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livioivil · Nov 3, 2023 · 5a3320f · 5a3320f
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diff --git a/README.html b/README.html
@@ -366,6 +366,18 @@ <h2>Set up</h2>
 library(devtools)
 install_github(&quot;livioivil/flipscores&quot;)</code></pre>
 </div>
+<div id="references" class="section level2">
+<h2>References</h2>
+<p>J Hemerik, JJ Goeman and L Finos (2019) Robust testing in generalized
+linear models by sign-flipping score contributions. Journal of the Royal
+Statistical Society Series B: Statistical Methodology, Volume 82, Issue
+3, July 2020, Pages 841–864.<br />
+<a href="https://doi.org/10.1111/rssb.12369" class="uri">https://doi.org/10.1111/rssb.12369</a></p>
+<p>R De Santis, J Goeman, J Hemerik, L Finos (2022) Inference in
+generalized linear models with robustness to misspecified variances
+arXiv: 2209.13918.<br />
+<a href="https://arxiv.org/abs/2209.13918" class="uri">https://arxiv.org/abs/2209.13918</a></p>
+</div>
 <div id="some-examples" class="section level2">
 <h2>Some examples</h2>
 <pre class="r"><code>library(flipscores)
@@ -386,10 +398,10 @@ <h2>Some examples</h2>
 #&gt; 
 #&gt; Coefficients:
 #&gt;             Estimate   Score Std. Error z value Part. Cor Pr(&gt;|z|)   
-#&gt; (Intercept)  -0.1026 -0.7229     2.7127 -0.2665    -0.088    0.757   
-#&gt; ZB           -0.1501 -0.7125     2.1789 -0.3270    -0.104    0.641   
-#&gt; ZC            0.1633  0.8106     2.2232  0.3646     0.117    0.689   
-#&gt; X             0.9439 16.2062     4.7272  3.4283     0.671    0.009 **
+#&gt; (Intercept)  -0.1026 -0.7229     2.7127 -0.2665    -0.088   0.7460   
+#&gt; ZB           -0.1501 -0.7125     2.1789 -0.3270    -0.104   0.6564   
+#&gt; ZC            0.1633  0.8106     2.2232  0.3646     0.117   0.6924   
+#&gt; X             0.9439 16.2062     4.7272  3.4283     0.671   0.0098 **
 #&gt; ---
 #&gt; Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 #&gt; 
@@ -411,8 +423,8 @@ <h2>Some examples</h2>
 #&gt; 
 #&gt; Model: Y ~ Z + X
 #&gt;   Df   Score Pr(&gt;Score)   
-#&gt; Z  2 0.73408     0.7022   
-#&gt; X  1 0.02957     0.0090 **
+#&gt; Z  2 0.77184     0.6984   
+#&gt; X  1 0.02977     0.0098 **
 #&gt; ---
 #&gt; Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 # or
@@ -427,7 +439,7 @@ <h2>Some examples</h2>
 #&gt; Model 1: Y ~ Z
 #&gt; Model 2: Y ~ Z + X
 #&gt;                    Df    Score Pr(&gt;Score)  
-#&gt; Model 2 vs Model 1  1 0.029235     0.0122 *
+#&gt; Model 2 vs Model 1  1 0.029586     0.0108 *
 #&gt; ---
 #&gt; Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 # and
@@ -442,7 +454,7 @@ <h2>Some examples</h2>
 #&gt; Model 1: Y ~ X
 #&gt; Model 2: Y ~ Z + X
 #&gt;                    Df  Score Pr(&gt;Score)
-#&gt; Model 2 vs Model 1  2 1.4179     0.5172</code></pre>
+#&gt; Model 2 vs Model 1  2 1.4245     0.5244</code></pre>
 <div id="negative-binomial" class="section level3">
 <h3>Negative Binomial</h3>
 <pre class="r"><code>set.seed(1)
@@ -492,10 +504,10 @@ <h3>Negative Binomial</h3>
 #&gt; -2.0746  -0.7748  -0.1086   0.4617   2.0435  
 #&gt; 
 #&gt; Coefficients:
-#&gt;             Estimate    Score Std. Error  z value Part. Cor Pr(&gt;|t|)    
-#&gt; (Intercept) -0.15365 -1.84162    3.42399 -0.53786    -0.087   0.5674    
-#&gt; x            0.92089 14.93128    4.42610  3.37346     0.547   0.0008 ***
-#&gt; z           -0.01282 -0.72457    7.24491 -0.10001    -0.016   0.9580    
+#&gt;             Estimate    Score Std. Error  z value Part. Cor Pr(&gt;|t|)   
+#&gt; (Intercept) -0.15365 -1.84162    3.42399 -0.53786    -0.087   0.5808   
+#&gt; x            0.92089 14.93128    4.42610  3.37346     0.547   0.0014 **
