From b2cae525039705ffac56f809a333396a660e247d Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 12:07:58 +0200
Subject: [PATCH 1/6] Fixed logos

---
 inst/netrics.png     | Bin 0 -> 158184 bytes
 man/figures/logo.png | Bin 187347 -> 158184 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 inst/netrics.png

diff --git a/inst/netrics.png b/inst/netrics.png
new file mode 100644
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From 60921a185a51a3a41a770b962f26b6768daf063d Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Wed, 22 Apr 2026 08:30:07 +0200
Subject: [PATCH 2/6] Fixed centrality tutorials

---
 inst/tutorials/tutorial3/centrality.Rmd  |  76 +--
 inst/tutorials/tutorial3/centrality.html | 567 ++++++++++++++++-------
 2 files changed, 437 insertions(+), 206 deletions(-)

diff --git a/inst/tutorials/tutorial3/centrality.Rmd b/inst/tutorials/tutorial3/centrality.Rmd
index 3af1ad4..215becf 100644
--- a/inst/tutorials/tutorial3/centrality.Rmd
+++ b/inst/tutorials/tutorial3/centrality.Rmd
@@ -134,7 +134,7 @@ rowSums(mat) == colSums(mat)
 
 ```{r degreesum-hint-2, purl = FALSE}
 # Or by using a built in command in manynet like this:
-node_degree(ison_brandes, normalized = FALSE)
+node_by_degree(ison_brandes, normalized = FALSE)
 ```
 
 ```{r degreesum-solution}
@@ -143,7 +143,7 @@ mat <- as_matrix(ison_brandes)
 degrees <- rowSums(mat)
 rowSums(mat) == colSums(mat)
 # You can also just use a built in command in manynet though:
-node_degree(ison_brandes, normalized = FALSE)
+node_by_degree(ison_brandes, normalized = FALSE)
 ```
 
 ```{r degreesum-Q, echo=FALSE, purl = FALSE}
@@ -184,7 +184,7 @@ though there are more elaborate ways to do this in base and grid graphics.
 
 ```{r distrib-solution}
 # distribution of degree centrality scores of nodes
-plot(node_degree(ison_brandes))
+plot(node_by_degree(ison_brandes))
 ```
 
 What's plotted here by default is both the degree distribution as a histogram,
@@ -218,7 +218,7 @@ question("What can the degree distribution tell us?",
 ## Other centralities
 
 Other measures of centrality can be a little trickier to calculate by hand.
-Fortunately, we can use functions from `{manynet}` to help calculate the
+Fortunately, we can use functions from `{netrics}` to help calculate the
 `r gloss("betweenness")`, `r gloss("closeness")`, and `r gloss("eigenvector")` centralities for each node in the network.
 Let's collect the vectors of these centralities for the `ison_brandes` dataset:
 
@@ -227,27 +227,27 @@ Let's collect the vectors of these centralities for the `ison_brandes` dataset:
 ```
 
 ```{r micent-hint-1, purl = FALSE}
-# Use the node_betweenness() function to calculate the
+# Use the node_by_betweenness() function to calculate the
 # betweenness centralities of nodes in a network
-node_betweenness(ison_brandes)
+node_by_betweenness(ison_brandes)
 ```
 
 ```{r micent-hint-2, purl = FALSE}
-# Use the node_closeness() function to calculate the 
+# Use the node_by_closeness() function to calculate the 
 # closeness centrality of nodes in a network
-node_closeness(ison_brandes)
+node_by_closeness(ison_brandes)
 ```
 
 ```{r micent-hint-3, purl = FALSE}
-# Use the node_eigenvector() function to calculate 
+# Use the node_by_eigenvector() function to calculate 
 # the eigenvector centrality of nodes in a network
-node_eigenvector(ison_brandes)
+node_by_eigenvector(ison_brandes)
 ```
 
 ```{r micent-solution}
-node_betweenness(ison_brandes)
-node_closeness(ison_brandes)
-node_eigenvector(ison_brandes)
+node_by_betweenness(ison_brandes)
+node_by_closeness(ison_brandes)
+node_by_eigenvector(ison_brandes)
 ```
 
 What is returned here are vectors of betweenness, closeness, and eigenvector 
@@ -301,7 +301,7 @@ Try to answer the following questions for yourself:
 - what does Bonacich mean when he says that `r gloss("power")` and influence are not the same thing?
 - can you think of a real-world example when an actor might be central but not powerful, or powerful but not central?
 
-Note that all centrality measures in `{manynet}` return normalized
+Note that all centrality measures in `{netrics}` return normalized
 scores by default -- 
 for the raw scores, include `normalized = FALSE` in the function as an extra argument.
 
@@ -312,9 +312,9 @@ Now, can you create degree distributions for each of these?
 ```
 
 ```{r otherdist-solution}
-plot(node_betweenness(ison_brandes))
-plot(node_closeness(ison_brandes))
-plot(node_eigenvector(ison_brandes))
+plot(node_by_betweenness(ison_brandes))
+plot(node_by_closeness(ison_brandes))
+plot(node_by_eigenvector(ison_brandes))
 ```
 
 ## Plotting centrality
@@ -335,19 +335,19 @@ we can highlight which node or nodes hold the maximum score in red.
 ```{r ggid-solution}
 # plot the network, highlighting the node with the highest centrality score with a different colour
 ison_brandes %>%
-  mutate_nodes(color = node_is_max(node_degree())) %>%
+  mutate_nodes(color = node_is_max(node_by_degree())) %>%
   graphr(node_color = "color")
 
 ison_brandes %>%
-  mutate_nodes(color = node_is_max(node_betweenness())) %>%
+  mutate_nodes(color = node_is_max(node_by_betweenness())) %>%
   graphr(node_color = "color")
 
 ison_brandes %>%
-  mutate_nodes(color = node_is_max(node_closeness())) %>%
+  mutate_nodes(color = node_is_max(node_by_closeness())) %>%
   graphr(node_color = "color")
 
 ison_brandes %>%
-  mutate_nodes(color = node_is_max(node_eigenvector())) %>%
+  mutate_nodes(color = node_is_max(node_by_eigenvector())) %>%
   graphr(node_color = "color")
 ```
 
@@ -361,19 +361,19 @@ What can you see?
 
 ```{r ggid_twomode-solution}
 ison_brandes2 %>%
-  add_node_attribute("color", node_is_max(node_degree(ison_brandes2))) %>%
+  add_node_attribute("color", node_is_max(node_by_degree(ison_brandes2))) %>%
   graphr(node_color = "color", layout = "bipartite")
 
 ison_brandes2 %>%
-  add_node_attribute("color", node_is_max(node_betweenness(ison_brandes2))) %>%
+  add_node_attribute("color", node_is_max(node_by_betweenness(ison_brandes2))) %>%
   graphr(node_color = "color", layout = "bipartite")
 
 ison_brandes2 %>%
-  add_node_attribute("color", node_is_max(node_closeness(ison_brandes2))) %>%
+  add_node_attribute("color", node_is_max(node_by_closeness(ison_brandes2))) %>%
   graphr(node_color = "color", layout = "bipartite")
 
 ison_brandes2 %>%
-  add_node_attribute("color", node_is_max(node_eigenvector(ison_brandes2))) %>%
+  add_node_attribute("color", node_is_max(node_by_eigenvector(ison_brandes2))) %>%
   graphr(node_color = "color", layout = "bipartite")
 ```
 
@@ -391,7 +391,7 @@ question("Select all that are true for the two-mode Brandes network.",
 
 <img src="https://media.makeameme.org/created/centralization-here-i.jpg"/>
 
-`{manynet}` also implements network `r gloss("centralization")` functions.
+`{netrics}` also implements network `r gloss("centralization")` functions.
 Here we are no longer interested in the level of the node,
 but in the level of the whole network, 
 so the syntax replaces `node_` with `net_`:
@@ -401,10 +401,10 @@ so the syntax replaces `node_` with `net_`:
 ```
 
 ```{r centzn-solution}
-net_degree(ison_brandes)
-net_betweenness(ison_brandes)
-net_closeness(ison_brandes)
-print(net_eigenvector(ison_brandes), digits = 5)
+net_by_degree(ison_brandes)
+net_by_betweenness(ison_brandes)
+net_by_closeness(ison_brandes)
+print(net_by_eigenvector(ison_brandes), digits = 5)
 ```
 
 By default, scores are printed up to 3 decimal places,
@@ -428,18 +428,18 @@ but fortunately the `{patchwork}` package is here to help.
 
 ```{r multiplot-solution}
 ison_brandes <- ison_brandes %>%
-  add_node_attribute("degree", node_is_max(node_degree(ison_brandes))) %>%
-  add_node_attribute("betweenness", node_is_max(node_betweenness(ison_brandes))) %>%
-  add_node_attribute("closeness", node_is_max(node_closeness(ison_brandes))) %>%
-  add_node_attribute("eigenvector", node_is_max(node_eigenvector(ison_brandes)))
+  add_node_attribute("degree", node_is_max(node_by_degree(ison_brandes))) %>%
+  add_node_attribute("betweenness", node_is_max(node_by_betweenness(ison_brandes))) %>%
+  add_node_attribute("closeness", node_is_max(node_by_closeness(ison_brandes))) %>%
+  add_node_attribute("eigenvector", node_is_max(node_by_eigenvector(ison_brandes)))
 gd <- graphr(ison_brandes, node_color = "degree") + 
-  ggtitle("Degree", subtitle = round(net_degree(ison_brandes), 2))
+  ggtitle("Degree", subtitle = round(net_by_degree(ison_brandes), 2))
 gc <- graphr(ison_brandes, node_color = "closeness") + 
-  ggtitle("Closeness", subtitle = round(net_closeness(ison_brandes), 2))
+  ggtitle("Closeness", subtitle = round(net_by_closeness(ison_brandes), 2))
 gb <- graphr(ison_brandes, node_color = "betweenness") + 
-  ggtitle("Betweenness", subtitle = round(net_betweenness(ison_brandes), 2))
+  ggtitle("Betweenness", subtitle = round(net_by_betweenness(ison_brandes), 2))
 ge <- graphr(ison_brandes, node_color = "eigenvector") + 
-  ggtitle("Eigenvector", subtitle = round(net_eigenvector(ison_brandes), 2))
+  ggtitle("Eigenvector", subtitle = round(net_by_eigenvector(ison_brandes), 2))
 (gd | gb) / (gc | ge)
 # ggsave("brandes-centralities.pdf")
 ```
diff --git a/inst/tutorials/tutorial3/centrality.html b/inst/tutorials/tutorial3/centrality.html
index 6ee3cc1..d3ab251 100644
--- a/inst/tutorials/tutorial3/centrality.html
+++ b/inst/tutorials/tutorial3/centrality.html
@@ -13,7 +13,7 @@
 
 <meta name="progressive" content="false" />
 <meta name="allow-skip" content="false" />
-<meta name="learnr-version-prerender" content="0.11.5" />
+<meta name="learnr-version-prerender" content="0.11.6" />
 
 <title>Centrality</title>
 
@@ -205,7 +205,7 @@ <h2>Degree centrality</h2>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code># Or by using a built in command in manynet like this:
-node_degree(ison_brandes, normalized = FALSE)</code></pre>
+node_by_degree(ison_brandes, normalized = FALSE)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="degreesum-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
@@ -215,7 +215,7 @@ <h2>Degree centrality</h2>
 degrees &lt;- rowSums(mat)
 rowSums(mat) == colSums(mat)
 # You can also just use a built in command in manynet though:
-node_degree(ison_brandes, normalized = FALSE)</code></pre>
+node_by_degree(ison_brandes, normalized = FALSE)</code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
 <div data-label="degreesum-Q" class="tutorial-question panel-body">
@@ -252,7 +252,7 @@ <h2>Degree distributions</h2>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code># distribution of degree centrality scores of nodes
-plot(node_degree(ison_brandes))</code></pre>
+plot(node_by_degree(ison_brandes))</code></pre>
 </div>
 <p>What’s plotted here by default is both the degree distribution as a
 histogram, as well as a density plot overlaid on it in red.</p>
@@ -276,7 +276,7 @@ <h2>Degree distributions</h2>
 <div id="section-other-centralities" class="section level2">
 <h2>Other centralities</h2>
 <p>Other measures of centrality can be a little trickier to calculate by
-hand. Fortunately, we can use functions from <code>{manynet}</code> to
+hand. Fortunately, we can use functions from <code>{netrics}</code> to
 help calculate the
 <abbr><dfn title='The betweenness centrality of a node is the proportion of shortest paths between all pairs of nodes that pass through that node.'>
 betweenness </dfn></abbr>,
@@ -294,30 +294,30 @@ <h2>Other centralities</h2>
 <div class="tutorial-exercise-support" data-label="micent-hint-1"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code># Use the node_betweenness() function to calculate the
+<pre class="text"><code># Use the node_by_betweenness() function to calculate the
 # betweenness centralities of nodes in a network
-node_betweenness(ison_brandes)</code></pre>
+node_by_betweenness(ison_brandes)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="micent-hint-2"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code># Use the node_closeness() function to calculate the 
+<pre class="text"><code># Use the node_by_closeness() function to calculate the 
 # closeness centrality of nodes in a network
-node_closeness(ison_brandes)</code></pre>
+node_by_closeness(ison_brandes)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="micent-hint-3"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code># Use the node_eigenvector() function to calculate 
+<pre class="text"><code># Use the node_by_eigenvector() function to calculate 
 # the eigenvector centrality of nodes in a network
-node_eigenvector(ison_brandes)</code></pre>
+node_by_eigenvector(ison_brandes)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="micent-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>node_betweenness(ison_brandes)
-node_closeness(ison_brandes)
-node_eigenvector(ison_brandes)</code></pre>
+<pre class="text"><code>node_by_betweenness(ison_brandes)
+node_by_closeness(ison_brandes)
+node_by_eigenvector(ison_brandes)</code></pre>
 </div>
 <p>What is returned here are vectors of betweenness, closeness, and
 eigenvector scores for the nodes in the network. But what do they
@@ -354,7 +354,7 @@ <h2>Other centralities</h2>
 <li>can you think of a real-world example when an actor might be central
 but not powerful, or powerful but not central?</li>
 </ul>
-<p>Note that all centrality measures in <code>{manynet}</code> return
+<p>Note that all centrality measures in <code>{netrics}</code> return
 normalized scores by default – for the raw scores, include
 <code>normalized = FALSE</code> in the function as an extra
 argument.</p>
@@ -367,9 +367,9 @@ <h2>Other centralities</h2>
 <div class="tutorial-exercise-support" data-label="otherdist-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>plot(node_betweenness(ison_brandes))
-plot(node_closeness(ison_brandes))
-plot(node_eigenvector(ison_brandes))</code></pre>
+<pre class="text"><code>plot(node_by_betweenness(ison_brandes))
+plot(node_by_closeness(ison_brandes))
+plot(node_by_eigenvector(ison_brandes))</code></pre>
 </div>
 </div>
 <div id="section-plotting-centrality" class="section level2">
@@ -392,19 +392,19 @@ <h2>Plotting centrality</h2>
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code># plot the network, highlighting the node with the highest centrality score with a different colour
 ison_brandes %&gt;%
-  mutate_nodes(color = node_is_max(node_degree())) %&gt;%
+  mutate_nodes(color = node_is_max(node_by_degree())) %&gt;%
   graphr(node_color = &quot;color&quot;)
 
 ison_brandes %&gt;%
-  mutate_nodes(color = node_is_max(node_betweenness())) %&gt;%
+  mutate_nodes(color = node_is_max(node_by_betweenness())) %&gt;%
   graphr(node_color = &quot;color&quot;)
 
 ison_brandes %&gt;%
-  mutate_nodes(color = node_is_max(node_closeness())) %&gt;%
+  mutate_nodes(color = node_is_max(node_by_closeness())) %&gt;%
   graphr(node_color = &quot;color&quot;)
 
 ison_brandes %&gt;%
-  mutate_nodes(color = node_is_max(node_eigenvector())) %&gt;%
+  mutate_nodes(color = node_is_max(node_by_eigenvector())) %&gt;%
   graphr(node_color = &quot;color&quot;)</code></pre>
 </div>
 <p>How neat! Try it with the two-mode version. What can you see?</p>
@@ -419,19 +419,19 @@ <h2>Plotting centrality</h2>
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
 <pre class="text"><code>ison_brandes2 %&gt;%
-  add_node_attribute(&quot;color&quot;, node_is_max(node_degree(ison_brandes2))) %&gt;%
+  add_node_attribute(&quot;color&quot;, node_is_max(node_by_degree(ison_brandes2))) %&gt;%
   graphr(node_color = &quot;color&quot;, layout = &quot;bipartite&quot;)
 
 ison_brandes2 %&gt;%
-  add_node_attribute(&quot;color&quot;, node_is_max(node_betweenness(ison_brandes2))) %&gt;%
+  add_node_attribute(&quot;color&quot;, node_is_max(node_by_betweenness(ison_brandes2))) %&gt;%
   graphr(node_color = &quot;color&quot;, layout = &quot;bipartite&quot;)
 
 ison_brandes2 %&gt;%
-  add_node_attribute(&quot;color&quot;, node_is_max(node_closeness(ison_brandes2))) %&gt;%
+  add_node_attribute(&quot;color&quot;, node_is_max(node_by_closeness(ison_brandes2))) %&gt;%
   graphr(node_color = &quot;color&quot;, layout = &quot;bipartite&quot;)
 
