test metric. Nevertheless, they differ in their *statist
 
 ### :mortar_board: Cite
 
-If you use the ASO test via `aso()`, please cite the original work:
+Using this package in general, please cite the following:
+
+    @article{ulmer2022deep,
+      title={deep-significance-Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks},
+      author={Ulmer, Dennis and Hardmeier, Christian and Frellsen, Jes},
+      journal={arXiv preprint arXiv:2204.06815},
+      year={2022}
+    }
+
+
+If you use the ASO test via `aso()` or `multi_aso, please cite the original works:
 
     @inproceedings{dror2019deep,
       author    = {Rotem Dror and
@@ -502,21 +512,20 @@ If you use the ASO test via `aso()`, please cite the original work:
       timestamp = {Tue, 28 Jan 2020 10:27:52 +0100},
     }
 
-Using this package in general, please cite the following:
-
-    @software{dennis_ulmer_2021_4638709,
-      author       = {Dennis Ulmer},
-      title        = {{deep-significance: Easy and Better Significance 
-                       Testing for Deep Neural Networks}},
-      month        = mar,
-      year         = 2021,
-      note         = {https://github.com/Kaleidophon/deep-significance},
-      publisher    = {Zenodo},
-      version      = {v1.0.0a},
-      doi          = {10.5281/zenodo.4638709},
-      url          = {https://doi.org/10.5281/zenodo.4638709}
+    @incollection{del2018optimal,
+      title={An optimal transportation approach for assessing almost stochastic order},
+      author={Del Barrio, Eustasio and Cuesta-Albertos, Juan A and Matr{\'a}n, Carlos},
+      booktitle={The Mathematics of the Uncertain},
+      pages={33--44},
+      year={2018},
+      publisher={Springer}
     }
 
+For instance, you can write
+
+    In order to compare models, we use the Almost Stochastic Order test \citep{del2018optimal, dror2019deep} as 
+    implemented by \citet{ulmer2022deep}.
+
 ### :medal_sports: Acknowledgements
 
 This package was created out of discussions of the [NLPnorth group](https://nlpnorth.github.io/) at the IT University 

README_RAW.md

@@ -475,7 +475,7 @@ recommended as the result of the test will also become less accurate. Technicall
   the number of jobs with `num_jobs` instead is always preferred.
 
 * While we could declare a model stochastically dominant with $\epsilon_\text{min} < 0.5$, we found this to have a comparatively high
-Type I error (false positives). Tests in our paper have shown that a more useful threshold that trades of Type I and 
+Type I error (false positives). Tests [in our paper](https://arxiv.org/pdf/2204.06815.pdf) have shown that a more useful threshold that trades of Type I and 
   Type II error between different scenarios might be $\tau = 0.2$.
 
 * Bootstrap and permutation-randomization are all non-parametric tests, i.e. they don't make any assumptions about 
@@ -489,7 +489,17 @@ the distribution of our test metric. Nevertheless, they differ in their *statist
 
 ### :mortar_board: Cite
 
-If you use the ASO test via `aso()`, please cite the original work:
+Using this package in general, please cite the following:
+
+    @article{ulmer2022deep,
+      title={deep-significance-Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks},
+      author={Ulmer, Dennis and Hardmeier, Christian and Frellsen, Jes},
+      journal={arXiv preprint arXiv:2204.06815},
+      year={2022}
+    }
+
+
+If you use the ASO test via `aso()` or `multi_aso, please cite the original works:
 
     @inproceedings{dror2019deep,
       author    = {Rotem Dror and
@@ -510,21 +520,20 @@ If you use the ASO test via `aso()`, please cite the original work:
       timestamp = {Tue, 28 Jan 2020 10:27:52 +0100},
     }
 
-Using this package in general, please cite the following:
-
-    @software{dennis_ulmer_2021_4638709,
-      author       = {Dennis Ulmer},
-      title        = {{deep-significance: Easy and Better Significance 
-                       Testing for Deep Neural Networks}},
-      month        = mar,
-      year         = 2021,
-      note         = {https://github.com/Kaleidophon/deep-significance},
-      publisher    = {Zenodo},
-      version      = {v1.0.0a},
-      doi          = {10.5281/zenodo.4638709},
-      url          = {https://doi.org/10.5281/zenodo.4638709}
+    @incollection{del2018optimal,
+      title={An optimal transportation approach for assessing almost stochastic order},
+      author={Del Barrio, Eustasio and Cuesta-Albertos, Juan A and Matr{\'a}n, Carlos},
+      booktitle={The Mathematics of the Uncertain},
+      pages={33--44},
+      year={2018},
+      publisher={Springer}
     }
 
