From e9487a115608a71d52c12d3c3bce4f9aa460e205 Mon Sep 17 00:00:00 2001
From: nkoenen <79153641+nkoenen@users.noreply.github.com>
Date: Tue, 26 Nov 2024 11:17:38 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20bips-hb/?=
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diff --git a/articles/Example_1_iris.html b/articles/Example_1_iris.html
index 9b166a3..5e5c324 100644
--- a/articles/Example_1_iris.html
+++ b/articles/Example_1_iris.html
@@ -416,7 +416,7 @@ <h5 id="plotly-based-plot">plotly-based plot<a class="anchor" aria-label="anchor
 <code class="sourceCode R"><span><span class="co"># Show data point 1 for output node 1 (Setosa) and 2 (Versicolor)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">deeplift_mean</span>, data_idx <span class="op">=</span> <span class="fl">1</span>, output_idx <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, as_plotly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
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@@ -463,7 +463,7 @@ <h5 id="plotly-based-plot-1">plotly-based plot<a class="anchor" aria-label="anch
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diff --git a/articles/Example_3_imagenet.html b/articles/Example_3_imagenet.html
index d7752e9..818a210 100644
--- a/articles/Example_3_imagenet.html
+++ b/articles/Example_3_imagenet.html
@@ -272,7 +272,7 @@ <h2 id="pre-trained-model-vgg19">Pre-trained model VGG19<a class="anchor" aria-l
 <code class="sourceCode R"><span><span class="co"># Load the model</span></span>
 <span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/keras/man/application_vgg.html" class="external-link">application_vgg19</a></span><span class="op">(</span>include_top <span class="op">=</span> <span class="cn">TRUE</span>, weights <span class="op">=</span> <span class="st">"imagenet"</span><span class="op">)</span> </span>
 <span><span class="co">#&gt; Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels.h5</span></span>
-<span><span class="co">#&gt;      8192/574710816 [..............................] - ETA: 0s  4202496/574710816 [..............................] - ETA: 15s 12509184/574710816 [..............................] - ETA: 7s  16785408/574710816 [..............................] - ETA: 7s 24551424/574710816 [&gt;.............................] - ETA: 6s 33554432/574710816 [&gt;.............................] - ETA: 5s 36888576/574710816 [&gt;.............................] - ETA: 5s 43966464/574710816 [=&gt;............................] - ETA: 5s 50339840/574710816 [=&gt;............................] - ETA: 5s 58728448/574710816 [==&gt;...........................] - ETA: 5s 67395584/574710816 [==&gt;...........................] - ETA: 4s 75505664/574710816 [==&gt;...........................] - ETA: 4s 83886080/574710816 [===&gt;..........................] - ETA: 4s 92282880/574710816 [===&gt;..........................] - ETA: 4s100671488/574710816 [====&gt;.........................] - ETA: 4s109060096/574710816 [====&gt;.........................] - ETA: 4s118808576/574710816 [=====&gt;........................] - ETA: 3s126230528/574710816 [=====&gt;........................] - ETA: 3s134225920/574710816 [======&gt;.......................] - ETA: 3s143581184/574710816 [======&gt;.......................] - ETA: 3s151003136/574710816 [======&gt;.......................] - ETA: 3s161431552/574710816 [=======&gt;......................] - ETA: 3s167813120/574710816 [=======&gt;......................] - ETA: 3s176168960/574710816 [========&gt;.....................] - ETA: 3s184033280/574710816 [========&gt;.....................] - ETA: 3s191774720/574710816 [=========&gt;....................] - ETA: 3s198877184/574710816 [=========&gt;....................] - ETA: 2s203923456/574710816 [=========&gt;....................] - ETA: 2s209723392/574710816 [=========&gt;....................] - ETA: 3s219545600/574710816 [==========&gt;...................] - ETA: 2s227172352/574710816 [==========&gt;...................] - ETA: 2s235134976/574710816 [===========&gt;..................] - ETA: 2s246104064/574710816 [===========&gt;..................] - ETA: 2s256770048/574710816 [============&gt;.................] - ETA: 2s260055040/574710816 [============&gt;.................] - ETA: 2s270819328/574710816 [=============&gt;................] - ETA: 2s279298048/574710816 [=============&gt;................] - ETA: 2s286056448/574710816 [=============&gt;................] - ETA: 2s293609472/574710816 [==============&gt;...............] - ETA: 2s302882816/574710816 [==============&gt;...............] - ETA: 2s313147392/574710816 [===============&gt;..............] - ETA: 2s320880640/574710816 [===============&gt;..............] - ETA: 1s331243520/574710816 [================&gt;.............] - ETA: 1s340344832/574710816 [================&gt;.............] - ETA: 1s346562560/574710816 [=================&gt;............] - ETA: 1s356900864/574710816 [=================&gt;............] - ETA: 1s367443968/574710816 [==================&gt;...........] - ETA: 1s377348096/574710816 [==================&gt;...........] - ETA: 1s387768320/574710816 [===================&gt;..........] - ETA: 1s398581760/574710816 [===================&gt;..........] - ETA: 1s402661376/574710816 [====================&gt;.........] - ETA: 1s412213248/574710816 [====================&gt;.........] - ETA: 1s422469632/574710816 [=====================&gt;........] - ETA: 1s434307072/574710816 [=====================&gt;........] - ETA: 1s444219392/574710816 [======================&gt;.......] - ETA: 0s450068480/574710816 [======================&gt;.......] - ETA: 0s461725696/574710816 [=======================&gt;......] - ETA: 0s469770240/574710816 [=======================&gt;......] - ETA: 0s479395840/574710816 [========================&gt;.....] - ETA: 0s489136128/574710816 [========================&gt;.....] - ETA: 0s494936064/574710816 [========================&gt;.....] - ETA: 0s504619008/574710816 [=========================&gt;....] - ETA: 0s515252224/574710816 [=========================&gt;....] - ETA: 0s524697600/574710816 [==========================&gt;...] - ETA: 0s533274624/574710816 [==========================&gt;...] - ETA: 0s539721728/574710816 [===========================&gt;..] - ETA: 0s548921344/574710816 [===========================&gt;..] - ETA: 0s557858816/574710816 [============================&gt;.] - ETA: 0s564551680/574710816 [============================&gt;.] - ETA: 0s570859520/574710816 [============================&gt;.] - ETA: 0s574710816/574710816 [==============================] - 4s 0us/step</span></span>