 ison_brandes2 %&gt;%
-  add_node_attribute(&quot;color&quot;, node_is_max(node_eigenvector(ison_brandes2))) %&gt;%
+  add_node_attribute(&quot;color&quot;, node_is_max(node_by_eigenvector(ison_brandes2))) %&gt;%
   graphr(node_color = &quot;color&quot;, layout = &quot;bipartite&quot;)</code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
@@ -446,7 +446,7 @@ <h2>Plotting centrality</h2>
 <div id="section-centralization" class="section level2">
 <h2>Centralization</h2>
 <p><img src="https://media.makeameme.org/created/centralization-here-i.jpg"/></p>
-<p><code>{manynet}</code> also implements network
+<p><code>{netrics}</code> also implements network
 <abbr><dfn title='A measure of how unequal the centralities of the nodes in a network are.'>
 centralization </dfn></abbr> functions. Here we are no longer interested
 in the level of the node, but in the level of the whole network, so the
@@ -459,10 +459,10 @@ <h2>Centralization</h2>
 <div class="tutorial-exercise-support" data-label="centzn-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_degree(ison_brandes)
-net_betweenness(ison_brandes)
-net_closeness(ison_brandes)
-print(net_eigenvector(ison_brandes), digits = 5)</code></pre>
+<pre class="text"><code>net_by_degree(ison_brandes)
+net_by_betweenness(ison_brandes)
+net_by_closeness(ison_brandes)
+print(net_by_eigenvector(ison_brandes), digits = 5)</code></pre>
 </div>
 <p>By default, scores are printed up to 3 decimal places, but this can
 be modified and, in any case, the unrounded values are retained
@@ -484,18 +484,18 @@ <h2>Centralization</h2>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>ison_brandes &lt;- ison_brandes %&gt;%
-  add_node_attribute(&quot;degree&quot;, node_is_max(node_degree(ison_brandes))) %&gt;%
-  add_node_attribute(&quot;betweenness&quot;, node_is_max(node_betweenness(ison_brandes))) %&gt;%
-  add_node_attribute(&quot;closeness&quot;, node_is_max(node_closeness(ison_brandes))) %&gt;%
-  add_node_attribute(&quot;eigenvector&quot;, node_is_max(node_eigenvector(ison_brandes)))
+  add_node_attribute(&quot;degree&quot;, node_is_max(node_by_degree(ison_brandes))) %&gt;%
+  add_node_attribute(&quot;betweenness&quot;, node_is_max(node_by_betweenness(ison_brandes))) %&gt;%
+  add_node_attribute(&quot;closeness&quot;, node_is_max(node_by_closeness(ison_brandes))) %&gt;%
+  add_node_attribute(&quot;eigenvector&quot;, node_is_max(node_by_eigenvector(ison_brandes)))
 gd &lt;- graphr(ison_brandes, node_color = &quot;degree&quot;) + 
-  ggtitle(&quot;Degree&quot;, subtitle = round(net_degree(ison_brandes), 2))
+  ggtitle(&quot;Degree&quot;, subtitle = round(net_by_degree(ison_brandes), 2))
 gc &lt;- graphr(ison_brandes, node_color = &quot;closeness&quot;) + 
-  ggtitle(&quot;Closeness&quot;, subtitle = round(net_closeness(ison_brandes), 2))
+  ggtitle(&quot;Closeness&quot;, subtitle = round(net_by_closeness(ison_brandes), 2))
 gb &lt;- graphr(ison_brandes, node_color = &quot;betweenness&quot;) + 
-  ggtitle(&quot;Betweenness&quot;, subtitle = round(net_betweenness(ison_brandes), 2))
+  ggtitle(&quot;Betweenness&quot;, subtitle = round(net_by_betweenness(ison_brandes), 2))
 ge &lt;- graphr(ison_brandes, node_color = &quot;eigenvector&quot;) + 
-  ggtitle(&quot;Eigenvector&quot;, subtitle = round(net_eigenvector(ison_brandes), 2))
+  ggtitle(&quot;Eigenvector&quot;, subtitle = round(net_by_eigenvector(ison_brandes), 2))
 (gd | gb) / (gc | ge)
 # ggsave(&quot;brandes-centralities.pdf&quot;)</code></pre>
 </div>
@@ -586,9 +586,34 @@ <h2>Glossary</h2>
 <p>
 <script type="application/shiny-prerendered" data-context="server-start">
 library(learnr)
-library(manynet)
-library(autograph)
 knitr::opts_chunk$set(echo = FALSE)
+clear_glossary()
+learnr::random_phrases_add(language = "fr",
+                           praise = c("C'est génial!",
+                                      "Beau travail",
+                                      "Excellent travail!",
+                                      "Bravo!",
+                                      "Super!",
+                                      "Bien fait",
+                                      "Bien joué",
+                                      "Tu l'as fait!",
+                                      "Je savais que tu pouvais le faire.",
+                                      "Ça a l'air facile!",
+                                      "C'était un travail de première classe.",
+                                      "C'est ce que j'appelle un bon travail!"),
+                          encouragement = c("Bon effort",
+                                            "Vous l'avez presque maîtrisé!",
+                                            "Ça avance bien.",
+                                            "Continuez comme ça.",
+                                            "Continuez à travailler dur!",
+                                            "Vous apprenez vite!",
+                                            "Vous faites un excellent travail aujourd'hui."))
+learnr::random_phrases_add(language = "en", 
+                           praise = c("C'est génial!",
+                                      "Beau travail!",
+                                      "Bravo!",
+                                      "Super!"),
+                           encouragement = c("Bon effort"))
 ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)
 </script>
  
@@ -622,12 +647,30 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "coercion", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
+"                                      \"Bravo!\",", "                                      \"Super!\",", 
+"                                      \"Bien fait\",", "                                      \"Bien joué\",", 
+"                                      \"Tu l'as fait!\",", "                                      \"Je savais que tu pouvais le faire.\",", 
+"                                      \"Ça a l'air facile!\",", 
+"                                      \"C'était un travail de première classe.\",", 
+"                                      \"C'est ce que j'appelle un bon travail!\"),", 
+"                          encouragement = c(\"Bon effort\",", 
+"                                            \"Vous l'avez presque maîtrisé!\",", 
+"                                            \"Ça avance bien.\",", 
+"                                            \"Continuez comme ça.\",", 
+"                                            \"Continuez à travailler dur!\",", 
+"                                            \"Vous apprenez vite!\",", 
+"                                            \"Vous faites un excellent travail aujourd'hui.\"))", 
+"learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
+"                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
 "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "coercion", code = "", opts = list(
-        label = "\"coercion\"", exercise = "TRUE", purl = "FALSE"), 
-        engine = "r")), code_check = NULL, error_check = NULL, 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "coercion", 
+    code = "", opts = list(label = "\"coercion\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("# plot the one-mode version", 
     "graphr(ison_brandes)", "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)", 
     "# plot the two-mode version", "graphr(ison_brandes2, layout = \"bipartite\")"
@@ -665,13 +708,32 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "addingnames", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
+"                                      \"Bravo!\",", "                                      \"Super!\",", 
+"                                      \"Bien fait\",", "                                      \"Bien joué\",", 
+"                                      \"Tu l'as fait!\",", "                                      \"Je savais que tu pouvais le faire.\",", 
+"                                      \"Ça a l'air facile!\",", 
+"                                      \"C'était un travail de première classe.\",", 
+"                                      \"C'est ce que j'appelle un bon travail!\"),", 
+"                          encouragement = c(\"Bon effort\",", 
+"                                            \"Vous l'avez presque maîtrisé!\",", 
+"                                            \"Ça avance bien.\",", 
+"                                            \"Continuez comme ça.\",", 
+"                                            \"Continuez à travailler dur!\",", 
+"                                            \"Vous apprenez vite!\",", 
+"                                            \"Vous faites un excellent travail aujourd'hui.\"))", 
+"learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
+"                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
 "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "addingnames", code = "ison_brandes <- to_named(ison_brandes)", 
-        opts = list(label = "\"addingnames\"", exercise = "TRUE", 
-            purl = "FALSE"), engine = "r")), code_check = NULL, 
-    error_check = NULL, check = NULL, solution = structure(c("ison_brandes <- to_named(ison_brandes)", 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "addingnames", 
+    code = "ison_brandes <- to_named(ison_brandes)", opts = list(
+        label = "\"addingnames\"", exercise = "TRUE", purl = "FALSE"), 
+    engine = "r")), code_check = NULL, error_check = NULL, check = NULL, 
+    solution = structure(c("ison_brandes <- to_named(ison_brandes)", 
     "# plot network with names", "graphr(ison_brandes)"), chunk_opts = list(
         label = "addingnames-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
@@ -707,9 +769,28 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "degreesum", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
+"                                      \"Bravo!\",", "                                      \"Super!\",", 
+"                                      \"Bien fait\",", "                                      \"Bien joué\",", 
+"                                      \"Tu l'as fait!\",", "                                      \"Je savais que tu pouvais le faire.\",", 
+"                                      \"Ça a l'air facile!\",", 
+"                                      \"C'était un travail de première classe.\",", 
+"                                      \"C'est ce que j'appelle un bon travail!\"),", 
+"                          encouragement = c(\"Bon effort\",", 
+"                                            \"Vous l'avez presque maîtrisé!\",", 
+"                                            \"Ça avance bien.\",", 
+"                                            \"Continuez comme ça.\",", 
+"                                            \"Continuez à travailler dur!\",", 
+"                                            \"Vous apprenez vite!\",", 
+"                                            \"Vous faites un excellent travail aujourd'hui.\"))", 
+"learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
+"                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
 "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = "ison_brandes <- to_named(ison_brandes)", 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = "ison_brandes <- to_named(ison_brandes)", 
     chunks = list(list(label = "addingnames", code = "ison_brandes <- to_named(ison_brandes)", 
         opts = list(label = "\"addingnames\"", exercise = "TRUE", 
             purl = "FALSE"), engine = "r"), list(label = "degreesum", 
@@ -719,7 +800,7 @@ <h2>Glossary</h2>
     check = NULL, solution = structure(c("# manually calculate degree centrality", 
     "mat <- as_matrix(ison_brandes)", "degrees <- rowSums(mat)", 
     "rowSums(mat) == colSums(mat)", "# You can also just use a built in command in manynet though:", 
-    "node_degree(ison_brandes, normalized = FALSE)"), chunk_opts = list(
+    "node_by_degree(ison_brandes, normalized = FALSE)"), chunk_opts = list(
         label = "degreesum-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
         tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA, 
@@ -747,11 +828,11 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "degreesum-Q", 
     question = structure("Are the row sums the same as the column sums?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_bb08e6f", 
+    "character")), answers = list(structure(list(id = "lnr_ans_6ddfc41", 
         option = "Yes", value = "Yes", label = structure("Yes", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("That’s right, that’s because this is an undirected network.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_c5888ae", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_c238a69", 
         option = "No", value = "No", label = structure("No", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
     "tutorial_quiz_answer"))), button_labels = list(submit = structure("<span data-i18n=\"button.questionsubmit\">Submit Answer<\u002fspan>", html = TRUE, class = c("html", 
@@ -762,7 +843,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "degreesum-Q-answer", question = "degreesum-Q"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = FALSE, 
-    seed = 2050248179.04528, options = list()), class = c("learnr_radio", 
+    seed = 1852153514.63752, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
 
@@ -770,23 +851,23 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "degreeinterpQ", 
     question = structure("In what ways are higher degree nodes more ‘central’?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_6431b70", 
+    "character")), answers = list(structure(list(id = "lnr_ans_77c10bd", 
         option = "They have more power than other nodes", value = "They have more power than other nodes", 
         label = structure("They have more power than other nodes", html = TRUE, class = c("html", 
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@@ -800,7 +881,7 @@ <h2>Glossary</h2>
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+"learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
+"                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
 "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "otherdist", code = "", opts = list(
-        label = "\"otherdist\"", exercise = "TRUE", purl = "FALSE"), 
-        engine = "r")), code_check = NULL, error_check = NULL, 
-    check = NULL, solution = structure(c("plot(node_betweenness(ison_brandes))", 
-    "plot(node_closeness(ison_brandes))", "plot(node_eigenvector(ison_brandes))"
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "otherdist", 
+    code = "", opts = list(label = "\"otherdist\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
+    check = NULL, solution = structure(c("plot(node_by_betweenness(ison_brandes))", 
+    "plot(node_by_closeness(ison_brandes))", "plot(node_by_eigenvector(ison_brandes))"
     ), chunk_opts = list(label = "otherdist-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -1127,9 +1264,28 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "ggid", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
+"                                      \"Bravo!\",", "                                      \"Super!\",", 
+"                                      \"Bien fait\",", "                                      \"Bien joué\",", 
+"                                      \"Tu l'as fait!\",", "                                      \"Je savais que tu pouvais le faire.\",", 
+"                                      \"Ça a l'air facile!\",", 
+"                                      \"C'était un travail de première classe.\",", 
+"                                      \"C'est ce que j'appelle un bon travail!\"),", 
+"                          encouragement = c(\"Bon effort\",", 
+"                                            \"Vous l'avez presque maîtrisé!\",", 
+"                                            \"Ça avance bien.\",", 
+"                                            \"Continuez comme ça.\",", 
+"                                            \"Continuez à travailler dur!\",", 
+"                                            \"Vous apprenez vite!\",", 
+"                                            \"Vous faites un excellent travail aujourd'hui.\"))", 
+"learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
+"                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
 "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = "ison_brandes <- to_named(ison_brandes)", 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = "ison_brandes <- to_named(ison_brandes)", 
     chunks = list(list(label = "addingnames", code = "ison_brandes <- to_named(ison_brandes)", 
         opts = list(label = "\"addingnames\"", exercise = "TRUE", 
             purl = "FALSE"), engine = "r"), list(label = "ggid", 
@@ -1137,13 +1293,13 @@ <h2>Glossary</h2>
             exercise.setup = "\"addingnames\"", purl = "FALSE"), 
         engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("# plot the network, highlighting the node with the highest centrality score with a different colour", 
-    "ison_brandes %>%", "  mutate_nodes(color = node_is_max(node_degree())) %>%", 
+    "ison_brandes %>%", "  mutate_nodes(color = node_is_max(node_by_degree())) %>%", 
     "  graphr(node_color = \"color\")", "", "ison_brandes %>%", 
-    "  mutate_nodes(color = node_is_max(node_betweenness())) %>%", 
+    "  mutate_nodes(color = node_is_max(node_by_betweenness())) %>%", 
     "  graphr(node_color = \"color\")", "", "ison_brandes %>%", 
-    "  mutate_nodes(color = node_is_max(node_closeness())) %>%", 
+    "  mutate_nodes(color = node_is_max(node_by_closeness())) %>%", 
     "  graphr(node_color = \"color\")", "", "ison_brandes %>%", 
-    "  mutate_nodes(color = node_is_max(node_eigenvector())) %>%", 
+    "  mutate_nodes(color = node_is_max(node_by_eigenvector())) %>%", 
     "  graphr(node_color = \"color\")"), chunk_opts = list(label = "ggid-solution")), 
     tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -1178,20 +1334,39 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "ggid_twomode", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
+"                                      \"Bravo!\",", "                                      \"Super!\",", 
+"                                      \"Bien fait\",", "                                      \"Bien joué\",", 
+"                                      \"Tu l'as fait!\",", "                                      \"Je savais que tu pouvais le faire.\",", 
+"                                      \"Ça a l'air facile!\",", 
+"                                      \"C'était un travail de première classe.\",", 
+"                                      \"C'est ce que j'appelle un bon travail!\"),", 
+"                          encouragement = c(\"Bon effort\",", 
+"                                            \"Vous l'avez presque maîtrisé!\",", 
+"                                            \"Ça avance bien.\",", 
+"                                            \"Continuez comme ça.\",", 
+"                                            \"Continuez à travailler dur!\",", 
+"                                            \"Vous apprenez vite!\",", 
+"                                            \"Vous faites un excellent travail aujourd'hui.\"))", 
+"learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
+"                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
 "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "ggid_twomode", code = "# Instead of \"ison_brandes\", use \"ison_brandes2\"\n", 
-        opts = list(label = "\"ggid_twomode\"", exercise = "TRUE", 
-            purl = "FALSE"), engine = "r")), code_check = NULL, 
-    error_check = NULL, check = NULL, solution = structure(c("ison_brandes2 %>%", 
-    "  add_node_attribute(\"color\", node_is_max(node_degree(ison_brandes2))) %>%", 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "ggid_twomode", 
+    code = "# Instead of \"ison_brandes\", use \"ison_brandes2\"\n", 
+    opts = list(label = "\"ggid_twomode\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
+    check = NULL, solution = structure(c("ison_brandes2 %>%", 
+    "  add_node_attribute(\"color\", node_is_max(node_by_degree(ison_brandes2))) %>%", 
     "  graphr(node_color = \"color\", layout = \"bipartite\")", 
-    "", "ison_brandes2 %>%", "  add_node_attribute(\"color\", node_is_max(node_betweenness(ison_brandes2))) %>%", 
+    "", "ison_brandes2 %>%", "  add_node_attribute(\"color\", node_is_max(node_by_betweenness(ison_brandes2))) %>%", 
     "  graphr(node_color = \"color\", layout = \"bipartite\")", 
-    "", "ison_brandes2 %>%", "  add_node_attribute(\"color\", node_is_max(node_closeness(ison_brandes2))) %>%", 
+    "", "ison_brandes2 %>%", "  add_node_attribute(\"color\", node_is_max(node_by_closeness(ison_brandes2))) %>%", 
     "  graphr(node_color = \"color\", layout = \"bipartite\")", 
-    "", "ison_brandes2 %>%", "  add_node_attribute(\"color\", node_is_max(node_eigenvector(ison_brandes2))) %>%", 
+    "", "ison_brandes2 %>%", "  add_node_attribute(\"color\", node_is_max(node_by_eigenvector(ison_brandes2))) %>%", 
     "  graphr(node_color = \"color\", layout = \"bipartite\")"
     ), chunk_opts = list(label = "ggid_twomode-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
@@ -1222,16 +1397,16 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "brandes2quiz", 
     question = structure("Select all that are true for the two-mode Brandes network.", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_2ad2909", 
+    "character")), answers = list(structure(list(id = "lnr_ans_3d78332", 
         option = "Only one node is selected in each plot.", value = "Only one node is selected in each plot.", 
         label = structure("Only one node is selected in each plot.", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_1f429f3", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_5a22dd0", 
         option = "The maximum degree triangle has a higher degree than the maximum degree circle(s).", 
         value = "The maximum degree triangle has a higher degree than the maximum degree circle(s).", 
         label = structure("The maximum degree triangle has a higher degree than the maximum degree circle(s).", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_8b15239", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_6e3c5f6", 
         option = "No node is ever the most central according to two or more different centrality measures.", 
         value = "No node is ever the most central according to two or more different centrality measures.", 
         label = structure("No node is ever the most central according to two or more different centrality measures.", html = TRUE, class = c("html", 
@@ -1244,7 +1419,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "brandes2quiz-answer", question = "brandes2quiz"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 1746889698.18654, options = list()), class = c("learnr_radio", 
+    seed = 417716823.805485, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
  