+For instance, you can write
+
+    In order to compare models, we use the Almost Stochastic Order test \citep{del2018optimal, dror2019deep} as 
+    implemented by \citet{ulmer2022deep}.
+
 ### :medal_sports: Acknowledgements
 
 This package was created out of discussions of the [NLPnorth group](https://nlpnorth.github.io/) at the IT University 

docs/README_DOCS.md

@@ -467,7 +467,7 @@ recommended as the result of the test will also become less accurate. Technicall
   the number of jobs with `num_jobs` instead is always preferred.
 
 * While we could declare a model stochastically dominant with <img src="dabed7f05cf133d9eb92631d564a96a8.svg?invert_in_darkmode" align=middle width=72.19750559999999pt height=21.18721440000001pt/>, we found this to have a comparatively high
-Type I error (false positives). Tests in our paper have shown that a more useful threshold that trades of Type I and 
+Type I error (false positives). Tests [in our paper](https://arxiv.org/pdf/2204.06815.pdf) have shown that a more useful threshold that trades of Type I and 
   Type II error between different scenarios might be <img src="9ac49cb370a5b09fca29068ea18eab63.svg?invert_in_darkmode" align=middle width=51.969107849999986pt height=21.18721440000001pt/>.
 
 * Bootstrap and permutation-randomization are all non-parametric tests, i.e. they don't make any assumptions about 
@@ -481,7 +481,17 @@ the distribution of our test metric. Nevertheless, they differ in their *statist
 
 ### |:mortar_board:| Cite
 
-If you use the ASO test via `aso()`, please cite the original work:
+Using this package in general, please cite the following:
+
+    @article{ulmer2022deep,
+      title={deep-significance-Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks},
+      author={Ulmer, Dennis and Hardmeier, Christian and Frellsen, Jes},
+      journal={arXiv preprint arXiv:2204.06815},
+      year={2022}
+    }
+
+
+If you use the ASO test via `aso()` or `multi_aso, please cite the original works:
 
     @inproceedings{dror2019deep,
       author    = {Rotem Dror and
@@ -502,21 +512,20 @@ If you use the ASO test via `aso()`, please cite the original work:
       timestamp = {Tue, 28 Jan 2020 10:27:52 +0100},
     }
 
-Using this package in general, please cite the following:
-
-    @software{dennis_ulmer_2021_4638709,
-      author       = {Dennis Ulmer},
-      title        = {{deep-significance: Easy and Better Significance 
-                       Testing for Deep Neural Networks}},
-      month        = mar,
-      year         = 2021,
-      note         = {https://github.com/Kaleidophon/deep-significance},
-      publisher    = {Zenodo},
-      version      = {v1.0.0a},
-      doi          = {10.5281/zenodo.4638709},
-      url          = {https://doi.org/10.5281/zenodo.4638709}
+    @incollection{del2018optimal,
+      title={An optimal transportation approach for assessing almost stochastic order},
+      author={Del Barrio, Eustasio and Cuesta-Albertos, Juan A and Matr{\'a}n, Carlos},
+      booktitle={The Mathematics of the Uncertain},
+      pages={33--44},
+      year={2018},
+      publisher={Springer}
     }
 
+For instance, you can write
+
+    In order to compare models, we use the Almost Stochastic Order test \citep{del2018optimal, dror2019deep} as 
+    implemented by \citet{ulmer2022deep}.
+
 ### |:medal_sports:| Acknowledgements
 