+<span><span class="co">#&gt;      8192/574710816 [..............................] - ETA: 0s  4202496/574710816 [..............................] - ETA: 9s  8708096/574710816 [..............................] - ETA: 8s 20398080/574710816 [&gt;.............................] - ETA: 4s 30072832/574710816 [&gt;.............................] - ETA: 4s 33562624/574710816 [&gt;.............................] - ETA: 4s 42672128/574710816 [=&gt;............................] - ETA: 4s 50421760/574710816 [=&gt;............................] - ETA: 3s 58728448/574710816 [==&gt;...........................] - ETA: 3s 69140480/574710816 [==&gt;...........................] - ETA: 3s 76996608/574710816 [===&gt;..........................] - ETA: 3s 88399872/574710816 [===&gt;..........................] - ETA: 3s 95821824/574710816 [====&gt;.........................] - ETA: 3s107954176/574710816 [====&gt;.........................] - ETA: 2s119488512/574710816 [=====&gt;........................] - ETA: 2s125837312/574710816 [=====&gt;........................] - ETA: 2s136830976/574710816 [======&gt;.......................] - ETA: 2s149192704/574710816 [======&gt;.......................] - ETA: 2s159653888/574710816 [=======&gt;......................] - ETA: 2s172228608/574710816 [=======&gt;......................] - ETA: 2s176644096/574710816 [========&gt;.....................] - ETA: 2s188006400/574710816 [========&gt;.....................] - ETA: 2s192946176/574710816 [=========&gt;....................] - ETA: 2s202858496/574710816 [=========&gt;....................] - ETA: 2s214859776/574710816 [==========&gt;...................] - ETA: 2s227295232/574710816 [==========&gt;...................] - ETA: 2s239034368/574710816 [===========&gt;..................] - ETA: 1s250773504/574710816 [============&gt;.................] - ETA: 1s261586944/574710816 [============&gt;.................] - ETA: 1s273268736/574710816 [=============&gt;................] - ETA: 1s281976832/574710816 [=============&gt;................] - ETA: 1s294412288/574710816 [==============&gt;...............] - ETA: 1s306692096/574710816 [===============&gt;..............] - ETA: 1s314826752/574710816 [===============&gt;..............] - ETA: 1s326598656/574710816 [================&gt;.............] - ETA: 1s337903616/574710816 [================&gt;.............] - ETA: 1s348930048/574710816 [=================&gt;............] - ETA: 1s358883328/574710816 [=================&gt;............] - ETA: 1s360718336/574710816 [=================&gt;............] - ETA: 1s372416512/574710816 [==================&gt;...........] - ETA: 1s383598592/574710816 [===================&gt;..........] - ETA: 1s395198464/574710816 [===================&gt;..........] - ETA: 1s402661376/574710816 [====================&gt;.........] - ETA: 0s413007872/574710816 [====================&gt;.........] - ETA: 0s424935424/574710816 [=====================&gt;........] - ETA: 0s436715520/574710816 [=====================&gt;........] - ETA: 0s447307776/574710816 [======================&gt;.......] - ETA: 0s457195520/574710816 [======================&gt;.......] - ETA: 0s467689472/574710816 [=======================&gt;......] - ETA: 0s478535680/574710816 [=======================&gt;......] - ETA: 0s490946560/574710816 [========================&gt;.....] - ETA: 0s502693888/574710816 [=========================&gt;....] - ETA: 0s513253376/574710816 [=========================&gt;....] - ETA: 0s523927552/574710816 [==========================&gt;...] - ETA: 0s535748608/574710816 [==========================&gt;...] - ETA: 0s546709504/574710816 [===========================&gt;..] - ETA: 0s557867008/574710816 [============================&gt;.] - ETA: 0s569016320/574710816 [============================&gt;.] - ETA: 0s574710816/574710816 [==============================] - 3s 0us/step</span></span>
 <span></span>
 <span><span class="co"># get predictions</span></span>
 <span><span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">images</span><span class="op">)</span></span>
@@ -373,11 +373,11 @@ <h2 id="pre-trained-model-resnet50">Pre-trained model ResNet50<a class="anchor"
 <code class="sourceCode R"><span><span class="co"># Load the model</span></span>
 <span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/keras/man/application_resnet.html" class="external-link">application_resnet50</a></span><span class="op">(</span>include_top <span class="op">=</span> <span class="cn">TRUE</span>, weights <span class="op">=</span> <span class="st">"imagenet"</span><span class="op">)</span> </span>
 <span><span class="co">#&gt; Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5</span></span>
-<span><span class="co">#&gt;      8192/102967424 [..............................] - ETA: 0s  1769472/102967424 [..............................] - ETA: 2s  4202496/102967424 [&gt;.............................] - ETA: 2s 13901824/102967424 [===&gt;..........................] - ETA: 1s 16785408/102967424 [===&gt;..........................] - ETA: 1s 25174016/102967424 [======&gt;.......................] - ETA: 1s 33562624/102967424 [========&gt;.....................] - ETA: 0s 40542208/102967424 [==========&gt;...................] - ETA: 0s 46374912/102967424 [============&gt;.................] - ETA: 0s 50339840/102967424 [=============&gt;................] - ETA: 0s 58728448/102967424 [================&gt;.............] - ETA: 0s 67117056/102967424 [==================&gt;...........] - ETA: 0s 75505664/102967424 [====================&gt;.........] - ETA: 0s 86515712/102967424 [========================&gt;.....] - ETA: 0s 92282880/102967424 [=========================&gt;....] - ETA: 0s102967424/102967424 [==============================] - 1s 0us/step</span></span>
+<span><span class="co">#&gt;      8192/102967424 [..............................] - ETA: 0s  2678784/102967424 [..............................] - ETA: 1s  6660096/102967424 [&gt;.............................] - ETA: 1s 16785408/102967424 [===&gt;..........................] - ETA: 0s 25452544/102967424 [======&gt;.......................] - ETA: 0s 33562624/102967424 [========&gt;.....................] - ETA: 0s 44752896/102967424 [============&gt;.................] - ETA: 0s 51822592/102967424 [==============&gt;...............] - ETA: 0s 63348736/102967424 [=================&gt;............] - ETA: 0s 67117056/102967424 [==================&gt;...........] - ETA: 0s 75505664/102967424 [====================&gt;.........] - ETA: 0s 87261184/102967424 [========================&gt;.....] - ETA: 0s 95813632/102967424 [==========================&gt;...] - ETA: 0s102967424/102967424 [==============================] - 1s 0us/step</span></span>
 <span></span>
 <span><span class="co"># get predictions</span></span>
 <span><span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">images</span><span class="op">)</span></span>
-<span><span class="co">#&gt; 1/1 - 1s - 958ms/epoch - 958ms/step</span></span>
+<span><span class="co">#&gt; 1/1 - 1s - 915ms/epoch - 915ms/step</span></span>
 <span><span class="va">pred_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/keras/man/imagenet_decode_predictions.html" class="external-link">imagenet_decode_predictions</a></span><span class="op">(</span><span class="va">pred</span>, top <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># store the top prediction with the class label in a data.frame</span></span>
diff --git a/articles/detailed_overview.html b/articles/detailed_overview.html
index bbd494d..7ccbb48 100644
--- a/articles/detailed_overview.html
+++ b/articles/detailed_overview.html
@@ -1189,7 +1189,7 @@ <h3 id="fields">Fields<a class="anchor" aria-label="anchor" href="#fields"></a>
 <span></span>
 <span><span class="co"># mean squared error</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="op">(</span><span class="va">y</span> <span class="op">-</span> <span class="va">y_true</span><span class="op">)</span><span class="op">**</span><span class="fl">2</span><span class="op">)</span></span>
-<span><span class="co">#&gt; [1] 1.258253e-15</span></span></code></pre></div>
+<span><span class="co">#&gt; [1] 2.146431e-15</span></span></code></pre></div>
 <p>Since we did not pass any arguments for the input and output names,
 they were generated and stored in the list format in the
 <code>input_names</code> and <code>output_names</code> fields. Remember