@@ -1258,18 +1433,37 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "centzn", global_setup = structure(c("library(learnr)", 
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+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
+"                                      \"Bravo!\",", "                                      \"Super!\",", 
+"                                      \"Bien fait\",", "                                      \"Bien joué\",", 
+"                                      \"Tu l'as fait!\",", "                                      \"Je savais que tu pouvais le faire.\",", 
+"                                      \"Ça a l'air facile!\",", 
+"                                      \"C'était un travail de première classe.\",", 
+"                                      \"C'est ce que j'appelle un bon travail!\"),", 
+"                          encouragement = c(\"Bon effort\",", 
+"                                            \"Vous l'avez presque maîtrisé!\",", 
+"                                            \"Ça avance bien.\",", 
+"                                            \"Continuez comme ça.\",", 
+"                                            \"Continuez à travailler dur!\",", 
+"                                            \"Vous apprenez vite!\",", 
+"                                            \"Vous faites un excellent travail aujourd'hui.\"))", 
+"learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
+"                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
 "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = "ison_brandes <- to_named(ison_brandes)", 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = "ison_brandes <- to_named(ison_brandes)", 
     chunks = list(list(label = "addingnames", code = "ison_brandes <- to_named(ison_brandes)", 
         opts = list(label = "\"addingnames\"", exercise = "TRUE", 
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         code = "", opts = list(label = "\"centzn\"", exercise = "TRUE", 
             exercise.setup = "\"addingnames\"", purl = "FALSE"), 
         engine = "r")), code_check = NULL, error_check = NULL, 
-    check = NULL, solution = structure(c("net_degree(ison_brandes)", 
-    "net_betweenness(ison_brandes)", "net_closeness(ison_brandes)", 
-    "print(net_eigenvector(ison_brandes), digits = 5)"), chunk_opts = list(
+    check = NULL, solution = structure(c("net_by_degree(ison_brandes)", 
+    "net_by_betweenness(ison_brandes)", "net_by_closeness(ison_brandes)", 
+    "print(net_by_eigenvector(ison_brandes), digits = 5)"), chunk_opts = list(
         label = "centzn-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
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@@ -1304,9 +1498,28 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "multiplot", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
+"                                      \"Bravo!\",", "                                      \"Super!\",", 
+"                                      \"Bien fait\",", "                                      \"Bien joué\",", 
+"                                      \"Tu l'as fait!\",", "                                      \"Je savais que tu pouvais le faire.\",", 
+"                                      \"Ça a l'air facile!\",", 
+"                                      \"C'était un travail de première classe.\",", 
+"                                      \"C'est ce que j'appelle un bon travail!\"),", 
+"                          encouragement = c(\"Bon effort\",", 
+"                                            \"Vous l'avez presque maîtrisé!\",", 
+"                                            \"Ça avance bien.\",", 
+"                                            \"Continuez comme ça.\",", 
+"                                            \"Continuez à travailler dur!\",", 
+"                                            \"Vous apprenez vite!\",", 
+"                                            \"Vous faites un excellent travail aujourd'hui.\"))", 
+"learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
+"                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
+"                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
 "ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = "ison_brandes <- to_named(ison_brandes)", 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = "ison_brandes <- to_named(ison_brandes)", 
     chunks = list(list(label = "addingnames", code = "ison_brandes <- to_named(ison_brandes)", 
         opts = list(label = "\"addingnames\"", exercise = "TRUE", 
             purl = "FALSE"), engine = "r"), list(label = "multiplot", 
@@ -1314,18 +1527,18 @@ <h2>Glossary</h2>
             exercise.setup = "\"addingnames\"", purl = "FALSE", 
             fig.width = "9"), engine = "r")), code_check = NULL, 
     error_check = NULL, check = NULL, solution = structure(c("ison_brandes <- ison_brandes %>%", 
-    "  add_node_attribute(\"degree\", node_is_max(node_degree(ison_brandes))) %>%", 
-    "  add_node_attribute(\"betweenness\", node_is_max(node_betweenness(ison_brandes))) %>%", 
-    "  add_node_attribute(\"closeness\", node_is_max(node_closeness(ison_brandes))) %>%", 
-    "  add_node_attribute(\"eigenvector\", node_is_max(node_eigenvector(ison_brandes)))", 
+    "  add_node_attribute(\"degree\", node_is_max(node_by_degree(ison_brandes))) %>%", 
+    "  add_node_attribute(\"betweenness\", node_is_max(node_by_betweenness(ison_brandes))) %>%", 
+    "  add_node_attribute(\"closeness\", node_is_max(node_by_closeness(ison_brandes))) %>%", 
+    "  add_node_attribute(\"eigenvector\", node_is_max(node_by_eigenvector(ison_brandes)))", 
     "gd <- graphr(ison_brandes, node_color = \"degree\") + ", 
-    "  ggtitle(\"Degree\", subtitle = round(net_degree(ison_brandes), 2))", 
+    "  ggtitle(\"Degree\", subtitle = round(net_by_degree(ison_brandes), 2))", 
     "gc <- graphr(ison_brandes, node_color = \"closeness\") + ", 
-    "  ggtitle(\"Closeness\", subtitle = round(net_closeness(ison_brandes), 2))", 
+    "  ggtitle(\"Closeness\", subtitle = round(net_by_closeness(ison_brandes), 2))", 
     "gb <- graphr(ison_brandes, node_color = \"betweenness\") + ", 
-    "  ggtitle(\"Betweenness\", subtitle = round(net_betweenness(ison_brandes), 2))", 
+    "  ggtitle(\"Betweenness\", subtitle = round(net_by_betweenness(ison_brandes), 2))", 
     "ge <- graphr(ison_brandes, node_color = \"eigenvector\") + ", 
-    "  ggtitle(\"Eigenvector\", subtitle = round(net_eigenvector(ison_brandes), 2))", 
+    "  ggtitle(\"Eigenvector\", subtitle = round(net_by_eigenvector(ison_brandes), 2))", 
     "(gd | gb) / (gc | ge)", "# ggsave(\"brandes-centralities.pdf\")"
     ), chunk_opts = list(label = "multiplot-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
@@ -1354,19 +1567,19 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "centzdq", question = structure("How centralized is the ison_brandes network? Select all that apply.", html = TRUE, class = c("html", 
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@@ -1379,7 +1592,7 @@ <h2>Glossary</h2>
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     answer = "centzdq-answer", question = "centzdq"), loading = NULL, 
-    random_answer_order = TRUE, allow_retry = TRUE, seed = 872531293.093696, 
+    random_answer_order = TRUE, allow_retry = TRUE, seed = 994946848.536692, 
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 </script>
@@ -1387,22 +1600,22 @@ <h2>Glossary</h2>
 
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@@ -1414,7 +1627,7 @@ <h2>Glossary</h2>
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@@ -1429,12 +1642,30 @@ <h2>Glossary</h2>
 
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+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "freeplayend", 
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@@ -1461,12 +1692,12 @@ <h2>Glossary</h2>
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From ced8ca6c938dd4b62df7f310803124a95e39ea7a Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Wed, 22 Apr 2026 08:30:17 +0200
Subject: [PATCH 3/6] Updated community tutorial

---
 inst/tutorials/tutorial4/community.Rmd  |  90 ++++-----
 inst/tutorials/tutorial4/community.html | 246 ++++++++++++------------
 2 files changed, 168 insertions(+), 168 deletions(-)

diff --git a/inst/tutorials/tutorial4/community.Rmd b/inst/tutorials/tutorial4/community.Rmd
index 6c30f87..609ce41 100644
--- a/inst/tutorials/tutorial4/community.Rmd
+++ b/inst/tutorials/tutorial4/community.Rmd
@@ -237,7 +237,7 @@ respectively:
 net_ties(tasks)/(net_nodes(tasks)*(net_nodes(tasks)-1))
 ```
 
-but we can also just use the `{manynet}` function for calculating the density,
+but we can also just use the `{netrics}` function for calculating the density,
 which always uses the equation appropriate for the type of network...
 
 ```{r dens, exercise=TRUE, exercise.setup = "separatingnets", purl = FALSE}
@@ -245,10 +245,10 @@ which always uses the equation appropriate for the type of network...
 ```
 
 ```{r dens-solution}
-net_density(tasks)
+net_by_density(tasks)
 ```
 
-Note that the various measures in `{manynet}` print results to three decimal points
+Note that the various measures in `{netrics}` print results to three decimal points
 by default, but the underlying result retains the same recurrence.
 So same result...
 
@@ -286,7 +286,7 @@ First, let's calculate
 `r gloss("reciprocity")`
 in the task network.
 While one could do this by hand,
-it's more efficient to do this using the `{manynet}` package.
+it's more efficient to do this using the `{netrics}` package.
 Can you guess the correct name of the function?
 
 ```{r recip, exercise=TRUE, exercise.setup = "separatingnets", purl = FALSE}
@@ -294,7 +294,7 @@ Can you guess the correct name of the function?
 ```
 
 ```{r recip-solution}
-net_reciprocity(tasks)
+net_by_reciprocity(tasks)
 # this function calculates the amount of reciprocity in the whole network
 ```
 
@@ -305,7 +305,7 @@ reciprocated and not.
 
 ```{r recip-explanation, exercise = TRUE}
 tasks %>% mutate_ties(rec = tie_is_reciprocated(tasks)) %>% graphr(edge_color = "rec")
-net_indegree(tasks)
+net_by_indegree(tasks)
 ```
 
 So we can see that indeed there are very few asymmetric ties,
@@ -325,7 +325,7 @@ Again, can you guess the correct name of this function?
 ```
 
 ```{r trans-solution}
-net_transitivity(tasks)
+net_by_transitivity(tasks)
 # this function calculates the amount of transitivity in the whole network
 ```
 
@@ -460,7 +460,7 @@ interested in the correlation of ties between nodes.
 Let's return to the question of closure.
 First try one of the closure measures we have already treated that gives us
 a sense of shared partners for one-mode networks.
-Then compare this with `net_equivalency()`, which can be used on the original
+Then compare this with `net_by_equivalency()`, which can be used on the original
 two-mode network.
 
 ```{r twomode-cohesion, exercise=TRUE, exercise.setup = "easyway", purl = FALSE}
@@ -468,22 +468,22 @@ two-mode network.
 ```
 
 ```{r twomode-cohesion-hint-1, purl = FALSE}
-# net_transitivity(): Calculate transitivity in a network
+# net_by_transitivity(): Calculate transitivity in a network
 
-net_transitivity(women_graph)
-net_transitivity(event_graph)
+net_by_transitivity(women_graph)
+net_by_transitivity(event_graph)
 ```
 
 ```{r twomode-cohesion-hint-2, purl = FALSE}
-# net_equivalency(): Calculate equivalence or reinforcement in a (usually two-mode) network
+# net_by_equivalency(): Calculate equivalence or reinforcement in a (usually two-mode) network
 
-net_equivalency(ison_southern_women)
+net_by_equivalency(ison_southern_women)
 ```
 
 ```{r twomode-cohesion-solution}
-net_transitivity(women_graph)
-net_transitivity(event_graph)
-net_equivalency(ison_southern_women)
+net_by_transitivity(women_graph)
+net_by_transitivity(event_graph)
+net_by_equivalency(ison_southern_women)
 ```
 
 ```{r equil-interp, echo=FALSE, purl = FALSE}
@@ -514,7 +514,7 @@ Now let's look at the friendship network, 'friends'.
 We're interested here in how many 
 `r gloss("components", "component")`
 there are.
-By default, the `net_components()` function will
+By default, the `net_by_components()` function will
 return the number of _strong_ components for directed networks.
 For _weak_ components, you will need to first make the network 
 `r gloss("undirected")`.
@@ -534,7 +534,7 @@ question("Weak components...",
 ```
 
 ```{r comp-no-hint-1, purl = FALSE}
-net_components(friends)
+net_by_components(friends)
 # note that friends is a directed network
 # you can see this by calling the object 'friends'
 # or by running `is_directed(friends)`
@@ -545,13 +545,13 @@ net_components(friends)
 # Note: to_undirected() returns an object with all tie direction removed, 
 # so any pair of nodes with at least one directed edge 
 # will be connected by an undirected edge in the new network.
-net_components(to_undirected(friends))
+net_by_components(to_undirected(friends))
 ```
 
 ```{r comp-no-solution}
 # note that friends is a directed network
-net_components(friends)
-net_components(to_undirected(friends))
+net_by_components(friends)
+net_by_components(to_undirected(friends))
 ```
 
 ```{r comp-interp, echo = FALSE, purl = FALSE}
@@ -572,7 +572,7 @@ question("How many components are there?",
 
 So we know how many components there are,
 but maybe we're also interested in which nodes are members of which components?
-`node_components()` returns a membership vector
+`node_in_component()` returns a membership vector
 that can be used to color nodes in `graphr()`:
 
 ```{r comp-memb, exercise=TRUE, exercise.setup = "separatingnets", purl = FALSE}
@@ -581,12 +581,12 @@ that can be used to color nodes in `graphr()`:
 
 ```{r comp-memb-hint-1, purl = FALSE}
 friends <- friends %>% 
-  mutate(weak_comp = node_components(to_undirected(friends)),
-         strong_comp = node_components(friends))
-# node_components returns a vector of nodes' memberships to components in the network
+  mutate(weak_comp = node_in_component(to_undirected(friends)),
+         strong_comp = node_in_component(friends))
+# node_in_component returns a vector of nodes' memberships to components in the network
 # here, we are adding the nodes' membership to components as an attribute in the network
 # alternatively, we can also use the function `add_node_attribute()`
-# eg. `add_node_attribute(friends, "weak_comp", node_components(to_undirected(friends)))`
+# eg. `add_node_attribute(friends, "weak_comp", node_in_component(to_undirected(friends)))`
 ```
 
 ```{r comp-memb-hint-2, purl = FALSE}
@@ -598,8 +598,8 @@ graphr(friends, node_color = "strong_comp") + ggtitle("Strong components")
 
 ```{r comp-memb-solution}
 friends <- friends %>% 
-  mutate(weak_comp = node_components(to_undirected(friends)),
-         strong_comp = node_components(friends))
+  mutate(weak_comp = node_in_component(to_undirected(friends)),
+         strong_comp = node_in_component(friends))
 graphr(friends, node_color = "weak_comp") + ggtitle("Weak components") +
 graphr(friends, node_color = "strong_comp") + ggtitle("Strong components")
 ```
@@ -648,8 +648,8 @@ Since there are many isolates, there will be many components,
 even if we look at weak components and not just strong components.
 
 ```{r blogcomp, exercise = TRUE, exercise.setup = "blogsize"}
-net_components(blogs)
-net_components(to_undirected(blogs))
+node_in_component(blogs)
+node_in_component(to_undirected(blogs))
 ```
 
 ### Giant component
@@ -698,7 +698,7 @@ The most common measure of the fit of a community assignment in a network is
 `r gloss("modularity")`.
 
 ```{r blogmod, exercise=TRUE, exercise.setup = "blogtogiant"}
-net_modularity(blogs, membership = node_in_partition(blogs))
+net_by_modularity(blogs, membership = node_in_partition(blogs))
 ```
 
 Remember that modularity ranges between 1 and -1.
@@ -727,7 +727,7 @@ but is otherwise quite flexible.
 
 ```{r blogmodassign, exercise=TRUE, exercise.setup = "blogtogiant", warning=FALSE, fig.width=9}
 graphr(blogs, node_color = "Leaning")
-net_modularity(blogs, membership = node_attribute(blogs, "Leaning"))
+net_by_modularity(blogs, membership = node_attribute(blogs, "Leaning"))
 ```
 
 <img src="https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExcXFwZWF2azV0YTlwdnM0bTZncXpuOTdxdHFyMjJqcXJod2V3b29vYiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/PsBRTPKG71YVq/giphy.gif" alt="gif of Chevy Chase saying plot twist"/>
@@ -764,7 +764,7 @@ There is no one single best community detection algorithm.
 Instead there are several, each with their strengths and weaknesses. 
 Since this is a rather small network, we'll focus on the following methods: 
 walktrap, edge betweenness, and fast greedy.
-(Others are included in `{manynet}`/`{igraph}`)
+(Others are included in `{netrics}`/`{igraph}`)
 As you use them, consider how they portray communities and consider which one(s) 
 afford a sensible view of the social world as cohesively organized.
 
@@ -819,13 +819,13 @@ which(friend_wt == 2)
 
 ```{r walk-hint-3, purl = FALSE}
 # resulting in a modularity of 
-net_modularity(friends, friend_wt)
+net_by_modularity(friends, friend_wt)
 ```
 
 ```{r walk-solution}
 friend_wt <- node_in_walktrap(friends, times=50)
 # results in a modularity of 
-net_modularity(friends, friend_wt)
+net_by_modularity(friends, friend_wt)
 ```
 
 We can also visualise the clusters on the original network
@@ -857,9 +857,9 @@ graphr(friends,
        node_color = "walk_comm",
        node_group = "walk_comm") +
   ggtitle("Walktrap",
-    subtitle = round(net_modularity(friends, friend_wt), 3))
+    subtitle = round(net_by_modularity(friends, friend_wt), 3))
 # the function `round()` rounds the values to a specified number of decimal places
-# here, we are telling it to round the net_modularity score to 3 decimal places,
+# here, we are telling it to round the net_by_modularity score to 3 decimal places,
 # but the score is exactly 0.27 so only two decimal places are printed.
 ```
 
@@ -874,7 +874,7 @@ graphr(friends,
        node_color = "walk_comm",
        node_group = "walk_comm") +
   ggtitle("Walktrap",
-    subtitle = round(net_modularity(friends, friend_wt), 3))
+    subtitle = round(net_by_modularity(friends, friend_wt), 3))
 ```
 
 This can be helpful when polygons overlap to better identify membership
@@ -924,7 +924,7 @@ graphr(friends,
        node_color = "eb_comm",
        node_group = "eb_comm") +
   ggtitle("Edge-betweenness",
-    subtitle = round(net_modularity(friends, friend_eb), 3))
+    subtitle = round(net_by_modularity(friends, friend_eb), 3))
 ```
 
 ```{r ebplot-solution}
@@ -934,7 +934,7 @@ graphr(friends,
        node_color = "eb_comm",
        node_group = "eb_comm") +
   ggtitle("Edge-betweenness",
-    subtitle = round(net_modularity(friends, friend_eb), 3))
+    subtitle = round(net_by_modularity(friends, friend_eb), 3))
 ```
 
 For more on this algorithm, see M Newman and M Girvan: Finding and 
@@ -959,7 +959,7 @@ although I personally find it both useful and in many cases quite "accurate".
 ```{r fg-hint-1, purl = FALSE}
 friend_fg <- node_in_greedy(friends)
 friend_fg # Does this result in a different community partition?
-net_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure
+net_by_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure
 ```
 
 ```{r fg-hint-2, purl = FALSE}
@@ -970,14 +970,14 @@ graphr(friends,
        node_color = "fg_comm",
        node_group = "fg_comm") +
   ggtitle("Fast-greedy",
-    subtitle = round(net_modularity(friends, friend_fg), 3))
+    subtitle = round(net_by_modularity(friends, friend_fg), 3))
 # 
 ```
 
 ```{r fg-solution}
 friend_fg <- node_in_greedy(friends)
 friend_fg # Does this result in a different community partition?
-net_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure
+net_by_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure
 
 # Again, we can visualise these communities in different ways:
 friends <- friends %>% 
@@ -986,7 +986,7 @@ graphr(friends,
        node_color = "fg_comm",
        node_group = "fg_comm") +
   ggtitle("Fast-greedy",
-    subtitle = round(net_modularity(friends, friend_fg), 3))
+    subtitle = round(net_by_modularity(friends, friend_fg), 3))
 ```
 