 This package was created out of discussions of the [NLPnorth group](https://nlpnorth.github.io/) at the IT University 

docs/build/html/README_DOCS.html

@@ -566,7 +566,7 @@ <h3>General recommendations &amp; other notes<a class="headerlink" href="#genera
 that becomes tighter with the number of samples and bootstrap iterations (del Barrio et al., 2017). Thus, increasing
 the number of jobs with <code class="docutils literal notranslate"><span class="pre">num_jobs</span></code> instead is always preferred.</p></li>
 <li><p>While we could declare a model stochastically dominant with <img src="dabed7f05cf133d9eb92631d564a96a8.svg?invert_in_darkmode" align=middle width=72.19750559999999pt height=21.18721440000001pt/>, we found this to have a comparatively high
-Type I error (false positives). Tests in our paper have shown that a more useful threshold that trades of Type I and
+Type I error (false positives). Tests <a class="reference external" href="https://arxiv.org/pdf/2204.06815.pdf">in our paper</a> have shown that a more useful threshold that trades of Type I and
 Type II error between different scenarios might be <img src="9ac49cb370a5b09fca29068ea18eab63.svg?invert_in_darkmode" align=middle width=51.969107849999986pt height=21.18721440000001pt/>.</p></li>
 <li><p>Bootstrap and permutation-randomization are all non-parametric tests, i.e. they don’t make any assumptions about
 the distribution of our test metric. Nevertheless, they differ in their <em>statistical power</em>, which is defined as the probability
@@ -580,7 +580,16 @@ <h3>General recommendations &amp; other notes<a class="headerlink" href="#genera
 </section>
 <section id="mortar-board-cite">
 <h3>|:mortar_board:| Cite<a class="headerlink" href="#mortar-board-cite" title="Permalink to this headline">¶</a></h3>
-<p>If you use the ASO test via <code class="docutils literal notranslate"><span class="pre">aso()</span></code>, please cite the original work:</p>
+<p>Using this package in general, please cite the following:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@article</span><span class="p">{</span><span class="n">ulmer2022deep</span><span class="p">,</span>
+  <span class="n">title</span><span class="o">=</span><span class="p">{</span><span class="n">deep</span><span class="o">-</span><span class="n">significance</span><span class="o">-</span><span class="n">Easy</span> <span class="ow">and</span> <span class="n">Meaningful</span> <span class="n">Statistical</span> <span class="n">Significance</span> <span class="n">Testing</span> <span class="ow">in</span> <span class="n">the</span> <span class="n">Age</span> <span class="n">of</span> <span class="n">Neural</span> <span class="n">Networks</span><span class="p">},</span>
+  <span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Ulmer</span><span class="p">,</span> <span class="n">Dennis</span> <span class="ow">and</span> <span class="n">Hardmeier</span><span class="p">,</span> <span class="n">Christian</span> <span class="ow">and</span> <span class="n">Frellsen</span><span class="p">,</span> <span class="n">Jes</span><span class="p">},</span>
+  <span class="n">journal</span><span class="o">=</span><span class="p">{</span><span class="n">arXiv</span> <span class="n">preprint</span> <span class="n">arXiv</span><span class="p">:</span><span class="mf">2204.06815</span><span class="p">},</span>
+  <span class="n">year</span><span class="o">=</span><span class="p">{</span><span class="mi">2022</span><span class="p">}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>If you use the ASO test via <code class="docutils literal notranslate"><span class="pre">aso()</span></code> or `multi_aso, please cite the original works:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@inproceedings</span><span class="p">{</span><span class="n">dror2019deep</span><span class="p">,</span>
   <span class="n">author</span>    <span class="o">=</span> <span class="p">{</span><span class="n">Rotem</span> <span class="n">Dror</span> <span class="ow">and</span>
                <span class="n">Segev</span> <span class="n">Shlomov</span> <span class="ow">and</span>
@@ -599,21 +608,20 @@ <h3>|:mortar_board:| Cite<a class="headerlink" href="#mortar-board-cite" title="
   <span class="n">doi</span>       <span class="o">=</span> <span class="p">{</span><span class="mf">10.18653</span><span class="o">/</span><span class="n">v1</span><span class="o">/</span><span class="n">p19</span><span class="o">-</span><span class="mi">1266</span><span class="p">},</span>
   <span class="n">timestamp</span> <span class="o">=</span> <span class="p">{</span><span class="n">Tue</span><span class="p">,</span> <span class="mi">28</span> <span class="n">Jan</span> <span class="mi">2020</span> <span class="mi">10</span><span class="p">:</span><span class="mi">27</span><span class="p">:</span><span class="mi">52</span> <span class="o">+</span><span class="mi">0100</span><span class="p">},</span>