@@ -1247,12 +1247,12 @@ <h3 id="fields">Fields<a class="anchor" aria-label="anchor" href="#fields"></a>
 <span><span class="va">converter_modified</span><span class="op">$</span><span class="fu">model</span><span class="op">(</span><span class="va">x</span>, channels_first <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span><span class="co">#&gt; [[1]]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt;  0.3696  0.3305  0.2999</span></span>
+<span><span class="co">#&gt;  0.1759  0.3844  0.4396</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{1,3} ]</span></span>
 <span><span class="va">converter</span><span class="op">$</span><span class="fu">model</span><span class="op">(</span><span class="va">x</span>, channels_first <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span><span class="co">#&gt; [[1]]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt;  0.5707  0.1511  0.2782</span></span>
+<span><span class="co">#&gt;  0.1839  0.6621  0.1540</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{1,3} ]</span></span></code></pre></div>
 </details><p>In addition, the default <code><a href="https://rdrr.io/r/base/print.html" class="external-link">print()</a></code> function for R6 classes
 has been overwritten so that all important properties, fields and
@@ -2956,14 +2956,14 @@ <h4 id="array-type-array">Array (<code>type = 'array'</code>)<a class="anchor" a
 <span><span class="co"># Show the result</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">result_array</span><span class="op">)</span></span>
 <span><span class="co">#&gt; List of 2</span></span>
-<span><span class="co">#&gt;  $ Input_1: num [1:5, 1:10, 1:10, 1:2, 1:2] 0 -0.0044 0 0 0 ...</span></span>
+<span><span class="co">#&gt;  $ Input_1: num [1:5, 1:10, 1:10, 1:2, 1:2] 0 -0.010647 -0.000724 0.019982 -0.066683 ...</span></span>
 <span><span class="co">#&gt;   ..- attr(*, "dimnames")=List of 5</span></span>
 <span><span class="co">#&gt;   .. ..$ : NULL</span></span>
 <span><span class="co">#&gt;   .. ..$ : chr [1:10] "H1" "H2" "H3" "H4" ...</span></span>
 <span><span class="co">#&gt;   .. ..$ : chr [1:10] "W1" "W2" "W3" "W4" ...</span></span>
 <span><span class="co">#&gt;   .. ..$ : chr [1:2] "C1" "C2"</span></span>
 <span><span class="co">#&gt;   .. ..$ : chr [1:2] "Y1" "Y2"</span></span>
-<span><span class="co">#&gt;  $ Input_2: num [1:5, 1:11, 1:2] -0.0118 0.1835 0.3181 0.2518 -0.0914 ...</span></span>
+<span><span class="co">#&gt;  $ Input_2: num [1:5, 1:11, 1:2] -0.3843 -0.0541 0.0198 -0.3302 -0.3646 ...</span></span>
 <span><span class="co">#&gt;   ..- attr(*, "dimnames")=List of 3</span></span>
 <span><span class="co">#&gt;   .. ..$ : NULL</span></span>
 <span><span class="co">#&gt;   .. ..$ : chr [1:11] "X1" "X2" "X3" "X4" ...</span></span>
@@ -3026,27 +3026,27 @@ <h4 id="array-type-array">Array (<code>type = 'array'</code>)<a class="anchor" a
 <span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">result_array</span><span class="op">)</span></span>
 <span><span class="co">#&gt; List of 2</span></span>
 <span><span class="co">#&gt;  $ Output_1:List of 2</span></span>
-<span><span class="co">#&gt;   ..$ Input_1: num [1:5, 1:10, 1:10, 1:2, 1:2] 0 0 -0.0456 -0.0776 -0.0144 ...</span></span>
+<span><span class="co">#&gt;   ..$ Input_1: num [1:5, 1:10, 1:10, 1:2, 1:2] -0.0471 0 -0.0607 -0.0237 0.0665 ...</span></span>
 <span><span class="co">#&gt;   .. ..- attr(*, "dimnames")=List of 5</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : NULL</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:10] "H1" "H2" "H3" "H4" ...</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:10] "W1" "W2" "W3" "W4" ...</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:2] "C1" "C2"</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:2] "Y1" "Y2"</span></span>
-<span><span class="co">#&gt;   ..$ Input_2: num [1:5, 1:11, 1:2] -0.381 -0.141 -0.085 -0.328 -0.217 ...</span></span>
+<span><span class="co">#&gt;   ..$ Input_2: num [1:5, 1:11, 1:2] -0.76 -0.112 -0.288 -0.404 -0.305 ...</span></span>
 <span><span class="co">#&gt;   .. ..- attr(*, "dimnames")=List of 3</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : NULL</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:11] "X1" "X2" "X3" "X4" ...</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:2] "Y1" "Y2"</span></span>
 <span><span class="co">#&gt;  $ Output_2:List of 2</span></span>
-<span><span class="co">#&gt;   ..$ Input_1: num [1:5, 1:10, 1:10, 1:2, 1:2] 0 0 0.0256 -0.0252 0.0197 ...</span></span>
+<span><span class="co">#&gt;   ..$ Input_1: num [1:5, 1:10, 1:10, 1:2, 1:2] 0.00645 0 -0.06356 -0.03888 0.00438 ...</span></span>
 <span><span class="co">#&gt;   .. ..- attr(*, "dimnames")=List of 5</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : NULL</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:10] "H1" "H2" "H3" "H4" ...</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:10] "W1" "W2" "W3" "W4" ...</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:2] "C1" "C2"</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:2] "Y1" "Y3"</span></span>
-<span><span class="co">#&gt;   ..$ Input_2: num [1:5, 1:11, 1:2] 0.0363 -0.0735 0.0882 0.0343 0.0959 ...</span></span>
+<span><span class="co">#&gt;   ..$ Input_2: num [1:5, 1:11, 1:2] -0.2497 0.0451 0.0683 -0.216 -0.0155 ...</span></span>
 <span><span class="co">#&gt;   .. ..- attr(*, "dimnames")=List of 3</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : NULL</span></span>
 <span><span class="co">#&gt;   .. .. ..$ : chr [1:11] "X1" "X2" "X3" "X4" ...</span></span>
@@ -3331,7 +3331,7 @@ <h3 id="plot-single-results-plot">Plot single results <code>plot()</code><a clas
 <span><span class="co"># Show plot (it also includes a drop down menu for selecting the colorscale)</span></span>
 <span><span class="va">p</span></span></code></pre></div>
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 <code class="sourceCode R"><span><span class="co"># We can do the same for models with image data. In addition, you can define</span></span>
 <span><span class="co"># the aggregation function for the channels</span></span>
 <span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">img_grad</span>,</span>
@@ -3559,7 +3559,7 @@ <h3 id="plot-summarized-results-plot_global">Plot summarized results <code>plot_
 <span><span class="co"># point and toggle the plot type 'Boxplot' or 'Violin')</span></span>
 <span><span class="va">p</span></span></code></pre></div>
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 <code class="sourceCode R"><span><span class="co"># We can do the same for models with image data (but have to use the method</span></span>
 <span><span class="co"># `plot_global`, since no boxplots are created). In addition, you can define</span></span>
 <span><span class="co"># the aggregation function for the channels</span></span>
@@ -3589,7 +3589,7 @@ <h3 id="plot-summarized-results-plot_global">Plot summarized results <code>plot_
 <span><span class="co"># and a scale for selecting the respective percentile)</span></span>
 <span><span class="va">p</span></span></code></pre></div>
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 </div>
 <div class="section level3">
 <h3 id="advanced-plotting">Advanced plotting<a class="anchor" aria-label="anchor" href="#advanced-plotting"></a>
@@ -4251,7 +4251,7 @@ <h4 id="plots-based-on-plotly">Plots based on plotly<a class="anchor" aria-label
 <span><span class="co"># Show the whole plot</span></span>