 See A Clauset, MEJ Newman, C Moore: 
@@ -1005,7 +1005,7 @@ question("What is the difference between communities and components?",
 
 ### Communities
 
-Lastly, `{manynet}` includes a function to run through and find the membership
+Lastly, `{netrics}` includes a function to run through and find the membership
 assignment that maximises `r gloss("modularity")` across any of the applicable community
 detection procedures.
 This is helpfully called `node_in_community()`.
diff --git a/inst/tutorials/tutorial4/community.html b/inst/tutorials/tutorial4/community.html
index fda2a0e..37264f3 100644
--- a/inst/tutorials/tutorial4/community.html
+++ b/inst/tutorials/tutorial4/community.html
@@ -13,7 +13,7 @@
 
 <meta name="progressive" content="false" />
 <meta name="allow-skip" content="false" />
-<meta name="learnr-version-prerender" content="0.11.5" />
+<meta name="learnr-version-prerender" content="0.11.6" />
 
 <title>Cohesion and Community</title>
 
@@ -299,7 +299,7 @@ <h3>Density</h3>
 <pre class="text"><code># calculating network density manually according to equation
 net_ties(tasks)/(net_nodes(tasks)*(net_nodes(tasks)-1))</code></pre>
 </div>
-<p>but we can also just use the <code>{manynet}</code> function for
+<p>but we can also just use the <code>{netrics}</code> function for
 calculating the density, which always uses the equation appropriate for
 the type of network…</p>
 <div class="tutorial-exercise" data-label="dens" data-completion="1"
@@ -310,9 +310,9 @@ <h3>Density</h3>
 <div class="tutorial-exercise-support" data-label="dens-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_density(tasks)</code></pre>
+<pre class="text"><code>net_by_density(tasks)</code></pre>
 </div>
-<p>Note that the various measures in <code>{manynet}</code> print
+<p>Note that the various measures in <code>{netrics}</code> print
 results to three decimal points by default, but the underlying result
 retains the same recurrence. So same result…</p>
 <div class="panel panel-default tutorial-question-container">
@@ -345,7 +345,7 @@ <h3>Reciprocity</h3>
 <p>First, let’s calculate
 <abbr><dfn title='A measure of how often nodes in a directed network are mutually linked.'>
 reciprocity </dfn></abbr> in the task network. While one could do this
-by hand, it’s more efficient to do this using the <code>{manynet}</code>
+by hand, it’s more efficient to do this using the <code>{netrics}</code>
 package. Can you guess the correct name of the function?</p>
 <div class="tutorial-exercise" data-label="recip" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
@@ -355,7 +355,7 @@ <h3>Reciprocity</h3>
 <div class="tutorial-exercise-support" data-label="recip-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_reciprocity(tasks)
+<pre class="text"><code>net_by_reciprocity(tasks)
 # this function calculates the amount of reciprocity in the whole network</code></pre>
 </div>
 <p>Wow, this seems quite high based on what we observed visually! But if
@@ -366,7 +366,7 @@ <h3>Reciprocity</h3>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>tasks %&gt;% mutate_ties(rec = tie_is_reciprocated(tasks)) %&gt;% graphr(edge_color = &quot;rec&quot;)
-net_indegree(tasks)</code></pre>
+net_by_indegree(tasks)</code></pre>
 <script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{&quot;engine&quot;:&quot;R&quot;}\">R Code<\/span>"}</script>
 </div>
 <p>So we can see that indeed there are very few asymmetric ties, and yet
@@ -388,7 +388,7 @@ <h3>Transitivity</h3>
 <div class="tutorial-exercise-support" data-label="trans-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_transitivity(tasks)
+<pre class="text"><code>net_by_transitivity(tasks)
 # this function calculates the amount of transitivity in the whole network</code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
@@ -521,7 +521,7 @@ <h3>Closure in two-mode networks</h3>
 <p>Let’s return to the question of closure. First try one of the closure
 measures we have already treated that gives us a sense of shared
 partners for one-mode networks. Then compare this with
-<code>net_equivalency()</code>, which can be used on the original
+<code>net_by_equivalency()</code>, which can be used on the original
 two-mode network.</p>
 <div class="tutorial-exercise" data-label="twomode-cohesion"
 data-completion="1" data-diagnostics="1" data-startover="1"
@@ -532,26 +532,26 @@ <h3>Closure in two-mode networks</h3>
 data-label="twomode-cohesion-hint-1" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
-<pre class="text"><code># net_transitivity(): Calculate transitivity in a network
+<pre class="text"><code># net_by_transitivity(): Calculate transitivity in a network
 
-net_transitivity(women_graph)
-net_transitivity(event_graph)</code></pre>
+net_by_transitivity(women_graph)
+net_by_transitivity(event_graph)</code></pre>
 </div>
 <div class="tutorial-exercise-support"
 data-label="twomode-cohesion-hint-2" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
-<pre class="text"><code># net_equivalency(): Calculate equivalence or reinforcement in a (usually two-mode) network
+<pre class="text"><code># net_by_equivalency(): Calculate equivalence or reinforcement in a (usually two-mode) network
 
-net_equivalency(ison_southern_women)</code></pre>
+net_by_equivalency(ison_southern_women)</code></pre>
 </div>
 <div class="tutorial-exercise-support"
 data-label="twomode-cohesion-solution" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
-<pre class="text"><code>net_transitivity(women_graph)
-net_transitivity(event_graph)
-net_equivalency(ison_southern_women)</code></pre>
+<pre class="text"><code>net_by_transitivity(women_graph)
+net_by_transitivity(event_graph)
+net_by_equivalency(ison_southern_women)</code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
 <div data-label="equil-interp" class="tutorial-question panel-body">
@@ -573,7 +573,7 @@ <h2>Components</h2>
 here in how many
 <abbr><dfn title='A component is a connected subgraph not part of a larger connected subgraph.'>
 components </dfn></abbr> there are. By default, the
-<code>net_components()</code> function will return the number of
+<code>net_by_components()</code> function will return the number of
 <em>strong</em> components for directed networks. For <em>weak</em>
 components, you will need to first make the network
 <abbr><dfn title='An undirected or line network is one in which tie direction is undefined.'>
@@ -595,7 +595,7 @@ <h2>Components</h2>
 <div class="tutorial-exercise-support" data-label="comp-no-hint-1"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_components(friends)
+<pre class="text"><code>net_by_components(friends)
 # note that friends is a directed network
 # you can see this by calling the object &#39;friends&#39;
 # or by running `is_directed(friends)`</code></pre>
@@ -607,14 +607,14 @@ <h2>Components</h2>
 # Note: to_undirected() returns an object with all tie direction removed, 
 # so any pair of nodes with at least one directed edge 
 # will be connected by an undirected edge in the new network.
-net_components(to_undirected(friends))</code></pre>
+net_by_components(to_undirected(friends))</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="comp-no-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code># note that friends is a directed network
-net_components(friends)
-net_components(to_undirected(friends))</code></pre>
+net_by_components(friends)
+net_by_components(to_undirected(friends))</code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
 <div data-label="comp-interp" class="tutorial-question panel-body">
@@ -626,7 +626,7 @@ <h2>Components</h2>
 </div>
 <p>So we know how many components there are, but maybe we’re also
 interested in which nodes are members of which components?
-<code>node_components()</code> returns a membership vector that can be
+<code>node_in_component()</code> returns a membership vector that can be
 used to color nodes in <code>graphr()</code>:</p>
 <div class="tutorial-exercise" data-label="comp-memb"
 data-completion="1" data-diagnostics="1" data-startover="1"
@@ -637,12 +637,12 @@ <h2>Components</h2>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>friends &lt;- friends %&gt;% 
-  mutate(weak_comp = node_components(to_undirected(friends)),
-         strong_comp = node_components(friends))
-# node_components returns a vector of nodes&#39; memberships to components in the network
+  mutate(weak_comp = node_in_component(to_undirected(friends)),
+         strong_comp = node_in_component(friends))
+# node_in_component returns a vector of nodes&#39; memberships to components in the network
 # here, we are adding the nodes&#39; membership to components as an attribute in the network
 # alternatively, we can also use the function `add_node_attribute()`
-# eg. `add_node_attribute(friends, &quot;weak_comp&quot;, node_components(to_undirected(friends)))`</code></pre>
+# eg. `add_node_attribute(friends, &quot;weak_comp&quot;, node_in_component(to_undirected(friends)))`</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="comp-memb-hint-2"
 data-completion="1" data-diagnostics="1" data-startover="1"
@@ -656,8 +656,8 @@ <h2>Components</h2>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>friends &lt;- friends %&gt;% 
-  mutate(weak_comp = node_components(to_undirected(friends)),
-         strong_comp = node_components(friends))
+  mutate(weak_comp = node_in_component(to_undirected(friends)),
+         strong_comp = node_in_component(friends))
 graphr(friends, node_color = &quot;weak_comp&quot;) + ggtitle(&quot;Weak components&quot;) +
 graphr(friends, node_color = &quot;strong_comp&quot;) + ggtitle(&quot;Strong components&quot;)</code></pre>
 </div>
@@ -703,8 +703,8 @@ <h2>Factions</h2>
 <div class="tutorial-exercise" data-label="blogcomp" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
-<pre class="text"><code>net_components(blogs)
-net_components(to_undirected(blogs))</code></pre>
+<pre class="text"><code>node_in_component(blogs)
+node_in_component(to_undirected(blogs))</code></pre>
 <script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{&quot;engine&quot;:&quot;R&quot;}\">R Code<\/span>"}</script>
 </div>
 <div id="section-giant-component" class="section level3">
@@ -760,7 +760,7 @@ <h2>Modularity</h2>
 <div class="tutorial-exercise" data-label="blogmod" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
-<pre class="text"><code>net_modularity(blogs, membership = node_in_partition(blogs))</code></pre>
+<pre class="text"><code>net_by_modularity(blogs, membership = node_in_partition(blogs))</code></pre>
 <script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{&quot;engine&quot;:&quot;R&quot;}\">R Code<\/span>"}</script>
 </div>
 <p>Remember that modularity ranges between 1 and -1. How can we
@@ -783,7 +783,7 @@ <h2>Modularity</h2>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>graphr(blogs, node_color = &quot;Leaning&quot;)
-net_modularity(blogs, membership = node_attribute(blogs, &quot;Leaning&quot;))</code></pre>
+net_by_modularity(blogs, membership = node_attribute(blogs, &quot;Leaning&quot;))</code></pre>
 <script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{&quot;engine&quot;:&quot;R&quot;}\">R Code<\/span>"}</script>
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 <p><img src="https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExcXFwZWF2azV0YTlwdnM0bTZncXpuOTdxdHFyMjJqcXJod2V3b29vYiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/PsBRTPKG71YVq/giphy.gif" alt="gif of Chevy Chase saying plot twist"/></p>
@@ -818,7 +818,7 @@ <h2>Communities</h2>
 there are several, each with their strengths and weaknesses. Since this
 is a rather small network, we’ll focus on the following methods:
 walktrap, edge betweenness, and fast greedy. (Others are included in
-<code>{manynet}</code>/<code>{igraph}</code>) As you use them, consider
+<code>{netrics}</code>/<code>{igraph}</code>) As you use them, consider
 how they portray communities and consider which one(s) afford a sensible
 view of the social world as cohesively organized.</p>
 <div id="section-walktrap" class="section level3">
@@ -877,14 +877,14 @@ <h3>Walktrap</h3>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code># resulting in a modularity of 
-net_modularity(friends, friend_wt)</code></pre>
+net_by_modularity(friends, friend_wt)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="walk-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>friend_wt &lt;- node_in_walktrap(friends, times=50)
 # results in a modularity of 
-net_modularity(friends, friend_wt)</code></pre>
+net_by_modularity(friends, friend_wt)</code></pre>
 </div>
 <p>We can also visualise the clusters on the original network How does
 the following look? Plausible?</p>
@@ -920,9 +920,9 @@ <h3>Walktrap</h3>
        node_color = &quot;walk_comm&quot;,
        node_group = &quot;walk_comm&quot;) +
   ggtitle(&quot;Walktrap&quot;,
-    subtitle = round(net_modularity(friends, friend_wt), 3))
+    subtitle = round(net_by_modularity(friends, friend_wt), 3))
 # the function `round()` rounds the values to a specified number of decimal places
-# here, we are telling it to round the net_modularity score to 3 decimal places,
+# here, we are telling it to round the net_by_modularity score to 3 decimal places,
 # but the score is exactly 0.27 so only two decimal places are printed.</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="walkplot-solution"
@@ -938,7 +938,7 @@ <h3>Walktrap</h3>
        node_color = &quot;walk_comm&quot;,
        node_group = &quot;walk_comm&quot;) +
   ggtitle(&quot;Walktrap&quot;,
-    subtitle = round(net_modularity(friends, friend_wt), 3))</code></pre>
+    subtitle = round(net_by_modularity(friends, friend_wt), 3))</code></pre>
 </div>
 <p>This can be helpful when polygons overlap to better identify
 membership Or you can use node color and size to indicate other
@@ -996,7 +996,7 @@ <h3>Edge Betweenness</h3>
        node_color = &quot;eb_comm&quot;,
        node_group = &quot;eb_comm&quot;) +
   ggtitle(&quot;Edge-betweenness&quot;,
-    subtitle = round(net_modularity(friends, friend_eb), 3))</code></pre>
+    subtitle = round(net_by_modularity(friends, friend_eb), 3))</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="ebplot-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
@@ -1007,7 +1007,7 @@ <h3>Edge Betweenness</h3>
        node_color = &quot;eb_comm&quot;,
        node_group = &quot;eb_comm&quot;) +
   ggtitle(&quot;Edge-betweenness&quot;,
-    subtitle = round(net_modularity(friends, friend_eb), 3))</code></pre>
+    subtitle = round(net_by_modularity(friends, friend_eb), 3))</code></pre>
 </div>
 <p>For more on this algorithm, see M Newman and M Girvan: Finding and
 evaluating community structure in networks, Physical Review E 69, 026113
@@ -1037,7 +1037,7 @@ <h3>Fast Greedy</h3>
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>friend_fg &lt;- node_in_greedy(friends)
 friend_fg # Does this result in a different community partition?
-net_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure</code></pre>
+net_by_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="fg-hint-2"
 data-completion="1" data-diagnostics="1" data-startover="1"
@@ -1049,7 +1049,7 @@ <h3>Fast Greedy</h3>
        node_color = &quot;fg_comm&quot;,
        node_group = &quot;fg_comm&quot;) +
   ggtitle(&quot;Fast-greedy&quot;,
-    subtitle = round(net_modularity(friends, friend_fg), 3))
+    subtitle = round(net_by_modularity(friends, friend_fg), 3))
 # </code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="fg-solution"
@@ -1057,7 +1057,7 @@ <h3>Fast Greedy</h3>
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>friend_fg &lt;- node_in_greedy(friends)
 friend_fg # Does this result in a different community partition?
-net_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure
+net_by_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure
 
 # Again, we can visualise these communities in different ways:
 friends &lt;- friends %&gt;% 
@@ -1066,7 +1066,7 @@ <h3>Fast Greedy</h3>
        node_color = &quot;fg_comm&quot;,
        node_group = &quot;fg_comm&quot;) +
   ggtitle(&quot;Fast-greedy&quot;,
-    subtitle = round(net_modularity(friends, friend_fg), 3))</code></pre>
+    subtitle = round(net_by_modularity(friends, friend_fg), 3))</code></pre>
 </div>
 <p>See A Clauset, MEJ Newman, C Moore: Finding community structure in
 very large networks, <a href="https://arxiv.org/abs/cond-mat/0408187"
@@ -1082,7 +1082,7 @@ <h3>Fast Greedy</h3>
 </div>
 <div id="section-communities-1" class="section level3">
 <h3>Communities</h3>
-<p>Lastly, <code>{manynet}</code> includes a function to run through and
+<p>Lastly, <code>{netrics}</code> includes a function to run through and
 find the membership assignment that maximises
 <abbr><dfn title='The modularity of a membership assignment is the difference of the cross-cluster ties from the expected value.'>
 modularity </dfn></abbr> across any of the applicable community
@@ -1461,15 +1461,15 @@ <h2>Glossary</h2>
  
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-    error_check = NULL, check = NULL, solution = structure("net_density(tasks)", chunk_opts = list(
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@@ -1679,7 +1679,7 @@ <h2>Glossary</h2>
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             purl = "FALSE"), engine = "r")), code_check = NULL, 
-    error_check = NULL, check = NULL, solution = structure(c("net_reciprocity(tasks)", 
+    error_check = NULL, check = NULL, solution = structure(c("net_by_reciprocity(tasks)", 
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     ), chunk_opts = list(label = "recip-solution")), tests = NULL, 
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@@ -1738,7 +1738,7 @@ <h2>Glossary</h2>
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     setup = NULL, chunks = list(list(label = "recip-explanation", 
-        code = "tasks %>% mutate_ties(rec = tie_is_reciprocated(tasks)) %>% graphr(edge_color = \"rec\")\nnet_indegree(tasks)", 
+        code = "tasks %>% mutate_ties(rec = tie_is_reciprocated(tasks)) %>% graphr(edge_color = \"rec\")\nnet_by_indegree(tasks)", 
         opts = list(label = "\"recip-explanation\"", exercise = "TRUE"), 
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@@ -1759,7 +1759,7 @@ <h2>Glossary</h2>
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         max.print = 1000, label = "recip-explanation", exercise = TRUE, 
         code = c("tasks %>% mutate_ties(rec = tie_is_reciprocated(tasks)) %>% graphr(edge_color = \"rec\")", 
-        "net_indegree(tasks)"), out.width.px = 624, out.height.px = 384, 
+        "net_by_indegree(tasks)"), out.width.px = 624, out.height.px = 384, 
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@@ -1806,7 +1806,7 @@ <h2>Glossary</h2>
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             purl = "FALSE"), engine = "r")), code_check = NULL, 
-    error_check = NULL, check = NULL, solution = structure(c("net_transitivity(tasks)", 
+    error_check = NULL, check = NULL, solution = structure(c("net_by_transitivity(tasks)", 
     "# this function calculates the amount of transitivity in the whole network"
     ), chunk_opts = list(label = "trans-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
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@@ -2195,8 +2195,8 @@ <h2>Glossary</h2>
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-    check = NULL, solution = structure(c("net_transitivity(women_graph)", 
-    "net_transitivity(event_graph)", "net_equivalency(ison_southern_women)"
+    check = NULL, solution = structure(c("net_by_transitivity(women_graph)", 
+    "net_by_transitivity(event_graph)", "net_by_equivalency(ison_southern_women)"
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         value = "don't care about tie direction when establishing components.", 
         label = structure("don’t care about tie direction when establishing components.", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_d34cd1d", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_17aefc4", 
         option = "care about tie direction when establishing components.", 
         value = "care about tie direction when establishing components.", 
         label = structure("care about tie direction when establishing components.", html = TRUE, class = c("html", 
@@ -2285,7 +2285,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "weak-strong-answer", question = "weak-strong"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 799369996.627764, options = list()), class = c("learnr_radio", 
+    seed = 2036660529.55161, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
 
