From af2e1540cdad2fd00bb6216a38a754ff748629ad Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Fri, 25 Oct 2019 02:10:08 +0200
Subject: Static documentation rebuilt by pkgdown

---
 docs/reference/sigma_twocomp.html | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

(limited to 'docs/reference/sigma_twocomp.html')

diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html
index ef62212a..d95e1c28 100644
--- a/docs/reference/sigma_twocomp.html
+++ b/docs/reference/sigma_twocomp.html
@@ -36,13 +36,8 @@
 
 
 <meta property="og:title" content="Two component error model — sigma_twocomp" />
-<meta property="og:description" content="Function describing the standard deviation of the measurement error 
-  in dependence of the measured value \(y\):
-$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$
-    sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2)
-This is the error model used for example by Werner et al. (1978). The model
-  proposed by Rocke and Lorenzato (1995) can be written in this form as well,
-  but assumes approximate lognormal distribution of errors for high values of y." />
+<meta property="og:description" content="Function describing the standard deviation of the measurement error in
+dependence of the measured value \(y\):" />
 <meta name="twitter:card" content="summary" />
 
 
@@ -139,13 +134,8 @@ This is the error model used for example by Werner et al. (1978). The model
     </div>
 
     <div class="ref-description">
-    <p>Function describing the standard deviation of the measurement error 
-  in dependence of the measured value \(y\):</p>
-<p>$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$
-    sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2)</p>
-<p>This is the error model used for example by Werner et al. (1978). The model
-  proposed by Rocke and Lorenzato (1995) can be written in this form as well,
-  but assumes approximate lognormal distribution of errors for high values of y.</p>
+    <p>Function describing the standard deviation of the measurement error in
+dependence of the measured value \(y\):</p>
     </div>
 
     <pre class="usage"><span class='fu'>sigma_twocomp</span>(<span class='no'>y</span>, <span class='no'>sigma_low</span>, <span class='no'>rsd_high</span>)</pre>
@@ -159,18 +149,27 @@ This is the error model used for example by Werner et al. (1978). The model
     </tr>
     <tr>
       <th>sigma_low</th>
-      <td><p>The asymptotic minimum of the standard deviation for low observed values</p></td>
+      <td><p>The asymptotic minimum of the standard deviation for low
+observed values</p></td>
     </tr>
     <tr>
       <th>rsd_high</th>
-      <td><p>The coefficient describing the increase of the standard deviation with 
-    the magnitude of the observed value</p></td>
+      <td><p>The coefficient describing the increase of the standard
+deviation with the magnitude of the observed value</p></td>
     </tr>
     </table>
 
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
     <p>The standard deviation of the response variable.</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma =
+sqrt(sigma_low^2 + y^2 * rsd_high^2)</p>
+<p>This is the error model used for example by Werner et al. (1978). The model
+proposed by Rocke and Lorenzato (1995) can be written in this form as well,
+but assumes approximate lognormal distribution of errors for high values of
+y.</p>
     <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
 
     <p>Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978)
@@ -185,6 +184,7 @@ This is the error model used for example by Werner et al. (1978). The model
     <ul class="nav nav-pills nav-stacked">
       <li><a href="#arguments">Arguments</a></li>
       <li><a href="#value">Value</a></li>
+      <li><a href="#details">Details</a></li>
       <li><a href="#references">References</a></li>
     </ul>
 
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