 <span><span class="va">p</span></span></code></pre></div>
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class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Now you can select specific rows and columns for in-depth investigation,</span></span>
 <span><span class="co"># e.g. only the result for output "Y2"</span></span>
 <span><span class="va">p_new</span> <span class="op">&lt;-</span> <span class="va">p</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span></span>
@@ -4265,7 +4265,7 @@ <h4 id="plots-based-on-plotly">Plots based on plotly<a class="anchor" aria-label
 <span><span class="co"># Show the subplot</span></span>
 <span><span class="va">p_new</span></span></code></pre></div>
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 </li>
 <li>
 <p><code>[[</code> : With this basic generic indexing function, you can
@@ -4291,7 +4291,7 @@ <h4 id="plots-based-on-plotly">Plots based on plotly<a class="anchor" aria-label
 <span><span class="co"># Show the plot</span></span>
 <span><span class="va">p_new</span></span></code></pre></div>
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 </li>
 <li>
 <code>plot</code>, <code>print</code> and <code>show</code> : These
@@ -4321,7 +4321,7 @@ <h4 id="plots-based-on-plotly">Plots based on plotly<a class="anchor" aria-label
 <span><span class="co"># e.g. the margins</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span>, margin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.03</span>, <span class="fl">0.03</span>, <span class="fl">0.01</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
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diff --git a/articles/innsight.html b/articles/innsight.html
index 988d31e..d2cefff 100644
--- a/articles/innsight.html
+++ b/articles/innsight.html
@@ -721,7 +721,7 @@ <h3 id="step-2-apply-selected-method">Step 2: Apply selected method<a class="anc
 <span><span class="co">#&gt; ── <span style="font-weight: bold;">Result (</span><span style="color: #00BB00; font-weight: bold;">result</span><span style="font-weight: bold;">)</span> ──</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt;   ─ Shape: (10, 3, 10, 10, 5)</span></span>
-<span><span class="co">#&gt;   ─ Range: min: -0.155959, median: 2.80777e-05, max: 0.179385</span></span>
+<span><span class="co">#&gt;   ─ Range: min: -0.24646, median: 0.000167129, max: 0.284328</span></span>
 <span><span class="co">#&gt;   ─ Number of NaN values: 0</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">────────────────────────────────────────────────────────────────────────────────</span></span></span></code></pre></div>
@@ -791,18 +791,18 @@ <h4 id="get-results">Get results<a class="anchor" aria-label="anchor" href="#get
 <span></span>
 <span><span class="co"># Show the first 5 rows</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">result_data.frame</span>, <span class="fl">5</span><span class="op">)</span></span>
-<span><span class="co">#&gt;     data model_input model_output feature feature_2 channel output_node</span></span>
-<span><span class="co">#&gt; 1 data_1     Input_1     Output_1      H1        W1      C1          Y1</span></span>
-<span><span class="co">#&gt; 2 data_2     Input_1     Output_1      H1        W1      C1          Y1</span></span>
-<span><span class="co">#&gt; 3 data_3     Input_1     Output_1      H1        W1      C1          Y1</span></span>
-<span><span class="co">#&gt; 4 data_4     Input_1     Output_1      H1        W1      C1          Y1</span></span>
-<span><span class="co">#&gt; 5 data_5     Input_1     Output_1      H1        W1      C1          Y1</span></span>
-<span><span class="co">#&gt;          value      pred decomp_sum decomp_goal input_dimension</span></span>
-<span><span class="co">#&gt; 1 8.197683e-04 0.2944385  0.7761073   0.7761093               3</span></span>
-<span><span class="co">#&gt; 2 4.790732e-06 0.2841029  0.9008601   0.9008622               3</span></span>
-<span><span class="co">#&gt; 3 8.719084e-06 0.1334664  0.1914765   0.1914773               3</span></span>
-<span><span class="co">#&gt; 4 5.955151e-05 0.1918168  0.5691521   0.5691535               3</span></span>
-<span><span class="co">#&gt; 5 1.469243e-05 0.1676757  0.2357421   0.2357431               3</span></span></code></pre></div>
+<span><span class="co">#&gt;     data model_input model_output feature feature_2 channel output_node value</span></span>
+<span><span class="co">#&gt; 1 data_1     Input_1     Output_1      H1        W1      C1          Y1     0</span></span>
+<span><span class="co">#&gt; 2 data_2     Input_1     Output_1      H1        W1      C1          Y1     0</span></span>
+<span><span class="co">#&gt; 3 data_3     Input_1     Output_1      H1        W1      C1          Y1     0</span></span>
+<span><span class="co">#&gt; 4 data_4     Input_1     Output_1      H1        W1      C1          Y1     0</span></span>
+<span><span class="co">#&gt; 5 data_5     Input_1     Output_1      H1        W1      C1          Y1     0</span></span>
+<span><span class="co">#&gt;        pred decomp_sum decomp_goal input_dimension</span></span>
+<span><span class="co">#&gt; 1 0.1909436  0.5172616   0.5172629               3</span></span>
+<span><span class="co">#&gt; 2 0.1270780  0.2318697   0.2318704               3</span></span>
+<span><span class="co">#&gt; 3 0.2253208  0.6011138   0.6011152               3</span></span>
+<span><span class="co">#&gt; 4 0.2393590  0.8190562   0.8190582               3</span></span>
+<span><span class="co">#&gt; 5 0.1498721  0.1812416   0.1812423               3</span></span></code></pre></div>
 </details><details><summary><code>torch_tensor</code>
 </summary><div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Get result (make sure 'deeplift_dense' is defined!)</span></span>
@@ -890,7 +890,7 @@ <h4 id="plot-results">Plot results<a class="anchor" aria-label="anchor" href="#p
 <code class="sourceCode R"><span><span class="co"># Create a plotly plot for the first output</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">lrp_cnn</span>, aggr_channels <span class="op">=</span> <span class="st">"norm"</span>, output_idx <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>, as_plotly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
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function (<strong>ggplot2</strong>)
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function (<strong>ggplot2</strong>)
 </summary><div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Create boxplot for the first two output classes</span></span>
 <span><span class="fu"><a href="../reference/plot_global.html">plot_global</a></span><span class="op">(</span><span class="va">smooth_dense</span>, output_idx <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">)</span></span></code></pre></div>
@@ -909,7 +909,7 @@ <h4 id="plot-results">Plot results<a class="anchor" aria-label="anchor" href="#p
 <span>  ref_data_idx <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">55</span><span class="op">)</span>, as_plotly <span class="op">=</span> <span class="cn">TRUE</span></span>
 <span><span class="op">)</span></span></code></pre></div>
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diff --git a/index.html b/index.html
index 75ff003..e491c90 100644
--- a/index.html
+++ b/index.html
@@ -18,7 +18,7 @@
 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
 <!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
 <script src="pkgdown.js"></script><meta property="og:title" content="Get the Insights of Your Neural Network">