@@ -2330,7 +2330,7 @@ <h2>Glossary</h2>
             exercise = "TRUE", exercise.setup = "\"separatingnets\"", 
             purl = "FALSE"), engine = "r")), code_check = NULL, 
     error_check = NULL, check = NULL, solution = structure(c("# note that friends is a directed network", 
-    "net_components(friends)", "net_components(to_undirected(friends))"
+    "net_by_components(friends)", "net_by_components(to_undirected(friends))"
     ), chunk_opts = list(label = "comp-no-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -2360,19 +2360,19 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "comp-interp", 
     question = structure("How many components are there?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_8ab7561", 
+    "character")), answers = list(structure(list(id = "lnr_ans_e472170", 
         option = "2", value = "2", label = structure("2", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = structure("There are more than 2 components.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_ba29274", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_db50cda", 
         option = "3", value = "3", label = structure("3", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("There are 3 <em>weak<\u002fem> components.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_edd8bca", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_cb835e5", 
         option = "4", value = "4", label = structure("4", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("There are 4 <em>strong<\u002fem> components. <img src=\"https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExMDB5bzhjcGo3NHozZm5jdHcyMWk5emhnajNyZnFxc3Z2cmJnbGN0ZyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/YpS0oFFC2VJ84/giphy.gif\" alt=\"gif of two thumbs up\"/>", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_4e7aed7", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_4a39e93", 
         option = "5", value = "5", label = structure("5", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = structure("There are fewer than 5 components.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
@@ -2384,7 +2384,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "comp-interp-answer", question = "comp-interp"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 1971380286.082, options = list()), class = c("learnr_checkbox", 
+    seed = 1707354439.20495, options = list()), class = c("learnr_checkbox", 
 "tutorial_question")), session = session)
 </script>
  
@@ -2429,8 +2429,8 @@ <h2>Glossary</h2>
             exercise = "TRUE", exercise.setup = "\"separatingnets\"", 
             purl = "FALSE"), engine = "r")), code_check = NULL, 
     error_check = NULL, check = NULL, solution = structure(c("friends <- friends %>% ", 
-    "  mutate(weak_comp = node_components(to_undirected(friends)),", 
-    "         strong_comp = node_components(friends))", "graphr(friends, node_color = \"weak_comp\") + ggtitle(\"Weak components\") +", 
+    "  mutate(weak_comp = node_in_component(to_undirected(friends)),", 
+    "         strong_comp = node_in_component(friends))", "graphr(friends, node_color = \"weak_comp\") + ggtitle(\"Weak components\") +", 
     "graphr(friends, node_color = \"strong_comp\") + ggtitle(\"Strong components\")"
     ), chunk_opts = list(label = "comp-memb-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
@@ -2460,20 +2460,20 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "node-comp-interp", 
     question = structure("Why is there a difference between the weak and strong components results?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_4045d58", 
+    "character")), answers = list(structure(list(id = "lnr_ans_bd4034f", 
         option = "Because one node has only incoming ties.", 
         value = "Because one node has only incoming ties.", label = structure("Because one node has only incoming ties.", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_c17942c", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_444680e", 
         option = "Because three nodes cannot reach any other nodes.", 
         value = "Because three nodes cannot reach any other nodes.", 
         label = structure("Because three nodes cannot reach any other nodes.", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_9b57986", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_1188bde", 
         option = "Because there is an extra isolate.", value = "Because there is an extra isolate.", 
         label = structure("Because there is an extra isolate.", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_a40bbbd", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_a42f839", 
         option = "Because the tie strength matters.", value = "Because the tie strength matters.", 
         label = structure("Because the tie strength matters.", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
@@ -2485,7 +2485,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "node-comp-interp-answer", question = "node-comp-interp"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 1103875890.98597, options = list()), class = c("learnr_checkbox", 
+    seed = 1521752805.29138, options = list()), class = c("learnr_checkbox", 
 "tutorial_question")), session = session)
 </script>
 
@@ -2649,7 +2649,7 @@ <h2>Glossary</h2>
     setup = "# This is a large network\nnet_nodes(irps_blogs)\n# Let's concentrate on just a sample of 240 \n# (by deleting a random sample of 1250)\nblogs <- delete_nodes(irps_blogs, sample(1:1490, 1250))\ngraphr(blogs)", 
     chunks = list(list(label = "blogsize", code = "# This is a large network\nnet_nodes(irps_blogs)\n# Let's concentrate on just a sample of 240 \n# (by deleting a random sample of 1250)\nblogs <- delete_nodes(irps_blogs, sample(1:1490, 1250))\ngraphr(blogs)", 
         opts = list(label = "\"blogsize\"", exercise = "TRUE"), 
-        engine = "r"), list(label = "blogcomp", code = "net_components(blogs)\nnet_components(to_undirected(blogs))", 
+        engine = "r"), list(label = "blogcomp", code = "node_in_component(blogs)\nnode_in_component(to_undirected(blogs))", 
         opts = list(label = "\"blogcomp\"", exercise = "TRUE", 
             exercise.setup = "\"blogsize\""), engine = "r")), 
     code_check = NULL, error_check = NULL, check = NULL, solution = NULL, 
@@ -2670,8 +2670,8 @@ <h2>Glossary</h2>
         message = TRUE, render = NULL, ref.label = NULL, child = NULL, 
         engine = "r", split = FALSE, include = TRUE, purl = TRUE, 
         max.print = 1000, label = "blogcomp", exercise = TRUE, 
-        exercise.setup = "blogsize", code = c("net_components(blogs)", 
-        "net_components(to_undirected(blogs))"), out.width.px = 624, 
+        exercise.setup = "blogsize", code = c("node_in_component(blogs)", 
+        "node_in_component(to_undirected(blogs))"), out.width.px = 624, 
         out.height.px = 384, params.src = "blogcomp, exercise = TRUE, exercise.setup = \"blogsize\"", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
     engine = "r", version = "4"), class = c("r", "tutorial_exercise"
@@ -2848,7 +2848,7 @@ <h2>Glossary</h2>
         engine = "r"), list(label = "blogtogiant", code = "blogs <- blogs %>% to_giant()\nsum(node_is_isolate(blogs))\ngraphr(blogs)", 
         opts = list(label = "\"blogtogiant\"", exercise = "TRUE", 
             warning = "FALSE", fig.width = "9", exercise.setup = "\"blogsize\""), 
-        engine = "r"), list(label = "blogmod", code = "net_modularity(blogs, membership = node_in_partition(blogs))", 
+        engine = "r"), list(label = "blogmod", code = "net_by_modularity(blogs, membership = node_in_partition(blogs))", 
         opts = list(label = "\"blogmod\"", exercise = "TRUE", 
             exercise.setup = "\"blogtogiant\""), engine = "r")), 
     code_check = NULL, error_check = NULL, check = NULL, solution = NULL, 
@@ -2869,7 +2869,7 @@ <h2>Glossary</h2>
         message = TRUE, render = NULL, ref.label = NULL, child = NULL, 
         engine = "r", split = FALSE, include = TRUE, purl = TRUE, 
         max.print = 1000, label = "blogmod", exercise = TRUE, 
-        exercise.setup = "blogtogiant", code = "net_modularity(blogs, membership = node_in_partition(blogs))", 
+        exercise.setup = "blogtogiant", code = "net_by_modularity(blogs, membership = node_in_partition(blogs))", 
         out.width.px = 624, out.height.px = 384, params.src = "blogmod, exercise=TRUE, exercise.setup = \"blogtogiant\"", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
     engine = "r", version = "4"), class = c("r", "tutorial_exercise"
@@ -2879,22 +2879,22 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "blogQ", question = structure("Which statements about modularity are true?", html = TRUE, class = c("html", 
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+"character")), answers = list(structure(list(id = "lnr_ans_c78dc5a", 
     option = "A community assignment with modularity > 0 means there are more ties within communities than between communities.", 
     value = "A community assignment with modularity > 0 means there are more ties within communities than between communities.", 
     label = structure("A community assignment with modularity &gt; 0 means there are more ties within communities than between communities.", html = TRUE, class = c("html", 
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     value = "In a community assignment with modularity = 1, every community must have an internal density of 1 and an external density of 0.", 
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     value = "A community assignment with modularity = -1 is impossible.", 
     label = structure("A community assignment with modularity = -1 is impossible.", html = TRUE, class = c("html", 
@@ -2906,7 +2906,7 @@ <h2>Glossary</h2>
 "character")), incorrect = structure("Incorrect", html = TRUE, class = c("html", 
 "character")), message = NULL, post_message = NULL), ids = list(
     answer = "blogQ-answer", question = "blogQ"), loading = NULL, 
-    random_answer_order = TRUE, allow_retry = TRUE, seed = 2078411267.03216, 
+    random_answer_order = TRUE, allow_retry = TRUE, seed = 2130932843.00771, 
     options = list()), class = c("learnr_checkbox", "tutorial_question"
 )), session = session)
 </script>
@@ -2949,7 +2949,7 @@ <h2>Glossary</h2>
         engine = "r"), list(label = "blogtogiant", code = "blogs <- blogs %>% to_giant()\nsum(node_is_isolate(blogs))\ngraphr(blogs)", 
         opts = list(label = "\"blogtogiant\"", exercise = "TRUE", 
             warning = "FALSE", fig.width = "9", exercise.setup = "\"blogsize\""), 
-        engine = "r"), list(label = "blogmodassign", code = "graphr(blogs, node_color = \"Leaning\")\nnet_modularity(blogs, membership = node_attribute(blogs, \"Leaning\"))", 
+        engine = "r"), list(label = "blogmodassign", code = "graphr(blogs, node_color = \"Leaning\")\nnet_by_modularity(blogs, membership = node_attribute(blogs, \"Leaning\"))", 
         opts = list(label = "\"blogmodassign\"", exercise = "TRUE", 
             exercise.setup = "\"blogtogiant\"", warning = "FALSE", 
             fig.width = "9"), engine = "r")), code_check = NULL, 
@@ -2972,7 +2972,7 @@ <h2>Glossary</h2>
         engine = "r", split = FALSE, include = TRUE, purl = TRUE, 
         max.print = 1000, label = "blogmodassign", exercise = TRUE, 
         exercise.setup = "blogtogiant", code = c("graphr(blogs, node_color = \"Leaning\")", 
-        "net_modularity(blogs, membership = node_attribute(blogs, \"Leaning\"))"
+        "net_by_modularity(blogs, membership = node_attribute(blogs, \"Leaning\"))"
         ), out.width.px = 864, out.height.px = 384, params.src = "blogmodassign, exercise=TRUE, exercise.setup = \"blogtogiant\", warning=FALSE, fig.width=9", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
     engine = "r", version = "4"), class = c("r", "tutorial_exercise"
@@ -3087,7 +3087,7 @@ <h2>Glossary</h2>
             opts = list(label = "\"walk\"", exercise = "TRUE", 
                 exercise.setup = "\"separatingnets\""), engine = "r")), 
     code_check = NULL, error_check = NULL, check = NULL, solution = structure(c("friend_wt <- node_in_walktrap(friends, times=50)", 
-    "# results in a modularity of ", "net_modularity(friends, friend_wt)"
+    "# results in a modularity of ", "net_by_modularity(friends, friend_wt)"
     ), chunk_opts = list(label = "walk-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -3163,7 +3163,7 @@ <h2>Glossary</h2>
     "graphr(friends, node_group = \"walk_comm\")", "# or both!", 
     "graphr(friends,", "       node_color = \"walk_comm\",", 
     "       node_group = \"walk_comm\") +", "  ggtitle(\"Walktrap\",", 
-    "    subtitle = round(net_modularity(friends, friend_wt), 3))"
+    "    subtitle = round(net_by_modularity(friends, friend_wt), 3))"
     ), chunk_opts = list(label = "walkplot-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -3302,7 +3302,7 @@ <h2>Glossary</h2>
     check = NULL, solution = structure(c("friends <- friends %>% ", 
     "  mutate(eb_comm = friend_eb)", "graphr(friends,", "       node_color = \"eb_comm\",", 
     "       node_group = \"eb_comm\") +", "  ggtitle(\"Edge-betweenness\",", 
-    "    subtitle = round(net_modularity(friends, friend_eb), 3))"
+    "    subtitle = round(net_by_modularity(friends, friend_eb), 3))"
     ), chunk_opts = list(label = "ebplot-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -3370,11 +3370,11 @@ <h2>Glossary</h2>
             purl = "FALSE"), engine = "r")), code_check = NULL, 
     error_check = NULL, check = NULL, solution = structure(c("friend_fg <- node_in_greedy(friends)", 
     "friend_fg # Does this result in a different community partition?", 
-    "net_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure", 
+    "net_by_modularity(friends, friend_fg) # Compare this to the edge betweenness procedure", 
     "", "# Again, we can visualise these communities in different ways:", 
     "friends <- friends %>% ", "  mutate(fg_comm = friend_fg)", 
     "graphr(friends,", "       node_color = \"fg_comm\",", "       node_group = \"fg_comm\") +", 
-    "  ggtitle(\"Fast-greedy\",", "    subtitle = round(net_modularity(friends, friend_fg), 3))"
+    "  ggtitle(\"Fast-greedy\",", "    subtitle = round(net_by_modularity(friends, friend_fg), 3))"
     ), chunk_opts = list(label = "fg-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -3401,16 +3401,16 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "comm-comp", question = structure("What is the difference between communities and components?", html = TRUE, class = c("html", 
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     option = "Communities and components are just different terms for the same thing", 
     value = "Communities and components are just different terms for the same thing", 
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     label = structure("Components are about paths whereas communities are about the relationship between within-group and between-group ties", html = TRUE, class = c("html", 
@@ -3422,7 +3422,7 @@ <h2>Glossary</h2>
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 "character")), message = NULL, post_message = NULL), ids = list(
     answer = "comm-comp-answer", question = "comm-comp"), loading = NULL, 
-    random_answer_order = TRUE, allow_retry = TRUE, seed = 930697472.56661, 
+    random_answer_order = TRUE, allow_retry = TRUE, seed = 872252850.593826, 
     options = list()), class = c("learnr_radio", "tutorial_question"
 )), session = session)
 </script>
@@ -3495,38 +3495,38 @@ <h2>Glossary</h2>
 
