Small fixes to the manual

2 files changed, 4 insertions, 4 deletions

diff --git a/vignettes/gmkin_manual.Rmd b/vignettes/gmkin_manual.Rmd
index 6522916..dd1ae7f 100644
--- a/vignettes/gmkin_manual.Rmd
+++ b/vignettes/gmkin_manual.Rmd
@@ -115,7 +115,7 @@ setting up fits as shown below.
 
 ![datasets and models](img/datasetsnmodels.png)
 
-For editing, adding or removing datasets or models, you need to click on an
+For editing, adding or removing datasets or models, you need to double-click on an
 entry in the respective list. 
 
 For setting up a fit of a specific model to a specific dataset, the model and
@@ -393,7 +393,7 @@ the extension of the proposed filename for saving the plot.
 ### Confidence interval plots
 
 Whenever a new fit has been configured or a run of a fit has been completed, the plotting
-area is update with the abovementioned plot of the data and the current model solution.
+area is updated with the abovementioned plot of the data and the current model solution.
 
 In addition, a confidence interval plot is shown below this conventional plot. In case
 a fit has been run and confidence intervals were successfully calculated for the fit (i.e. 
diff --git a/vignettes/gmkin_manual.html b/vignettes/gmkin_manual.html
index 4e0f576..54b7ff0 100644
--- a/vignettes/gmkin_manual.html
+++ b/vignettes/gmkin_manual.html
@@ -122,7 +122,7 @@ if (window.hljs && document.readyState && document.readyState === "complete") {
 <h2>Datasets and Models</h2>
 <p>The project loaded at the start of gmkin contains two datasets and four kinetic models. These are listed to the left under the heading “Datasets and Models”, together with a button for setting up fits as shown below.</p>
 <p><img src="data:image/png;base64,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" alt="datasets and models" /></p>
-<p>For editing, adding or removing datasets or models, you need to click on an entry in the respective list.</p>
+<p>For editing, adding or removing datasets or models, you need to double-click on an entry in the respective list.</p>
 <p>For setting up a fit of a specific model to a specific dataset, the model and the dataset should be selected by clicking on them. If they are compatible, clicking the button “Configure fit for selected dataset and model” will set up the fit and open the “Plotting and Fitting” tab to the right.</p>
 </div>
 <div id="dataset-editor" class="section level2">
@@ -233,7 +233,7 @@ Model cost at call  37 :  371.2134 </code></pre>
 </div>
 <div id="confidence-interval-plots" class="section level3">
 <h3>Confidence interval plots</h3>
-<p>Whenever a new fit has been configured or a run of a fit has been completed, the plotting area is update with the abovementioned plot of the data and the current model solution.</p>
+<p>Whenever a new fit has been configured or a run of a fit has been completed, the plotting area is updated with the abovementioned plot of the data and the current model solution.</p>
 <p>In addition, a confidence interval plot is shown below this conventional plot. In case a fit has been run and confidence intervals were successfully calculated for the fit (i.e. if the model was not overparameterised and no other problems occurred), the confidence intervals are graphically displayed as bars as shown below.</p>
 <p><img src="data:image/png;base64,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" alt="conficence" /></p>
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