-<meta property="og:description" content="Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., Connection Weights described by Olden et al. (2004) &lt;doi:10.1016/j.ecolmodel.2004.03.013&gt;, layer-wise relevance propagation (LRP) described by Bach et al. (2015) &lt;doi:10.1371/journal.pone.0130140&gt;, deep learning important features (DeepLIFT) described by Shrikumar et al. (2017) &lt;arXiv:1704.02685&gt; and gradient-based methods like SmoothGrad described by Smilkov et al. (2017) &lt;arXiv:1706.03825&gt;, Gradient x Input described by Baehrens et al. (2009) &lt;arXiv:0912.1128&gt; or Vanilla Gradient. Details can be found in the accompanying scientific paper: Koenen &amp; Wright (2024, Journal of Statistical Software, &lt;doi:10.18637/jss.v111.i08&gt;).">
+<meta property="og:description" content="Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., Connection Weights described by Olden et al. (2004) &lt;doi:10.1016/j.ecolmodel.2004.03.013&gt;, layer-wise relevance propagation (LRP) described by Bach et al. (2015) &lt;doi:10.1371/journal.pone.0130140&gt;, deep learning important features (DeepLIFT) described by Shrikumar et al. (2017) &lt;doi:10.48550/arXiv.1704.02685&gt; and gradient-based methods like SmoothGrad described by Smilkov et al. (2017) &lt;doi:10.48550/arXiv.1706.03825&gt;, Gradient x Input or Vanilla Gradient. Details can be found in the accompanying scientific paper: Koenen &amp; Wright (2024, Journal of Statistical Software, &lt;doi:10.18637/jss.v111.i08&gt;).">
 <meta property="og:image" content="https://bips-hb.github.io/innsight/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -112,6 +112,7 @@ <h2 id="table-of-contents">Table of contents<a class="anchor" aria-label="anchor
 <li><a href="#usage">Usage</a></li>
 <li><a href="#examples">Examples</a></li>
 <li><a href="#contributing-and-future-work">Contributing and future work</a></li>
+<li><a href="#citation">Citation</a></li>
 <li><a href="#funding">Funding</a></li>
 </ul>
 </div>
@@ -149,7 +150,7 @@ <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#i
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h2>
-<p>The package can be installed directly from CRAN and the development version from GitHub with the following commands (successful installation of <a href="https://www.r-project.org/nosvn/pandoc/devtools.html" class="external-link"><code>devtools</code></a> is required)</p>
+<p>The package can be installed directly from CRAN and the development version from GitHub with the following commands (successful installation of <a href="https://devtools.r-lib.org/" class="external-link"><code>devtools</code></a> is required)</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Stable version</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"innsight"</span><span class="op">)</span></span>
diff --git a/news/index.html b/news/index.html
index 5fd057f..9bea3b7 100644
--- a/news/index.html
+++ b/news/index.html
@@ -70,7 +70,7 @@ <h1 data-toc-skip>Changelog <small></small></h1>
     </div>
 
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.3.1" id="innsight-031">innsight 0.3.1<a class="anchor" aria-label="anchor" href="#innsight-031"></a></h2>
+<h2 class="page-header" data-toc-text="0.3.1" id="innsight-031">innsight 0.3.1<small>2024-11-26</small><a class="anchor" aria-label="anchor" href="#innsight-031"></a></h2>
 <ul><li>Added citation information for the accompanying scientific publication in the documentation and the package metadata.</li>
 </ul></div>
     <div class="section level2">
diff --git a/pkgdown.yml b/pkgdown.yml
index 9f45a39..e0c476a 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -7,7 +7,7 @@ articles:
   Example_2_penguin: Example_2_penguin.html
   articles/Example_3_imagenet: Example_3_imagenet.html
   innsight: innsight.html
-last_built: 2024-11-25T15:13Z
+last_built: 2024-11-26T11:11Z
 urls:
   reference: https://bips-hb.github.io/innsight/reference
   article: https://bips-hb.github.io/innsight/articles
diff --git a/reference/ConnectionWeights-1.png b/reference/ConnectionWeights-1.png
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diff --git a/reference/ConnectionWeights.html b/reference/ConnectionWeights.html
index 26a06c8..b84e129 100644
--- a/reference/ConnectionWeights.html
+++ b/reference/ConnectionWeights.html
@@ -336,17 +336,17 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="co"># Print the head of the result as a data.frame</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">cw</span>, <span class="st">"data.frame"</span><span class="op">)</span>, <span class="fl">5</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     data model_input model_output feature output_node       value pred</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 data_1     Input_1     Output_1     Car          Y1 -0.08359230   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 data_1     Input_1     Output_1     Cat          Y1  0.00281797   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3 data_1     Input_1     Output_1     Dog          Y1 -0.03157729   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4 data_1     Input_1     Output_1   Plane          Y1 -0.04800894   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5 data_1     Input_1     Output_1   Horse          Y1  0.11799815   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    decomp_sum decomp_goal input_dimension</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 -0.04236242          NA               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 -0.04236242          NA               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3 -0.04236242          NA               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4 -0.04236242          NA               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5 -0.04236242          NA               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 data_1     Input_1     Output_1     Car          Y1 -0.07570085   NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 data_1     Input_1     Output_1     Cat          Y1  0.14107053   NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3 data_1     Input_1     Output_1     Dog          Y1  0.03844491   NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 data_1     Input_1     Output_1   Plane          Y1  0.01427058   NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 data_1     Input_1     Output_1   Horse          Y1  0.02426034   NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   decomp_sum decomp_goal input_dimension</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  0.1423455          NA               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2  0.1423455          NA               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3  0.1423455          NA               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4  0.1423455          NA               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5  0.1423455          NA               1</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">cw</span><span class="op">)</span></span></span>
diff --git a/reference/Converter.html b/reference/Converter.html
index 44ae6f7..3d5c284 100644
--- a/reference/Converter.html
+++ b/reference/Converter.html