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(La progression sera remise à zéro)\",\"youmustcomplete\":\"Vous devez d'abord compléter\",\"exercise\":\"l'exercice\",\"exercise_plural\":\"des exercices\",\"inthissection\":\"de cette section avec de continuer.\",\"code\":\"Code\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"and\":\"et\",\"or\":\"ou\",\"oxfordcomma\":\"\"}}},\"es\":{\"translation\":{\"button\":{\"runcode\":\"Ejecutar código\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Pista\",\"hint_plural\":\"Pistas\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Siguiente pista\",\"hintprev\":\"Pista anterior\",\"solution\":\"Solución\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Copiar al portapapeles\",\"startover\":\"Reiniciar\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Continuar\",\"submitanswer\":\"Enviar respuesta\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Tema anterior\",\"nexttopic\":\"Tema siguiente\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Volver a intentar\"},\"text\":{\"startover\":\"Reiniciar\",\"areyousure\":\"¿De verdad quieres empezar de nuevo? (todo el progreso del ejercicio se perderá)\",\"youmustcomplete\":\"Debes completar\",\"exercise\":\"el ejercicio\",\"exercise_plural\":\"los ejercicios\",\"inthissection\":\"en esta sección antes de continuar.\",\"code\":\"Código\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Cuestionario\",\"and\":\"y\",\"or\":\"o\",\"oxfordcomma\":\"\"}}},\"pt\":{\"translation\":{\"button\":{\"runcode\":\"Executar código\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Dica\",\"hint_plural\":\"Dicas\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Próxima dica\",\"hintprev\":\"Dica anterior\",\"solution\":\"Solução\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Copiar para a área de transferência\",\"startover\":\"Reiniciar\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Continuar\",\"submitanswer\":\"Enviar resposta\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Tópico anterior\",\"nexttopic\":\"Próximo tópico\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Tentar novamente\"},\"text\":{\"startover\":\"Reiniciar\",\"areyousure\":\"Tem certeza que deseja começar novamente? (todo o progresso feito será perdido)\",\"youmustcomplete\":\"Você deve completar\",\"exercise\":\"o exercício\",\"exercise_plural\":\"os exercícios\",\"inthissection\":\"nesta seção antes de continuar.\",\"code\":\"Código\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"and\":\"e\",\"or\":\"ou\",\"oxfordcomma\":\"\"}}},\"tr\":{\"translation\":{\"button\":{\"runcode\":\"Çalıştırma Kodu\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Ipucu\",\"hint_plural\":\"İpuçları\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Sonraki İpucu\",\"hintprev\":\"Önceki İpucu\",\"solution\":\"Çözüm\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Pano'ya Kopyala\",\"startover\":\"Baştan Başlamak\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Devam et\",\"submitanswer\":\"Cevabı onayla\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Önceki Konu\",\"nexttopic\":\"Sonraki Konu\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Tekrar Deneyin\"},\"text\":{\"startover\":\"Baştan Başlamak\",\"areyousure\":\"Baştan başlamak istediğinizden emin misiniz? (tüm egzersiz ilerlemesi kaybolacak)\",\"youmustcomplete\":\"Tamamlamalısın\",\"exercise\":\"egzersiz\",\"exercise_plural\":\"egzersizler\",\"inthissection\":\"devam etmeden önce bu bölümde\",\"code\":\"Kod\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Sınav\",\"oxfordcomma\":\"\"}}},\"emo\":{\"translation\":{\"button\":{\"runcode\":\"🏃\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"💡\",\"hint_plural\":\"$t(button.hint)\",\"hinttitle\":\"$t(button.hint)\",\"solution\":\"🎯\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"📋\",\"startover\":\"⏮\",\"startovertitle\":\"Start Over\",\"continue\":\"✅\",\"submitanswer\":\"🆗\",\"submitanswertitle\":\"Submit Answer\",\"previoustopic\":\"⬅\",\"nexttopic\":\"➡\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"🔁\"},\"text\":{\"startover\":\"⏮\",\"areyousure\":\"🤔\",\"youmustcomplete\":\"⚠️ 👉 🧑‍💻\",\"exercise\":\"\",\"exercise_plural\":\"\",\"inthissection\":\"\",\"code\":\"💻\",\"enginecap\":\"$t(text.code) {{engine}}\",\"oxfordcomma\":\"\"}}},\"eu\":{\"translation\":{\"button\":{\"runcode\":\"Kodea egikaritu\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Laguntza\",\"hint_plural\":\"Laguntza\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Aurreko laguntza\",\"hintprev\":\"Hurrengo laguntza\",\"solution\":\"Ebazpena\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Arbelean kopiatu\",\"startover\":\"Berrabiarazi\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Jarraitu\",\"submitanswer\":\"Erantzuna bidali\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Aurreko atala\",\"nexttopic\":\"Hurrengo atala\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Berriro saiatu\"},\"text\":{\"startover\":\"Berrabiarazi\",\"areyousure\":\"Berriro hasi nahi duzu? (egindako lana galdu egingo da)\",\"youmustcomplete\":\"Aurrera egin baino lehen atal honetako\",\"exercise\":\"ariketa egin behar duzu.\",\"exercise_plural\":\"ariketak egin behar dituzu.\",\"inthissection\":\"\",\"code\":\"Kodea\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Galdetegia\",\"oxfordcomma\":\"\"}}},\"de\":{\"translation\":{\"button\":{\"runcode\":\"Code ausführen\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Tipp\",\"hint_plural\":\"Tipps\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Nächster Tipp\",\"hintprev\":\"Vorheriger Tipp\",\"solution\":\"Lösung\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"In die Zwischenablage kopieren\",\"startover\":\"Neustart\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Weiter\",\"submitanswer\":\"Antwort einreichen\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Vorheriges Kapitel\",\"nexttopic\":\"Nächstes Kapitel\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Nochmal versuchen\"},\"text\":{\"startover\":\"Neustart\",\"areyousure\":\"Bist du sicher, dass du neustarten willst? (der gesamte Lernfortschritt wird gelöscht)\",\"youmustcomplete\":\"Vervollstädinge\",\"exercise\":\"die Übung\",\"exercise_plural\":\"die Übungen\",\"inthissection\":\"in diesem Kapitel, bevor du fortfährst.\",\"code\":\"Code\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"blank\":\"Lücke\",\"blank_plural\":\"Lücken\",\"pleasereplaceblank\":\"Bitte ersetze {{blank}} mit gültigem Code.\",\"unparsable\":\"Dies scheint kein gültiger R Code zu sein. R kann deinen Text nicht in einen gültigen Befehl übersetzen. Du hast vielleicht vergessen, die Lücke zu füllen, einen Unterstrich zu entfernen, ein Komma zwischen Argumente zu setzen oder ein eröffnendes <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> oder <code>{<\\/code> mit einem zugehörigen <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> oder <code>}<\\/code> zu schließen.\\n\",\"and\":\"und\",\"or\":\"oder\",\"listcomma\":\", \",\"oxfordcomma\":\",\"}}},\"ko\":{\"translation\":{\"button\":{\"runcode\":\"코드 실행\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"힌트\",\"hint_plural\":\"힌트들\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"다음 힌트\",\"hintprev\":\"이전 힌트\",\"solution\":\"솔루션\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"클립보드에 복사\",\"startover\":\"재학습\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"다음 학습으로\",\"submitanswer\":\"정답 제출\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"이전 토픽\",\"nexttopic\":\"다음 토픽\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"재시도\"},\"text\":{\"startover\":\"재학습\",\"areyousure\":\"다시 시작 하시겠습니까? (모든 예제의 진행 정보가 재설정됩니다)\",\"youmustcomplete\":\"당신은 완료해야 합니다\",\"exercise\":\"연습문제\",\"exercise_plural\":\"연습문제들\",\"inthissection\":\"이 섹션을 실행하기 전에\",\"code\":\"코드\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"퀴즈\",\"blank\":\"공백\",\"blank_plural\":\"공백들\",\"exercisecontainsblank\":\"이 연습문제에는 {{count}}개의 $t(text.blank)이 포함되어 있습니다.\",\"pleasereplaceblank\":\"{{blank}}를 유효한 코드로 바꾸십시오.\",\"unparsable\":\"이것은 유효한 R 코드가 아닐 수 있습니다. R은 텍스트를 완전한 명령으로 변환하는 방법을 결정할 수 없습니다. 당신은 공백이나 밑줄을 대체하여 채우기, 인수를 컴마로 구분하기, 또는 <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> , <code>{<\\/code>로 시작하는 구문을 닫는 <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code>, <code>}<\\/code>을 잊었을 수도 있습니다.\\n\",\"and\":\"그리고\",\"or\":\"혹은\",\"listcomma\":\", \",\"oxfordcomma\":\"\"}}},\"zh\":{\"translation\":{\"button\":{\"runcode\":\"运行代码\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"提示\",\"hint_plural\":\"提示\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"下一个提示\",\"hintprev\":\"上一个提示\",\"solution\":\"答案\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"复制到剪切板\",\"startover\":\"重新开始\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"继续\",\"submitanswer\":\"提交答案\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"上一专题\",\"nexttopic\":\"下一专题\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"再试一次\"},\"text\":{\"startover\":\"重置\",\"areyousure\":\"你确定要重新开始吗? (所有当前进度将被重置)\",\"youmustcomplete\":\"你必须完成\",\"exercise\":\"练习\",\"exercise_plural\":\"练习\",\"inthissection\":\"在进行本节之前\",\"code\":\"代码\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"测试\",\"blank\":\"空\",\"blank_plural\":\"空\",\"exercisecontainsblank\":\"本练习包含{{count}}个$t(text.blank)\",\"pleasereplaceblank\":\"请在{{blank}}内填写恰当的代码\",\"unparsable\":\"这似乎不是有效的R代码。 R不知道如何将您的文本转换为完整的命令。 您是否忘了填空，忘了删除下划线，忘了在参数之间包含逗号，或者是忘了用<code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code>,<code>}<\\/code>来封闭<code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code>。 or <code>{<\\/code>。\\n\",\"unparsablequotes\":\"<p>您的R代码中似乎含有特殊格式的引号，或者弯引号(<code>{{character}}<\\/code>) 在字符串前后，在R中字符串应该被直引号(<code>&quot;<\\/code> 或者 <code>'<\\/code>)包裹。<\\/p> {{code}} <p>别担心，该错误经常在复制粘贴包含格式的代码时遇到， 您可以尝试将该行中的代码替换为以下代码，也许还有其他地方需要修改。<\\/p> {{suggestion}}\\n\",\"unparsableunicode\":\"<p>您的代码中似乎包含有异常字符(<code>{{character}}<\\/code>),导致代码无效。<\\/p> {{code}} <p>有时候你的代码可能含有看似正常字符的特殊字符，特别是当你复制粘贴其他来源代码的时候。 请试着删除这些特殊字符,重新输入<\\/p>\\n\",\"unparsableunicodesuggestion\":\"<p>您的代码中似乎包含有异常字符(<code>{{character}}<\\/code>),导致代码无效。<\\/p> {{code}} <p>有时候你的代码可能含有看似正常字符的特殊字符，特别是当你复制粘贴其他来源代码的时候。 请试着删除这些特殊字符,重新输入<\\/p>\\n\",\"and\":\"且\",\"or\":\"或\",\"listcomma\":\",\",\"oxfordcomma\":\",\"}}},\"pl\":{\"translation\":{\"button\":{\"runcode\":\"Uruchom kod\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Podpowiedź\",\"hint_plural\":\"Podpowiedzi\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Następna podpowiedź\",\"hintprev\":\"Poprzednia podpowiedź\",\"solution\":\"Rozwiązanie\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Kopiuj do schowka\",\"startover\":\"Zacznij od początku\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Kontynuuj\",\"submitanswer\":\"Wyślij\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Poprzednia sekcja\",\"nexttopic\":\"Następna sekcja\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Spróbuj ponownie\"},\"text\":{\"startover\":\"Zacznij od początku\",\"areyousure\":\"Czy na pewno chcesz zacząć od początku? (cały postęp w zadaniu zostanie utracony)\",\"youmustcomplete\":\"Musisz ukończyć\",\"exercise\":\"ćwiczenie\",\"exercise_plural\":\"ćwiczenia\",\"inthissection\":\"w tej sekcji przed kontynuowaniem\",\"code\":\"Kod\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"blank\":\"luka\",\"blank_plural\":\"luk(i)\",\"exercisecontainsblank\":\"To ćwiczenie zawiera {{count}} $t(text.blank).\",\"pleasereplaceblank\":\"Proszę uzupełnić {{blank}} prawidłowym kodem.\",\"unparsable\":\"Wygląda na to, że może to nie być prawidłowy kod R. R nie jest w stanie przetworzyć Twojego tekstu na polecenie. Mogłeś(-aś) zapomnieć wypełnić luki, usunąć podkreślnik, umieścić przecinka między argumentami, lub zamknąć znak <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> lub <code>{<\\/code> odpowiadającym <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> lub <code>}<\\/code>.\\n\",\"unparsablequotes\":\"<p>Wygląda na to, że Twój kod zawiera szczególnie sformatowane cudzysłowy lub cudzysłowy typograficzne (<code>{{character}}<\\/code>) przy ciągach znaków, co sprawia, że kod jest niepoprawny. R wymaga cudzysłowów prostych (<code>&quot;<\\/code> albo <code>'<\\/code>).<\\/p> {{code}} <p>Nie martw się, to powszechne źródło błędów, gdy kopiuje się kod z innego programu, który sam formatuje teskt. Możesz spróbować zastąpić swój kod następującym kodem. Mogą być też inne miejsca, które wymagają poprawienia.<\\/p> {{suggestion}}\\n\",\"unparsableunicode\":\"<p>Wygląda na to, że Twój kod zawiera niespodziewany znak specjalny (<code>{{character}}<\\/code>), co sprawia, że kod jest niepoprawny.<\\/p> {{code}} <p>Czasami Twój kod może zawierać znak specjalny, który wygląda jak zwykły znak, zwłaszcza jeśli kopiujesz kod z innego programu. Spróbuj usunąć znak specjalny i wpisać do ponownie ręcznie.<\\/p>\\n\",\"unparsableunicodesuggestion\":\"<p>Wygląda na to, że Twój kod zawiera niespodziewany znak specjalny (<code>{{character}}<\\/code>), co sprawia, że kod jest niepoprawny.<\\/p> {{code}} <p>Czasami Twój kod może zawierać znak specjalny, który wygląda jak zwykły znak, zwłaszcza jeśli kopiujesz kod z innego programu. Możesz spróbować zastąpić swój kod następującym kodem. Mogą być też inne miejsca, które wymagają poprawienia.<\\/p> {{suggestion}}\\n\",\"and\":\"i\",\"or\":\"lub\",\"listcomma\":\", \",\"oxfordcomma\":\"\"}}},\"no\":{\"translation\":{\"button\":{\"runcode\":\"Kjør kode\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Hint\",\"hint_plural\":\"Hint\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Neste hint\",\"hintprev\":\"Forrige hint\",\"solution\":\"Løsning\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Kopier til utklippstavle\",\"startover\":\"Start på nytt\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Fortsett\",\"submitanswer\":\"Send inn svar\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Forrige tema\",\"nexttopic\":\"Neste tema\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Prøv igjen\"},\"text\":{\"startover\":\"Start på nytt\",\"areyousure\":\"Er du sikker på at du vil starte på nytt? (All fremgang vil bli slettet)\",\"youmustcomplete\":\"Du må fullføre\",\"exercise\":\"oppgave\",\"exercise_plural\":\"oppgaver\",\"inthissection\":\"i denne seksjonen før du fortsetter.\",\"code\":\"kode\",\"enginecap\":\"{{engine}}-$t(text.code)\",\"quiz\":\"Quiz\",\"blank\":\"tomrom\",\"blank_plural\":\"tomrom\",\"exercisecontainsblank\":\"Denne oppgaven inneholder {{count}} $t(text.blank).\",\"pleasereplaceblank\":\"Erstatt {{blank}} med gyldig kode.\",\"unparsable\":\"Dette ser ikke ut til å være gyldig R-kode. Slik R ser det, utgjør ikke teksten en fullstendig kommando. Du kan ha glemt å fylle ut en luke, å fjerne en understrek, å inkludere et komma mellom argumenter, eller å lukke et åpnende <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> eller <code>{<\\/code> med et tilsvarende <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> eller <code>}<\\/code>.\\n\",\"unparsablequotes\":\"<p>Det ser ut som om R-koden din inneholder spesielt formaterte anførselstegn eller &quot;krøllete&quot; anførselstegn (<code>{{character}}<\\/code>) rundt tekststrenger, noe som gjør koden din ugyldig. R krever at tekstverdier skal være inneholdt i rette anførselstegn (<code>&quot;<\\/code> eller <code>'<\\/code>).<\\/p> {{code}} <p>Dette er et vanlig problem når du kopierer kode fra et annet program som bruker sin egen formatering på tekst.  Du kan prøve å erstatte koden i den linjen med følgende. 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From a5d0bf0a55195c9884f2725a75cedce5bf895965 Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Wed, 22 Apr 2026 08:30:24 +0200
Subject: [PATCH 4/6] Updated position tutorial

---
 inst/tutorials/tutorial5/position.Rmd  |  68 +++++-----
 inst/tutorials/tutorial5/position.html | 168 ++++++++++++-------------
 2 files changed, 118 insertions(+), 118 deletions(-)

diff --git a/inst/tutorials/tutorial5/position.Rmd b/inst/tutorials/tutorial5/position.Rmd
index 06e21d0..2d967fd 100644
--- a/inst/tutorials/tutorial5/position.Rmd
+++ b/inst/tutorials/tutorial5/position.Rmd
@@ -55,7 +55,7 @@ tasks <- to_uniplex(ison_algebra, "tasks")
 
 <img src="https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExMTVtNTJqZmFsdmFpOGRjd24zeHZ1NTM0bDhxbWxrMWdja202NXo1eSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/fL1EXX8pQs0dd9zqLR/giphy.gif" alt="gif of rick and morty characters hanging out with themselves"/>
 
-For this session, we're going to use the "ison_algebra" dataset included in the `{manynet}` package.
+For this session, we're going to use the "ison_algebra" dataset included in the `{netrics}` package.
 Do you remember how to call the data?
 Can you find out some more information about it via its help file?
 
@@ -204,7 +204,7 @@ there are no bridges.
 
 ```{r bridges, exercise = TRUE, exercise.setup = "objects-setup"}
 sum(tie_is_bridge(friends))
-any(node_bridges(friends)>0)
+any(node_by_bridges(friends)>0)
 ```
 
 ### Constraint
@@ -212,7 +212,7 @@ any(node_bridges(friends)>0)
 But some nodes do seem more deeply embedded in the network than others.
 Let's take a look at which actors are least `r gloss("constrained", "constraint")`
 by their position in the *task* network.
-`{manynet}` makes this easy enough with the `node_constraint()` function.
+`{netrics}` makes this easy enough with the `node_by_constraint()` function.
 
 ```{r objects-setup, purl=FALSE}
 alge <- to_named(ison_algebra)
@@ -226,12 +226,12 @@ tasks <- to_uniplex(alge, "tasks")
 ```
 
 ```{r constraint-hint, purl = FALSE}
-node_constraint(____)
+node_by_constraint(____)
 # Don't forget we want to look at which actors are least constrained by their position in the 'tasks' network
 ```
 
 ```{r constraint-solution}
-node_constraint(tasks)
+node_by_constraint(tasks)
 ```
 
 This function returns a vector of constraint scores that can range between 0 and 1.
@@ -244,8 +244,8 @@ We can also identify the node with the minimum constraint score using `node_is_m
 
 ```{r constraintplot-hint-1, purl = FALSE}
 tasks <- tasks %>% 
-  mutate(constraint = node_constraint(____),
-         low_constraint = node_is_min(node_constraint(____)))
+  mutate(constraint = node_by_constraint(____),
+         low_constraint = node_is_min(node_by_constraint(____)))
 
 # Don't forget, we are still looking at the 'tasks' network
 ```
@@ -264,8 +264,8 @@ graphr(tasks, node_size = "constraint", node_color = "low_constraint")
 
 ```{r constraintplot-solution}
 tasks <- tasks %>% 
-  mutate(constraint = node_constraint(tasks), 
-         low_constraint = node_is_min(node_constraint(tasks)))
+  mutate(constraint = node_by_constraint(tasks), 
+         low_constraint = node_is_min(node_by_constraint(tasks)))
 graphr(tasks, node_size = "constraint", node_color = "low_constraint")
 ```
 
@@ -318,7 +318,7 @@ a uniplex subgraph thereof.
 