 <span class="p">}</span>
+
+<span class="nd">@incollection</span><span class="p">{</span><span class="n">del2018optimal</span><span class="p">,</span>
+  <span class="n">title</span><span class="o">=</span><span class="p">{</span><span class="n">An</span> <span class="n">optimal</span> <span class="n">transportation</span> <span class="n">approach</span> <span class="k">for</span> <span class="n">assessing</span> <span class="n">almost</span> <span class="n">stochastic</span> <span class="n">order</span><span class="p">},</span>
+  <span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Del</span> <span class="n">Barrio</span><span class="p">,</span> <span class="n">Eustasio</span> <span class="ow">and</span> <span class="n">Cuesta</span><span class="o">-</span><span class="n">Albertos</span><span class="p">,</span> <span class="n">Juan</span> <span class="n">A</span> <span class="ow">and</span> <span class="n">Matr</span><span class="p">{</span>\<span class="s1">&#39;a}n, Carlos},</span>
+  <span class="n">booktitle</span><span class="o">=</span><span class="p">{</span><span class="n">The</span> <span class="n">Mathematics</span> <span class="n">of</span> <span class="n">the</span> <span class="n">Uncertain</span><span class="p">},</span>
+  <span class="n">pages</span><span class="o">=</span><span class="p">{</span><span class="mi">33</span><span class="o">--</span><span class="mi">44</span><span class="p">},</span>
+  <span class="n">year</span><span class="o">=</span><span class="p">{</span><span class="mi">2018</span><span class="p">},</span>
+  <span class="n">publisher</span><span class="o">=</span><span class="p">{</span><span class="n">Springer</span><span class="p">}</span>
+<span class="p">}</span>
 </pre></div>
 </div>
-<p>Using this package in general, please cite the following:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@software</span><span class="p">{</span><span class="n">dennis_ulmer_2021_4638709</span><span class="p">,</span>
-  <span class="n">author</span>       <span class="o">=</span> <span class="p">{</span><span class="n">Dennis</span> <span class="n">Ulmer</span><span class="p">},</span>
-  <span class="n">title</span>        <span class="o">=</span> <span class="p">{{</span><span class="n">deep</span><span class="o">-</span><span class="n">significance</span><span class="p">:</span> <span class="n">Easy</span> <span class="ow">and</span> <span class="n">Better</span> <span class="n">Significance</span> 
-                   <span class="n">Testing</span> <span class="k">for</span> <span class="n">Deep</span> <span class="n">Neural</span> <span class="n">Networks</span><span class="p">}},</span>
-  <span class="n">month</span>        <span class="o">=</span> <span class="n">mar</span><span class="p">,</span>
-  <span class="n">year</span>         <span class="o">=</span> <span class="mi">2021</span><span class="p">,</span>
-  <span class="n">note</span>         <span class="o">=</span> <span class="p">{</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">Kaleidophon</span><span class="o">/</span><span class="n">deep</span><span class="o">-</span><span class="n">significance</span><span class="p">},</span>
-  <span class="n">publisher</span>    <span class="o">=</span> <span class="p">{</span><span class="n">Zenodo</span><span class="p">},</span>
-  <span class="n">version</span>      <span class="o">=</span> <span class="p">{</span><span class="n">v1</span><span class="mf">.0.0</span><span class="n">a</span><span class="p">},</span>
-  <span class="n">doi</span>          <span class="o">=</span> <span class="p">{</span><span class="mf">10.5281</span><span class="o">/</span><span class="n">zenodo</span><span class="mf">.4638709</span><span class="p">},</span>
-  <span class="n">url</span>          <span class="o">=</span> <span class="p">{</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">doi</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="mf">10.5281</span><span class="o">/</span><span class="n">zenodo</span><span class="mf">.4638709</span><span class="p">}</span>
-<span class="p">}</span>
+<p>For instance, you can write</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="n">order</span> <span class="n">to</span> <span class="n">compare</span> <span class="n">models</span><span class="p">,</span> <span class="n">we</span> <span class="n">use</span> <span class="n">the</span> <span class="n">Almost</span> <span class="n">Stochastic</span> <span class="n">Order</span> <span class="n">test</span> \<span class="n">citep</span><span class="p">{</span><span class="n">del2018optimal</span><span class="p">,</span> <span class="n">dror2019deep</span><span class="p">}</span> <span class="k">as</span> 
+<span class="n">implemented</span> <span class="n">by</span> \<span class="n">citet</span><span class="p">{</span><span class="n">ulmer2022deep</span><span class="p">}</span><span class="o">.</span>
 </pre></div>
 </div>
 </section>