@@ -498,9 +498,9 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="fu">torch_model</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9976  0.0024</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9872  0.0128</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8210  0.1790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2681  0.7319</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9596  0.0404</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0007  0.9993</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
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diff --git a/reference/DeepLift.html b/reference/DeepLift.html
index 41b9c23..fd8b5dc 100644
--- a/reference/DeepLift.html
+++ b/reference/DeepLift.html
@@ -364,19 +364,19 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">deeplift</span>, <span class="st">"torch.tensor"</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0324  0.1035</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0098 -0.0208</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0002  0.0016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0021  0.0044</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0200 -0.0112</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.01 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.9748  2.2200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -20.6816 -7.8347</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.4164 -15.6942</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.5192 -0.2258</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -24.3737 -0.8069</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.01 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.7222 -8.1462</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    5.3992 -11.2003</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.3143  0.9006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.1261  0.0677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.8885  7.6406</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0606 -0.0762</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0159  0.0202</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0021 -0.0613</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0024 -0.0001</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1220 -0.1552</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,5,2} ]</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
diff --git a/reference/DeepSHAP-1.png b/reference/DeepSHAP-1.png
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diff --git a/reference/DeepSHAP.html b/reference/DeepSHAP.html
index ab7df57..4b67ce8 100644
--- a/reference/DeepSHAP.html
+++ b/reference/DeepSHAP.html
@@ -368,18 +368,19 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">deepshap</span>, <span class="st">"torch.tensor"</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0651  0.0392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0380 -0.0096</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0476  0.0307</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0092 -0.2352</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2131 -0.0606</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0584 -0.0094</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0613 -0.0389</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0377 -0.1117</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0167  0.0666</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1756 -0.1989</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0091  0.0338</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0502  0.0088</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1032  0.1311</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0391  0.1757</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2952 -0.1921</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.01 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.6767  1.8734</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    2.7245  1.6984</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.9866  6.2845</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -9.2389 -11.0859</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.5355 -14.9231</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,5,2} ]</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
diff --git a/reference/ExpectedGradient-1.png b/reference/ExpectedGradient-1.png
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diff --git a/reference/ExpectedGradient.html b/reference/ExpectedGradient.html
index 44b55f6..d33787c 100644
--- a/reference/ExpectedGradient.html
+++ b/reference/ExpectedGradient.html
@@ -351,18 +351,18 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">int_grad</span>, <span class="st">"torch.tensor"</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1000  0.0878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0021  0.0007</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0996  0.1257</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1344  0.0339</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0010  0.0009</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1056 -0.0647</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0911 -0.1844</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3474  0.0474</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0296 -0.0090</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1124  0.0793</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0066 -0.0610</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0722  0.0667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0153 -0.0442</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1591  0.1607</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1433 -0.5850</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1815 -0.0080</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0002 -0.4099</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2464  0.3155</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1108 -0.0212</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2221  0.0353</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,5,2} ]</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
diff --git a/reference/Gradient-1.png b/reference/Gradient-1.png
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diff --git a/reference/Gradient.html b/reference/Gradient.html
index fbc7a72..b304781 100644
--- a/reference/Gradient.html
+++ b/reference/Gradient.html
@@ -307,18 +307,18 @@ <h2>Examples</h2>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Print the result as a data.frame for first 5 rows</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">grad</span>, <span class="st">"data.frame"</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>,<span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     data model_input model_output feature output_node        value      pred</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 data_1     Input_1     Output_1     Car     Buy it! -0.030447572 0.4150715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 data_2     Input_1     Output_1     Car     Buy it! -0.009859789 0.5594321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3 data_3     Input_1     Output_1     Car     Buy it! -0.058986809 0.4705018</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4 data_4     Input_1     Output_1     Car     Buy it! -0.025297035 0.3901630</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5 data_5     Input_1     Output_1     Car     Buy it!  0.122279078 0.5434746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   decomp_sum  