 ### Finding structurally equivalent classes
 
-In `{manynet}`, finding how the nodes of a network can be partitioned
+In `{netrics}`, finding how the nodes of a network can be partitioned
 into structurally equivalent classes can be as easy as:
 
 ```{r find-se, exercise = TRUE, exercise.setup = "data"}
@@ -337,8 +337,8 @@ how these classes are identified and how to interpret them.
 ### Step one: starting with a census
 
 All equivalence classes are based on nodes' similarity across some profile of motifs.
-In `{manynet}`, we call these motif *censuses*.
-Any kind of census can be used, and `{manynet}` includes a few options,
+In `{netrics}`, we call these motif *censuses*.
+Any kind of census can be used, and `{netrics}` includes a few options,
 but `node_in_structural()` is based off of the census of all the nodes' ties,
 both outgoing and incoming ties, to characterise their relationships to tie partners.
 
@@ -347,24 +347,24 @@ both outgoing and incoming ties, to characterise their relationships to tie part
 ```
 
 ```{r construct-cor-hint-1, purl = FALSE}
-# Let's use the node_by_tie() function
+# Let's use the node_x_tie() function
 # The function accepts an object such as a dataset
 # Hint: Which dataset are we using in this tutorial?
-node_by_tie(____)
+node_x_tie(____)
 ```
 
 ```{r construct-cor-hint-2, purl = FALSE}
-node_by_tie(ison_algebra)
+node_x_tie(ison_algebra)
 ```
 
 ```{r construct-cor-hint-3, purl = FALSE}
 # Now, let's get the dimensions of an object via the dim() function
-dim(node_by_tie(ison_algebra))
+dim(node_x_tie(ison_algebra))
 ```
 
 ```{r construct-cor-solution}
-node_by_tie(ison_algebra)
-dim(node_by_tie(ison_algebra))
+node_x_tie(ison_algebra)
+dim(node_x_tie(ison_algebra))
 ```
 
 We can see that the result is a matrix of 16 rows and 96 columns, 
@@ -379,7 +379,7 @@ what would you do if you wanted it to be binary?
 
 ```{r construct-binary-hint, purl = FALSE}
 # we could convert the result using as.matrix, returning the ties 
-as.matrix((node_by_tie(ison_algebra)>0)+0)
+as.matrix((node_x_tie(ison_algebra)>0)+0)
 
 ```
 
@@ -388,17 +388,17 @@ as.matrix((node_by_tie(ison_algebra)>0)+0)
 # Note that this also reduces the total number of possible paths between nodes
 ison_algebra %>%
   select_ties(-type) %>%
-  node_by_tie()
+  node_x_tie()
 ```
 
-Note that `node_by_tie()` does not need to be passed to `node_in_structural()` ---
+Note that `node_x_tie()` does not need to be passed to `node_in_structural()` ---
 this is done automatically!
 However, the more generic `node_in_equivalence()` is available and can be used 
-with whichever census (`node_by_*()` output) is desired.
-Feel free to explore using some of the other censuses available in `{manynet}`,
+with whichever census (`node_x_*()` output) is desired.
+Feel free to explore using some of the other censuses available in `{netrics}`,
 though some common ones are already used in the other equivalence convenience functions,
-e.g. `node_by_triad()` in `node_in_regular()` 
-and `node_by_path()` in `node_in_automorphic()`.
+e.g. `node_x_triad()` in `node_in_regular()` 
+and `node_x_path()` in `node_in_automorphic()`.
 
 ### Step two: growing a tree of similarity
 
@@ -413,7 +413,7 @@ so that help page should be consulted for more details.
 By default `"euclidean"` is used.
 
 Second, we can also set the type of clustering algorithm employed.
-By default, `{manynet}`'s equivalence functions use `r gloss("hierarchical clustering","hierclust")`, `"hier"`,
+By default, `{netrics}`'s equivalence functions use `r gloss("hierarchical clustering","hierclust")`, `"hier"`,
 but for compatibility and enthusiasts, we also offer `"concor"`,
 which implements a CONCOR (CONvergence of CORrelations) algorithm.
 
@@ -438,7 +438,7 @@ question("Do you see any differences?",
         allow_retry = TRUE)
 ```
 
-So plotting a `membership` vector from `{manynet}` returns a dendrogram
+So plotting a `membership` vector from `{netrics}` returns a dendrogram
 with the names of the nodes on the _y_-axis and the distance between them on the _x_-axis.
 Using the census as material, the distances between the nodes
 is used to create a dendrogram of (dis)similarity among the nodes.
@@ -469,7 +469,7 @@ But where does this red line come from?
 Or, more technically, how do we identify the number of clusters
 into which to assign nodes?
 
-`{manynet}` includes several different ways of establishing `k`,
+`{netrics}` includes several different ways of establishing `k`,
 or the number of clusters.
 Remember, the further to the right the red line is 
 (the lower on the tree the cut point is)
@@ -507,7 +507,7 @@ then we might expect there to be a relatively rapid increase
 in correlation as we move from, for example, 3 clusters to 4 clusters,
 but a relatively small increase from, for example, 13 clusters to 14 clusters.
 By identifying the inflection point in this line graph,
-`{manynet}` selects a number of clusters that represents a trade-off
+`{netrics}` selects a number of clusters that represents a trade-off
 between fit and parsimony.
 This is the `k = "elbow"` method.
 
@@ -535,12 +535,12 @@ Either is probably fine here,
 and there is much debate around how to select the number of clusters anyway.
 However, the silhouette method seems to do a better job of identifying 
 how unique the 16th node is.
-The silhouette method is also the default in `{manynet}`.
+The silhouette method is also the default in `{netrics}`.
 
 Note that there is a somewhat hidden parameter here, `range`.
 Since testing across all possible numbers of clusters can get 
 computationally expensive (not to mention uninterpretable) for large networks,
-`{manynet}` only considers up to 8 clusters by default.
+`{netrics}` only considers up to 8 clusters by default.
 This however can be modified to be higher or lower, e.g. `range = 16`.
 
 Finally, one last option is `k = "strict"`,
@@ -597,14 +597,14 @@ but this can be tweaked by assigning some other summary statistic as `FUN = `.
 ```
 
 ```{r summ-hint, purl = FALSE}
-# Let's wrap node_by_tie inside the summary() function
+# Let's wrap node_x_tie inside the summary() function
 # and pass it a membership result
-summary(node_by_tie(____),
+summary(node_x_tie(____),
         membership = ____)
 ```
 