+#&gt; z           -0.01282 -0.72457    7.24491 -0.10001    -0.016   0.9612   
 #&gt; ---
 #&gt; Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 #&gt; 
@@ -508,18 +520,6 @@ <h3>Negative Binomial</h3>
 #&gt; Number of Fisher Scoring iterations: 1</code></pre>
 </div>
 </div>
-<div id="references" class="section level2">
-<h2>References</h2>
-<p>J Hemerik, JJ Goeman and L Finos (2019) Robust testing in generalized
-linear models by sign-flipping score contributions. Journal of the Royal
-Statistical Society Series B: Statistical Methodology, Volume 82, Issue
-3, July 2020, Pages 841–864.<br />
-<a href="https://doi.org/10.1111/rssb.12369" class="uri">https://doi.org/10.1111/rssb.12369</a></p>
-<p>R De Santis, J Goeman, J Hemerik, L Finos (2022) Inference in
-generalized linear models with robustness to misspecified variances
-arXiv: 2209.13918.<br />
-<a href="https://arxiv.org/abs/2209.13918" class="uri">https://arxiv.org/abs/2209.13918</a></p>
-</div>
 <div id="bug-reports" class="section level2">
 <h2>Bug reports</h2>
 <p>If you encounter a bug, please file a <a href="https://github.com/tidyverse/reprex">reprex</a> (minimal

diff --git a/README.md b/README.md
@@ -49,11 +49,11 @@ summary(mod)
 #> -1.6910  -0.5792   0.1012   0.4900   1.0440  
 #> 
 #> Coefficients:
-#>             Estimate   Score Std. Error z value Part. Cor Pr(>|z|)  
-#> (Intercept)  -0.1026 -0.7229     2.7127 -0.2665    -0.088   0.7578  
-#> ZB           -0.1501 -0.7125     2.1789 -0.3270    -0.104   0.6700  
-#> ZC            0.1633  0.8106     2.2232  0.3646     0.117   0.6928  
-#> X             0.9439 16.2062     4.7272  3.4283     0.671   0.0118 *
+#>             Estimate   Score Std. Error z value Part. Cor Pr(>|z|)   
+#> (Intercept)  -0.1026 -0.7229     2.7127 -0.2665    -0.088   0.7460   
+#> ZB           -0.1501 -0.7125     2.1789 -0.3270    -0.104   0.6564   
+#> ZC            0.1633  0.8106     2.2232  0.3646     0.117   0.6924   
+#> X             0.9439 16.2062     4.7272  3.4283     0.671   0.0098 **
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 #> 
@@ -74,9 +74,9 @@ anova(mod)
 #> Inference is provided by FlipScores approach (5000 sign flips).
 #> 
 #> Model: Y ~ Z + X
-#>   Df   Score Pr(>Score)  
-#> Z  2 0.76757     0.6946  
-#> X  1 0.02942     0.0118 *
+#>   Df   Score Pr(>Score)   
+#> Z  2 0.77184     0.6984   
+#> X  1 0.02977     0.0098 **
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 # or
@@ -91,7 +91,7 @@ anova(mod0,mod)
 #> Model 1: Y ~ Z
 #> Model 2: Y ~ Z + X
 #>                    Df    Score Pr(>Score)  
-#> Model 2 vs Model 1  1 0.029502     0.0114 *
+#> Model 2 vs Model 1  1 0.029586     0.0108 *
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 # and
@@ -106,7 +106,7 @@ anova(mod0,mod)
 #> Model 1: Y ~ X
 #> Model 2: Y ~ Z + X
 #>                    Df  Score Pr(>Score)
-#> Model 2 vs Model 1  2 1.4314     0.5128
+#> Model 2 vs Model 1  2 1.4245     0.5244
 ```
 
 ### Negative Binomial
@@ -159,10 +159,10 @@ summary(mod)
 #> -2.0746  -0.7748  -0.1086   0.4617   2.0435  
 #> 
 #> Coefficients:
-#>             Estimate    Score Std. Error  z value Part. Cor Pr(>|t|)    
-#> (Intercept) -0.15365 -1.84162    3.42399 -0.53786    -0.087   0.5766    
-#> x            0.92089 14.93128    4.42610  3.37346     0.547   0.0008 ***
-#> z           -0.01282 -0.72457    7.24491 -0.10001    -0.016   0.9610    
+#>             Estimate    Score Std. Error  z value Part. Cor Pr(>|t|)   
+#> (Intercept) -0.15365 -1.84162    3.42399 -0.53786    -0.087   0.5808   
+#> x            0.92089 14.93128    4.42610  3.37346     0.547   0.0014 **
+#> z           -0.01282 -0.72457    7.24491 -0.10001    -0.016   0.9612   
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 #>