decomp_goal input_dimension</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  0.2734411  0.231000125               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2  0.2785822  0.191599935               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3  0.1721654  0.036616117               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4  0.1508674  0.017486557               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  0.1571460 -0.006727435               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     data model_input model_output feature output_node       value      pred</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 data_1     Input_1     Output_1     Car     Buy it! -0.22055109 0.2389102</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 data_2     Input_1     Output_1     Car     Buy it! -0.04505988 0.3497100</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3 data_3     Input_1     Output_1     Car     Buy it! -0.07866308 0.3972008</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 data_4     Input_1     Output_1     Car     Buy it! -0.17334284 0.4340879</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 data_5     Input_1     Output_1     Car     Buy it! -0.28752613 0.3949615</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    decomp_sum decomp_goal input_dimension</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 -0.34503235  -0.8250242               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 -0.02350425  -0.3810766               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3 -0.27761280  -0.2443061               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 -0.43665135  -0.5205401               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 -0.48284297  -0.3785598               1</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">grad</span>, output_idx <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">)</span></span></span>
diff --git a/reference/IntegratedGradient-1.png b/reference/IntegratedGradient-1.png
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diff --git a/reference/IntegratedGradient.html b/reference/IntegratedGradient.html
index f745fce..6cb7dc5 100644
--- a/reference/IntegratedGradient.html
+++ b/reference/IntegratedGradient.html
@@ -366,18 +366,19 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">int_grad</span>, <span class="st">"torch.tensor"</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0187 -0.0920</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0569  0.1472</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0063 -0.0029</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0104  0.0430</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0783  0.0907</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0081 -0.0003</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3639 -0.2948</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0676  0.1020</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0034 -0.0095</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0408 -0.0195</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0339 -0.4154</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0832  0.1284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0541  0.0228</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0204 -0.0419</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1618  0.1809</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.01 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8516  0.3024</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -44.3494 -37.2617</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.6202  9.0055</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.9486  6.6413</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.6768 -2.2878</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,5,2} ]</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
diff --git a/reference/LIME-1.png b/reference/LIME-1.png
index 7264463fca00b57de9b937c4d32e4bc9cea793fc..a07a22539a3d6b09b468cd1717592a84cdc47094 100644
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diff --git a/reference/LIME.html b/reference/LIME.html
index 6a6eaa7..d016b9f 100644
--- a/reference/LIME.html
+++ b/reference/LIME.html
@@ -362,15 +362,15 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">lime</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>,, <span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , Buy it!</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              Car         Cat          Dog       Plane        Horse</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1,]  0.01209771 -0.01481332 -0.009736266 -0.10478784 -0.003591877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] -0.01597157 -0.01663282  0.036495406 -0.01725605  0.001127589</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>              Car          Cat         Dog      Plane        Horse</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1,]  0.01024396  0.002091614 0.003054726 0.05778524  0.002752911</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] -0.02735157 -0.003952547 0.015483490 0.05437228 -0.003670471</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , Don't buy it!</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              Car        Cat          Dog      Plane        Horse</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] -0.01209771 0.01481332  0.009736266 0.10478784  0.003591881</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [2,]  0.01597157 0.01663281 -0.036495406 0.01725605 -0.001127590</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>              Car          Cat          Dog       Plane        Horse</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] -0.01024397 -0.002091614 -0.003054727 -0.05778524 -0.002752913</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [2,]  0.02735157  0.003952547 -0.015483488 -0.05437228  0.003670471</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
diff --git a/reference/LRP-1.png b/reference/LRP-1.png
index d60fa980da8801c86adc6dcc238bc03800d8c92d..bebfbba15fad4400a4c303919ac50b5b4cc8de50 100644
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diff --git a/reference/LRP.html b/reference/LRP.html
index fa08f5d..fd9305c 100644
--- a/reference/LRP.html
+++ b/reference/LRP.html
@@ -377,15 +377,15 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">lrp</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>,,<span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , Y1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                X1         X2          X3          X4          X5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] -0.005197162 0.09221318 0.123634420 -0.09311052 -0.05758811</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] -0.144790113 0.23646691 0.004719616 -0.02465526 -0.03035261</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               X1          X2          X3         X4         X5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] -0.03884860  0.07278180 -0.13302113 0.01201744 0.02257666</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] -0.01999244 -0.02485864  0.07431371 0.04680417 0.04504487</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , Y2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               X1          X2          X3          X4            X5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] 0.001424963  0.05690468  0.06997978 -0.05945887 -0.0279963557</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] 0.264975578 -0.01856175 -0.03811911  0.02537468  0.0003216181</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               X1          X2          X3         X4           X5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] -0.01525109  0.15358011 -0.02789297 0.22921170 -0.012848184</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] -0.05115769 -0.02275296  0.01611406 0.07071713 -0.006808903</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
diff --git a/reference/SHAP-1.png b/reference/SHAP-1.png
index 746829db04bb593bef04dfc3db2e515ea00c431c..75c54511b3beb8c1145c2996e8742d296448c573 100644
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diff --git a/reference/SHAP.html b/reference/SHAP.html
index 1bb3819..be89a66 100644
--- a/reference/SHAP.html
+++ b/reference/SHAP.html
@@ -349,15 +349,15 @@ <h2>Examples</h2>
 <span class="r-in"><span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">shap</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>,, <span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , Buy it!</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Car          Cat        Dog        Plane      Horse</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] 2.004075e-02 -0.004541248 0.02945241  0.011841953 0.02441075</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] 4.377961e-05 -0.004451841 0.00000000 -0.006747901 0.01584131</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               Car       Cat        Dog       Plane       Horse</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] -0.002318889 0.1419315 0.00699687 -0.04044771 -0.01266500</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [2,]  0.010592848 0.0275993 0.00000000 -0.10005927 -0.08379063</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> , , Don't buy it!</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Car         Cat          Dog       Plane        Horse</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] -0.001068413  0.02920985  0.008951962 -0.01135647 -0.015357137</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] -0.039483368 -0.03196919 -0.141717911  0.01197505  0.005489886</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>             Car         Cat          Dog      Plane      Horse</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] 0.01180428  0.03085333 0.0114286542 0.10106999 0.04726356</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] 0.01826233 -0.05126607 0.0001776218 0.02029669 0.03772181</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
diff --git a/reference/SmoothGrad-1.png b/reference/SmoothGrad-1.png
index bd6b6d2b333edbee56f3547d1b4d2149da6ec2f6..0c7d98d1cb98aefb34cdf6900f0a6054157bfff3 100644
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diff --git a/reference/SmoothGrad.html b/reference/SmoothGrad.html
index 10c8a58..e63635c 100644
--- a/reference/SmoothGrad.html
+++ b/reference/SmoothGrad.html
@@ -332,18 +332,18 @@ <h2>Examples</h2>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Print the result as a data.frame for first 5 rows</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="get_result.html">get_result</a></span><span class="op">(</span><span class="va">smoothgrad</span>, <span class="st">"data.frame"</span><span class="op">)</span>, <span class="fl">5</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     data model_input model_output feature output_node      value      pred</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 data_1     Input_1     Output_1      X1          Y1 0.12929699 0.4230998</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 data_2     Input_1     Output_1      X1          Y1 0.15932587 0.4300725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3 data_3     Input_1     Output_1      X1          Y1 0.03305231 0.4687854</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4 data_4     Input_1     Output_1      X1          Y1 0.08979802 0.4702520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5 data_5     Input_1     Output_1      X1          Y1 0.09232053 0.4437090</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   decomp_sum decomp_goal input_dimension</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 0.46422906  -0.3100612               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 0.43931256  -0.2815557               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3 0.09656284  -0.1250208               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4 0.20407192  -0.1191326               1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5 0.26503902  -0.2261224               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     data model_input model_output feature output_node       value      pred</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 data_1     Input_1     Output_1      X1          Y1  0.05168447 0.3943423</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 data_2     Input_1     Output_1      X1          Y1 -0.04433363 0.3219931</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3 data_3     Input_1     Output_1      X1          Y1 -0.09659281 0.3022582</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 data_4     Input_1     Output_1      X1          Y1 -0.10550380 0.3712806</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 data_5     Input_1     Output_1      X1          Y1 -0.11714230 0.4389589</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    decomp_sum decomp_goal input_dimension</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 -0.17348729  -0.4290954               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 -0.39392206  -0.7446272               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3 -0.14815050  -0.8365675               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 -0.22493906  -0.5267272               1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 -0.07728353  -0.2453885               1</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Plot the result for both classes</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">smoothgrad</span>, output_idx <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">)</span></span></span>
diff --git a/reference/innsight-package.html b/reference/innsight-package.html
index 0b53b1d..e84cd75 100644
--- a/reference/innsight-package.html
+++ b/reference/innsight-package.html
@@ -125,13 +125,14 @@ <h2>Details</h2>
 innsight" by Koenen &amp; Wright (2024), published in the
 <em>Journal of Statistical Software</em>. For a detailed explanation of the
 methods and use cases, please refer to the publication
-(doi: <a href="https://doi.org/10.18637/jss.v111.i08" class="external-link">10.18637/jss.v111.i08</a>).</p>
+(doi: <a href="https://doi.org/10.18637/jss.v111.i08" class="external-link">doi:10.18637/jss.v111.i08</a>
+).</p>
     </div>
     <div id="references">
     <h2>References</h2>
     <p>Koenen, N., &amp; Wright, M. N. (2024). Interpreting Deep Neural Networks with
 the Package innsight. Journal of Statistical Software, 111(8), 1-52. doi:
-<a href="https://doi.org/10.18637/jss.v111.i08" class="external-link">10.18637/jss.v111.i08</a></p>
+<a href="https://doi.org/10.18637/jss.v111.i08" class="external-link">doi:10.18637/jss.v111.i08</a></p>
     </div>
     <div id="see-also">
     <h2>See also</h2>