 ```{r summ-solution}
-summary(node_by_tie(alge),
+summary(node_x_tie(alge),
         membership = node_in_structural(alge))
 ```
 
diff --git a/inst/tutorials/tutorial5/position.html b/inst/tutorials/tutorial5/position.html
index 54a6232..8426eef 100644
--- a/inst/tutorials/tutorial5/position.html
+++ b/inst/tutorials/tutorial5/position.html
@@ -13,7 +13,7 @@
 
 <meta name="progressive" content="false" />
 <meta name="allow-skip" content="false" />
-<meta name="learnr-version-prerender" content="0.11.5" />
+<meta name="learnr-version-prerender" content="0.11.6" />
 
 <title>Position and Equivalence</title>
 
@@ -114,7 +114,7 @@
 <h2>Setting up</h2>
 <p><img src="https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExMTVtNTJqZmFsdmFpOGRjd24zeHZ1NTM0bDhxbWxrMWdja202NXo1eSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/fL1EXX8pQs0dd9zqLR/giphy.gif" alt="gif of rick and morty characters hanging out with themselves"/></p>
 <p>For this session, we’re going to use the “ison_algebra” dataset
-included in the <code>{manynet}</code> package. Do you remember how to
+included in the <code>{netrics}</code> package. Do you remember how to
 call the data? Can you find out some more information about it via its
 help file?</p>
 <div class="tutorial-exercise" data-label="data" data-completion="1"
@@ -244,7 +244,7 @@ <h3>Bridges</h3>
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
 <pre class="text"><code>sum(tie_is_bridge(friends))
-any(node_bridges(friends)&gt;0)</code></pre>
+any(node_by_bridges(friends)&gt;0)</code></pre>
 <script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{&quot;engine&quot;:&quot;R&quot;}\">R Code<\/span>"}</script>
 </div>
 </div>
@@ -254,8 +254,8 @@ <h3>Constraint</h3>
 others. Let’s take a look at which actors are least
 <abbr><dfn title='The constraint of a node is a measure of how much its connections are also connected to one another.'>
 constrained </dfn></abbr> by their position in the <em>task</em>
-network. <code>{manynet}</code> makes this easy enough with the
-<code>node_constraint()</code> function.</p>
+network. <code>{netrics}</code> makes this easy enough with the
+<code>node_by_constraint()</code> function.</p>
 <div class="tutorial-exercise" data-label="constraint"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
@@ -264,13 +264,13 @@ <h3>Constraint</h3>
 <div class="tutorial-exercise-support" data-label="constraint-hint"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>node_constraint(____)
+<pre class="text"><code>node_by_constraint(____)
 # Don&#39;t forget we want to look at which actors are least constrained by their position in the &#39;tasks&#39; network</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="constraint-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>node_constraint(tasks)</code></pre>
+<pre class="text"><code>node_by_constraint(tasks)</code></pre>
 </div>
 <p>This function returns a vector of constraint scores that can range
 between 0 and 1. Let’s graph the network again, sizing the nodes
@@ -286,8 +286,8 @@ <h3>Constraint</h3>
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
 <pre class="text"><code>tasks &lt;- tasks %&gt;% 
-  mutate(constraint = node_constraint(____),
-         low_constraint = node_is_min(node_constraint(____)))
+  mutate(constraint = node_by_constraint(____),
+         low_constraint = node_is_min(node_by_constraint(____)))
 
 # Don&#39;t forget, we are still looking at the &#39;tasks&#39; network</code></pre>
 </div>
@@ -312,8 +312,8 @@ <h3>Constraint</h3>
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
 <pre class="text"><code>tasks &lt;- tasks %&gt;% 
-  mutate(constraint = node_constraint(tasks), 
-         low_constraint = node_is_min(node_constraint(tasks)))
+  mutate(constraint = node_by_constraint(tasks), 
+         low_constraint = node_is_min(node_by_constraint(tasks)))
 graphr(tasks, node_size = &quot;constraint&quot;, node_color = &quot;low_constraint&quot;)</code></pre>
 </div>
 <p>Why minimum? Because constraint measures how well connected each
@@ -355,7 +355,7 @@ <h2>Structural Equivalence</h2>
 <div id="section-finding-structurally-equivalent-classes"
 class="section level3">
 <h3>Finding structurally equivalent classes</h3>
-<p>In <code>{manynet}</code>, finding how the nodes of a network can be
+<p>In <code>{netrics}</code>, finding how the nodes of a network can be
 partitioned into structurally equivalent classes can be as easy as:</p>
 <div class="tutorial-exercise" data-label="find-se" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
@@ -376,9 +376,9 @@ <h3>Finding structurally equivalent classes</h3>
 class="section level3">
 <h3>Step one: starting with a census</h3>
 <p>All equivalence classes are based on nodes’ similarity across some
-profile of motifs. In <code>{manynet}</code>, we call these motif
+profile of motifs. In <code>{netrics}</code>, we call these motif
 <em>censuses</em>. Any kind of census can be used, and
-<code>{manynet}</code> includes a few options, but
+<code>{netrics}</code> includes a few options, but
 <code>node_in_structural()</code> is based off of the census of all the
 nodes’ ties, both outgoing and incoming ties, to characterise their
 relationships to tie partners.</p>
@@ -390,28 +390,28 @@ <h3>Step one: starting with a census</h3>
 <div class="tutorial-exercise-support" data-label="construct-cor-hint-1"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code># Let&#39;s use the node_by_tie() function
+<pre class="text"><code># Let&#39;s use the node_x_tie() function
 # The function accepts an object such as a dataset
 # Hint: Which dataset are we using in this tutorial?
-node_by_tie(____)</code></pre>
+node_x_tie(____)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="construct-cor-hint-2"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>node_by_tie(ison_algebra)</code></pre>
+<pre class="text"><code>node_x_tie(ison_algebra)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="construct-cor-hint-3"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code># Now, let&#39;s get the dimensions of an object via the dim() function
-dim(node_by_tie(ison_algebra))</code></pre>
+dim(node_x_tie(ison_algebra))</code></pre>
 </div>
 <div class="tutorial-exercise-support"
 data-label="construct-cor-solution" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
-<pre class="text"><code>node_by_tie(ison_algebra)
-dim(node_by_tie(ison_algebra))</code></pre>
+<pre class="text"><code>node_x_tie(ison_algebra)
+dim(node_x_tie(ison_algebra))</code></pre>
 </div>
 <p>We can see that the result is a matrix of 16 rows and 96 columns,
 because we want to catalogue or take a census of all the different
@@ -428,7 +428,7 @@ <h3>Step one: starting with a census</h3>
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
 <pre class="text"><code># we could convert the result using as.matrix, returning the ties 
-as.matrix((node_by_tie(ison_algebra)&gt;0)+0)</code></pre>
+as.matrix((node_x_tie(ison_algebra)&gt;0)+0)</code></pre>
 </div>
 <div class="tutorial-exercise-support"
 data-label="construct-binary-solution" data-completion="1"
@@ -438,17 +438,17 @@ <h3>Step one: starting with a census</h3>
 # Note that this also reduces the total number of possible paths between nodes
 ison_algebra %&gt;%
   select_ties(-type) %&gt;%
-  node_by_tie()</code></pre>
+  node_x_tie()</code></pre>
 </div>
-<p>Note that <code>node_by_tie()</code> does not need to be passed to
+<p>Note that <code>node_x_tie()</code> does not need to be passed to
 <code>node_in_structural()</code> — this is done automatically! However,
 the more generic <code>node_in_equivalence()</code> is available and can
-be used with whichever census (<code>node_by_*()</code> output) is
+be used with whichever census (<code>node_x_*()</code> output) is
 desired. Feel free to explore using some of the other censuses available
-in <code>{manynet}</code>, though some common ones are already used in
+in <code>{netrics}</code>, though some common ones are already used in
 the other equivalence convenience functions,
-e.g. <code>node_by_triad()</code> in <code>node_in_regular()</code> and
-<code>node_by_path()</code> in <code>node_in_automorphic()</code>.</p>
+e.g. <code>node_x_triad()</code> in <code>node_in_regular()</code> and
+<code>node_x_path()</code> in <code>node_in_automorphic()</code>.</p>
 </div>
 <div id="section-step-two-growing-a-tree-of-similarity"
 class="section level3">
@@ -464,7 +464,7 @@ <h3>Step two: growing a tree of similarity</h3>
 help page should be consulted for more details. By default
 <code>"euclidean"</code> is used.</p>
 <p>Second, we can also set the type of clustering algorithm employed. By
-default, <code>{manynet}</code>’s equivalence functions use
+default, <code>{netrics}</code>’s equivalence functions use
 <abbr><dfn title='Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters.'>
 hierarchical clustering </dfn></abbr>, <code>"hier"</code>, but for
 compatibility and enthusiasts, we also offer <code>"concor"</code>,
@@ -494,7 +494,7 @@ <h3>Step two: growing a tree of similarity</h3>
 </div>
 </div>
 <p>So plotting a <code>membership</code> vector from
-<code>{manynet}</code> returns a dendrogram with the names of the nodes
+<code>{netrics}</code> returns a dendrogram with the names of the nodes
 on the <em>y</em>-axis and the distance between them on the
 <em>x</em>-axis. Using the census as material, the distances between the
 nodes is used to create a dendrogram of (dis)similarity among the nodes.
@@ -522,7 +522,7 @@ <h3>Step three: identifying the number of clusters</h3>
 to the branches (clusters) present at that cut-point.</p>
 <p>But where does this red line come from? Or, more technically, how do
 we identify the number of clusters into which to assign nodes?</p>
-<p><code>{manynet}</code> includes several different ways of
+<p><code>{netrics}</code> includes several different ways of
 establishing <code>k</code>, or the number of clusters. Remember, the
 further to the right the red line is (the lower on the tree the cut
 point is) the more dissimilar we’re allowing nodes in the same cluster
@@ -556,7 +556,7 @@ <h3>Step three: identifying the number of clusters</h3>
 be a relatively rapid increase in correlation as we move from, for
 example, 3 clusters to 4 clusters, but a relatively small increase from,
 for example, 13 clusters to 14 clusters. By identifying the inflection
-point in this line graph, <code>{manynet}</code> selects a number of
+point in this line graph, <code>{netrics}</code> selects a number of
 clusters that represents a trade-off between fit and parsimony. This is
 the <code>k = "elbow"</code> method.</p>
 <p>The other option is to evaluate a candidate for <code>k</code> based
@@ -582,11 +582,11 @@ <h3>Step three: identifying the number of clusters</h3>
 around how to select the number of clusters anyway. However, the
 silhouette method seems to do a better job of identifying how unique the
 16th node is. The silhouette method is also the default in
-<code>{manynet}</code>.</p>
+<code>{netrics}</code>.</p>
 <p>Note that there is a somewhat hidden parameter here,
 <code>range</code>. Since testing across all possible numbers of
 clusters can get computationally expensive (not to mention
-uninterpretable) for large networks, <code>{manynet}</code> only
+uninterpretable) for large networks, <code>{netrics}</code> only
 considers up to 8 clusters by default. This however can be modified to
 be higher or lower, e.g. <code>range = 16</code>.</p>
 <p>Finally, one last option is <code>k = "strict"</code>, which only
@@ -645,15 +645,15 @@ <h3>Summarising profiles</h3>
 <div class="tutorial-exercise-support" data-label="summ-hint"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code># Let&#39;s wrap node_by_tie inside the summary() function
+<pre class="text"><code># Let&#39;s wrap node_x_tie inside the summary() function
 # and pass it a membership result
-summary(node_by_tie(____),
+summary(node_x_tie(____),
         membership = ____)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="summ-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>summary(node_by_tie(alge),
+<pre class="text"><code>summary(node_x_tie(alge),
         membership = node_in_structural(alge))</code></pre>
 </div>
 <p>This node census produces 96 columns, <span class="math inline">\(16
@@ -1029,22 +1029,22 @@ <h2>Glossary</h2>
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@@ -1094,31 +1094,31 @@ <h2>Glossary</h2>
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 </script>
  
 <script type="application/shiny-prerendered" data-context="server">
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 </script>
 
@@ -1168,7 +1168,7 @@ <h2>Glossary</h2>
     setup = "alge <- to_named(ison_algebra)\nfriends <- to_uniplex(alge, \"friends\")\nsocial <- to_uniplex(alge, \"social\")\ntasks <- to_uniplex(alge, \"tasks\")", 
     chunks = list(list(label = "objects-setup", code = "alge <- to_named(ison_algebra)\nfriends <- to_uniplex(alge, \"friends\")\nsocial <- to_uniplex(alge, \"social\")\ntasks <- to_uniplex(alge, \"tasks\")", 
         opts = list(label = "\"objects-setup\"", purl = "FALSE"), 
-        engine = "r"), list(label = "bridges", code = "sum(tie_is_bridge(friends))\nany(node_bridges(friends)>0)", 
+        engine = "r"), list(label = "bridges", code = "sum(tie_is_bridge(friends))\nany(node_by_bridges(friends)>0)", 
         opts = list(label = "\"bridges\"", exercise = "TRUE", 
             exercise.setup = "\"objects-setup\""), engine = "r")), 
     code_check = NULL, error_check = NULL, check = NULL, solution = NULL, 
@@ -1190,7 +1190,7 @@ <h2>Glossary</h2>
         engine = "r", split = FALSE, include = TRUE, purl = TRUE, 
         max.print = 1000, label = "bridges", exercise = TRUE, 
         exercise.setup = "objects-setup", code = c("sum(tie_is_bridge(friends))", 
-        "any(node_bridges(friends)>0)"), out.width.px = 624, 
+        "any(node_by_bridges(friends)>0)"), out.width.px = 624, 
         out.height.px = 384, params.src = "bridges, exercise = TRUE, exercise.setup = \"objects-setup\"", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
     engine = "r", version = "4"), class = c("r", "tutorial_exercise"
@@ -1236,7 +1236,7 @@ <h2>Glossary</h2>
         opts = list(label = "\"constraint\"", exercise = "TRUE", 
             exercise.setup = "\"objects-setup\"", purl = "FALSE"), 
         engine = "r")), code_check = NULL, error_check = NULL, 
-    check = NULL, solution = structure("node_constraint(tasks)", chunk_opts = list(
+    check = NULL, solution = structure("node_by_constraint(tasks)", chunk_opts = list(
         label = "constraint-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
         tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA, 
@@ -1301,7 +1301,7 @@ <h2>Glossary</h2>
             exercise.setup = "\"objects-setup\"", purl = "FALSE"), 
         engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("tasks <- tasks %>% ", 
-    "  mutate(constraint = node_constraint(tasks), ", "         low_constraint = node_is_min(node_constraint(tasks)))", 
+    "  mutate(constraint = node_by_constraint(tasks), ", "         low_constraint = node_is_min(node_by_constraint(tasks)))", 
     "graphr(tasks, node_size = \"constraint\", node_color = \"low_constraint\")"
     ), chunk_opts = list(label = "constraintplot-solution")), 
     tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup", 
@@ -1332,11 +1332,11 @@ <h2>Glossary</h2>
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@@ -1376,7 +1376,7 @@ <h2>Glossary</h2>
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@@ -1482,8 +1482,8 @@ <h2>Glossary</h2>
         engine = "r"), list(label = "construct-cor", code = "", 
         opts = list(label = "\"construct-cor\"", exercise = "TRUE", 
             exercise.setup = "\"data\"", purl = "FALSE"), engine = "r")), 
-    code_check = NULL, error_check = NULL, check = NULL, solution = structure(c("node_by_tie(ison_algebra)", 
-    "dim(node_by_tie(ison_algebra))"), chunk_opts = list(label = "construct-cor-solution")), 
+    code_check = NULL, error_check = NULL, check = NULL, solution = structure(c("node_x_tie(ison_algebra)", 
+    "dim(node_x_tie(ison_algebra))"), chunk_opts = list(label = "construct-cor-solution")), 
     tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
         comment = NA, highlight = FALSE, size = "normalsize", 
@@ -1547,7 +1547,7 @@ <h2>Glossary</h2>
             exercise.setup = "\"data\"", purl = "FALSE"), engine = "r")), 
     code_check = NULL, error_check = NULL, check = NULL, solution = structure(c("# But it's easier to simplify the network by removing the classification into different types of ties.", 
     "# Note that this also reduces the total number of possible paths between nodes", 
-    "ison_algebra %>%", "  select_ties(-type) %>%", "  node_by_tie()"
+    "ison_algebra %>%", "  select_ties(-type) %>%", "  node_x_tie()"
     ), chunk_opts = list(label = "construct-binary-solution")), 
     tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -1642,11 +1642,11 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "scale-interp", 
     question = structure("Do you see any differences?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_f36bfd2", 
+    "character")), answers = list(structure(list(id = "lnr_ans_6736b39", 
         option = "Yes", value = "Yes", label = structure("Yes", html = TRUE, class = c("html", 
-        "character")), correct = TRUE, message = structure("I couldn’t have done it better myself.", html = TRUE, class = c("html", 
+        "character")), correct = TRUE, message = structure("Smashing!", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_adabf9c", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_7d36710", 
         option = "No", value = "No", label = structure("No", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
     "tutorial_quiz_answer"))), button_labels = list(submit = structure("<span data-i18n=\"button.questionsubmit\">Submit Answer<\u002fspan>", html = TRUE, class = c("html", 
@@ -1657,7 +1657,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "scale-interp-answer", question = "scale-interp"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 1102273723.48671, options = list()), class = c("learnr_radio", 
+    seed = 762560801.144905, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
  
@@ -1971,7 +1971,7 @@ <h2>Glossary</h2>
         list(label = "summ", code = "", opts = list(label = "\"summ\"", 
             exercise = "TRUE", exercise.setup = "\"strplot\"", 
             purl = "FALSE"), engine = "r")), code_check = NULL, 
-    error_check = NULL, check = NULL, solution = structure(c("summary(node_by_tie(alge),", 
+    error_check = NULL, check = NULL, solution = structure(c("summary(node_x_tie(alge),", 
     "        membership = node_in_structural(alge))"), chunk_opts = list(
         label = "summ-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
@@ -2198,12 +2198,12 @@ <h2>Glossary</h2>
 </p>
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(todo el progreso del ejercicio se perderá)\",\"youmustcomplete\":\"Debes completar\",\"exercise\":\"el ejercicio\",\"exercise_plural\":\"los ejercicios\",\"inthissection\":\"en esta sección antes de continuar.\",\"code\":\"Código\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Cuestionario\",\"and\":\"y\",\"or\":\"o\",\"oxfordcomma\":\"\"}}},\"pt\":{\"translation\":{\"button\":{\"runcode\":\"Executar código\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Dica\",\"hint_plural\":\"Dicas\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Próxima dica\",\"hintprev\":\"Dica anterior\",\"solution\":\"Solução\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Copiar para a área de transferência\",\"startover\":\"Reiniciar\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Continuar\",\"submitanswer\":\"Enviar resposta\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Tópico anterior\",\"nexttopic\":\"Próximo tópico\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Tentar novamente\"},\"text\":{\"startover\":\"Reiniciar\",\"areyousure\":\"Tem certeza que deseja começar novamente? (todo o progresso feito será perdido)\",\"youmustcomplete\":\"Você deve completar\",\"exercise\":\"o exercício\",\"exercise_plural\":\"os exercícios\",\"inthissection\":\"nesta seção antes de continuar.\",\"code\":\"Código\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"and\":\"e\",\"or\":\"ou\",\"oxfordcomma\":\"\"}}},\"tr\":{\"translation\":{\"button\":{\"runcode\":\"Çalıştırma Kodu\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Ipucu\",\"hint_plural\":\"İpuçları\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Sonraki İpucu\",\"hintprev\":\"Önceki İpucu\",\"solution\":\"Çözüm\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Pano'ya Kopyala\",\"startover\":\"Baştan Başlamak\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Devam et\",\"submitanswer\":\"Cevabı onayla\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Önceki Konu\",\"nexttopic\":\"Sonraki Konu\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Tekrar Deneyin\"},\"text\":{\"startover\":\"Baştan Başlamak\",\"areyousure\":\"Baştan başlamak istediğinizden emin misiniz? (tüm egzersiz ilerlemesi kaybolacak)\",\"youmustcomplete\":\"Tamamlamalısın\",\"exercise\":\"egzersiz\",\"exercise_plural\":\"egzersizler\",\"inthissection\":\"devam etmeden önce bu bölümde\",\"code\":\"Kod\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Sınav\",\"oxfordcomma\":\"\"}}},\"emo\":{\"translation\":{\"button\":{\"runcode\":\"🏃\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"💡\",\"hint_plural\":\"$t(button.hint)\",\"hinttitle\":\"$t(button.hint)\",\"solution\":\"🎯\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"📋\",\"startover\":\"⏮\",\"startovertitle\":\"Start Over\",\"continue\":\"✅\",\"submitanswer\":\"🆗\",\"submitanswertitle\":\"Submit Answer\",\"previoustopic\":\"⬅\",\"nexttopic\":\"➡\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"🔁\"},\"text\":{\"startover\":\"⏮\",\"areyousure\":\"🤔\",\"youmustcomplete\":\"⚠️ 👉 🧑‍💻\",\"exercise\":\"\",\"exercise_plural\":\"\",\"inthissection\":\"\",\"code\":\"💻\",\"enginecap\":\"$t(text.code) {{engine}}\",\"oxfordcomma\":\"\"}}},\"eu\":{\"translation\":{\"button\":{\"runcode\":\"Kodea egikaritu\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Laguntza\",\"hint_plural\":\"Laguntza\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Aurreko laguntza\",\"hintprev\":\"Hurrengo laguntza\",\"solution\":\"Ebazpena\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Arbelean kopiatu\",\"startover\":\"Berrabiarazi\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Jarraitu\",\"submitanswer\":\"Erantzuna bidali\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Aurreko atala\",\"nexttopic\":\"Hurrengo atala\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Berriro saiatu\"},\"text\":{\"startover\":\"Berrabiarazi\",\"areyousure\":\"Berriro hasi nahi duzu? (egindako lana galdu egingo da)\",\"youmustcomplete\":\"Aurrera egin baino lehen atal honetako\",\"exercise\":\"ariketa egin behar duzu.\",\"exercise_plural\":\"ariketak egin behar dituzu.\",\"inthissection\":\"\",\"code\":\"Kodea\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Galdetegia\",\"oxfordcomma\":\"\"}}},\"de\":{\"translation\":{\"button\":{\"runcode\":\"Code ausführen\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Tipp\",\"hint_plural\":\"Tipps\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Nächster Tipp\",\"hintprev\":\"Vorheriger Tipp\",\"solution\":\"Lösung\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"In die Zwischenablage kopieren\",\"startover\":\"Neustart\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Weiter\",\"submitanswer\":\"Antwort einreichen\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Vorheriges Kapitel\",\"nexttopic\":\"Nächstes Kapitel\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Nochmal versuchen\"},\"text\":{\"startover\":\"Neustart\",\"areyousure\":\"Bist du sicher, dass du neustarten willst? (der gesamte Lernfortschritt wird gelöscht)\",\"youmustcomplete\":\"Vervollstädinge\",\"exercise\":\"die Übung\",\"exercise_plural\":\"die Übungen\",\"inthissection\":\"in diesem Kapitel, bevor du fortfährst.\",\"code\":\"Code\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"blank\":\"Lücke\",\"blank_plural\":\"Lücken\",\"pleasereplaceblank\":\"Bitte ersetze {{blank}} mit gültigem Code.\",\"unparsable\":\"Dies scheint kein gültiger R Code zu sein. R kann deinen Text nicht in einen gültigen Befehl übersetzen. Du hast vielleicht vergessen, die Lücke zu füllen, einen Unterstrich zu entfernen, ein Komma zwischen Argumente zu setzen oder ein eröffnendes <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> oder <code>{<\\/code> mit einem zugehörigen <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> oder <code>}<\\/code> zu schließen.\\n\",\"and\":\"und\",\"or\":\"oder\",\"listcomma\":\", \",\"oxfordcomma\":\",\"}}},\"ko\":{\"translation\":{\"button\":{\"runcode\":\"코드 실행\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"힌트\",\"hint_plural\":\"힌트들\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"다음 힌트\",\"hintprev\":\"이전 힌트\",\"solution\":\"솔루션\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"클립보드에 복사\",\"startover\":\"재학습\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"다음 학습으로\",\"submitanswer\":\"정답 제출\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"이전 토픽\",\"nexttopic\":\"다음 토픽\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"재시도\"},\"text\":{\"startover\":\"재학습\",\"areyousure\":\"다시 시작 하시겠습니까? (모든 예제의 진행 정보가 재설정됩니다)\",\"youmustcomplete\":\"당신은 완료해야 합니다\",\"exercise\":\"연습문제\",\"exercise_plural\":\"연습문제들\",\"inthissection\":\"이 섹션을 실행하기 전에\",\"code\":\"코드\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"퀴즈\",\"blank\":\"공백\",\"blank_plural\":\"공백들\",\"exercisecontainsblank\":\"이 연습문제에는 {{count}}개의 $t(text.blank)이 포함되어 있습니다.\",\"pleasereplaceblank\":\"{{blank}}를 유효한 코드로 바꾸십시오.\",\"unparsable\":\"이것은 유효한 R 코드가 아닐 수 있습니다. R은 텍스트를 완전한 명령으로 변환하는 방법을 결정할 수 없습니다. 당신은 공백이나 밑줄을 대체하여 채우기, 인수를 컴마로 구분하기, 또는 <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> , <code>{<\\/code>로 시작하는 구문을 닫는 <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code>, <code>}<\\/code>을 잊었을 수도 있습니다.\\n\",\"and\":\"그리고\",\"or\":\"혹은\",\"listcomma\":\", \",\"oxfordcomma\":\"\"}}},\"zh\":{\"translation\":{\"button\":{\"runcode\":\"运行代码\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"提示\",\"hint_plural\":\"提示\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"下一个提示\",\"hintprev\":\"上一个提示\",\"solution\":\"答案\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"复制到剪切板\",\"startover\":\"重新开始\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"继续\",\"submitanswer\":\"提交答案\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"上一专题\",\"nexttopic\":\"下一专题\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"再试一次\"},\"text\":{\"startover\":\"重置\",\"areyousure\":\"你确定要重新开始吗? (所有当前进度将被重置)\",\"youmustcomplete\":\"你必须完成\",\"exercise\":\"练习\",\"exercise_plural\":\"练习\",\"inthissection\":\"在进行本节之前\",\"code\":\"代码\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"测试\",\"blank\":\"空\",\"blank_plural\":\"空\",\"exercisecontainsblank\":\"本练习包含{{count}}个$t(text.blank)\",\"pleasereplaceblank\":\"请在{{blank}}内填写恰当的代码\",\"unparsable\":\"这似乎不是有效的R代码。 R不知道如何将您的文本转换为完整的命令。 您是否忘了填空，忘了删除下划线，忘了在参数之间包含逗号，或者是忘了用<code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code>,<code>}<\\/code>来封闭<code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code>。 or <code>{<\\/code>。\\n\",\"unparsablequotes\":\"<p>您的R代码中似乎含有特殊格式的引号，或者弯引号(<code>{{character}}<\\/code>) 在字符串前后，在R中字符串应该被直引号(<code>&quot;<\\/code> 或者 <code>'<\\/code>)包裹。<\\/p> {{code}} <p>别担心，该错误经常在复制粘贴包含格式的代码时遇到， 您可以尝试将该行中的代码替换为以下代码，也许还有其他地方需要修改。<\\/p> {{suggestion}}\\n\",\"unparsableunicode\":\"<p>您的代码中似乎包含有异常字符(<code>{{character}}<\\/code>),导致代码无效。<\\/p> {{code}} <p>有时候你的代码可能含有看似正常字符的特殊字符，特别是当你复制粘贴其他来源代码的时候。 请试着删除这些特殊字符,重新输入<\\/p>\\n\",\"unparsableunicodesuggestion\":\"<p>您的代码中似乎包含有异常字符(<code>{{character}}<\\/code>),导致代码无效。<\\/p> {{code}} <p>有时候你的代码可能含有看似正常字符的特殊字符，特别是当你复制粘贴其他来源代码的时候。 请试着删除这些特殊字符,重新输入<\\/p>\\n\",\"and\":\"且\",\"or\":\"或\",\"listcomma\":\",\",\"oxfordcomma\":\",\"}}},\"pl\":{\"translation\":{\"button\":{\"runcode\":\"Uruchom kod\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Podpowiedź\",\"hint_plural\":\"Podpowiedzi\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Następna podpowiedź\",\"hintprev\":\"Poprzednia podpowiedź\",\"solution\":\"Rozwiązanie\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Kopiuj do schowka\",\"startover\":\"Zacznij od początku\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Kontynuuj\",\"submitanswer\":\"Wyślij\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Poprzednia sekcja\",\"nexttopic\":\"Następna sekcja\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Spróbuj ponownie\"},\"text\":{\"startover\":\"Zacznij od początku\",\"areyousure\":\"Czy na pewno chcesz zacząć od początku? (cały postęp w zadaniu zostanie utracony)\",\"youmustcomplete\":\"Musisz ukończyć\",\"exercise\":\"ćwiczenie\",\"exercise_plural\":\"ćwiczenia\",\"inthissection\":\"w tej sekcji przed kontynuowaniem\",\"code\":\"Kod\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"blank\":\"luka\",\"blank_plural\":\"luk(i)\",\"exercisecontainsblank\":\"To ćwiczenie zawiera {{count}} $t(text.blank).\",\"pleasereplaceblank\":\"Proszę uzupełnić {{blank}} prawidłowym kodem.\",\"unparsable\":\"Wygląda na to, że może to nie być prawidłowy kod R. R nie jest w stanie przetworzyć Twojego tekstu na polecenie. Mogłeś(-aś) zapomnieć wypełnić luki, usunąć podkreślnik, umieścić przecinka między argumentami, lub zamknąć znak <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> lub <code>{<\\/code> odpowiadającym <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> lub <code>}<\\/code>.\\n\",\"unparsablequotes\":\"<p>Wygląda na to, że Twój kod zawiera szczególnie sformatowane cudzysłowy lub cudzysłowy typograficzne (<code>{{character}}<\\/code>) przy ciągach znaków, co sprawia, że kod jest niepoprawny. R wymaga cudzysłowów prostych (<code>&quot;<\\/code> albo <code>'<\\/code>).<\\/p> {{code}} <p>Nie martw się, to powszechne źródło błędów, gdy kopiuje się kod z innego programu, który sam formatuje teskt. Możesz spróbować zastąpić swój kod następującym kodem. Mogą być też inne miejsca, które wymagają poprawienia.<\\/p> {{suggestion}}\\n\",\"unparsableunicode\":\"<p>Wygląda na to, że Twój kod zawiera niespodziewany znak specjalny (<code>{{character}}<\\/code>), co sprawia, że kod jest niepoprawny.<\\/p> {{code}} <p>Czasami Twój kod może zawierać znak specjalny, który wygląda jak zwykły znak, zwłaszcza jeśli kopiujesz kod z innego programu. Spróbuj usunąć znak specjalny i wpisać do ponownie ręcznie.<\\/p>\\n\",\"unparsableunicodesuggestion\":\"<p>Wygląda na to, że Twój kod zawiera niespodziewany znak specjalny (<code>{{character}}<\\/code>), co sprawia, że kod jest niepoprawny.<\\/p> {{code}} <p>Czasami Twój kod może zawierać znak specjalny, który wygląda jak zwykły znak, zwłaszcza jeśli kopiujesz kod z innego programu. Możesz spróbować zastąpić swój kod następującym kodem. Mogą być też inne miejsca, które wymagają poprawienia.<\\/p> {{suggestion}}\\n\",\"and\":\"i\",\"or\":\"lub\",\"listcomma\":\", \",\"oxfordcomma\":\"\"}}},\"no\":{\"translation\":{\"button\":{\"runcode\":\"Kjør kode\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Hint\",\"hint_plural\":\"Hint\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Neste hint\",\"hintprev\":\"Forrige hint\",\"solution\":\"Løsning\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Kopier til utklippstavle\",\"startover\":\"Start på nytt\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Fortsett\",\"submitanswer\":\"Send inn svar\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Forrige tema\",\"nexttopic\":\"Neste tema\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Prøv igjen\"},\"text\":{\"startover\":\"Start på nytt\",\"areyousure\":\"Er du sikker på at du vil starte på nytt? (All fremgang vil bli slettet)\",\"youmustcomplete\":\"Du må fullføre\",\"exercise\":\"oppgave\",\"exercise_plural\":\"oppgaver\",\"inthissection\":\"i denne seksjonen før du fortsetter.\",\"code\":\"kode\",\"enginecap\":\"{{engine}}-$t(text.code)\",\"quiz\":\"Quiz\",\"blank\":\"tomrom\",\"blank_plural\":\"tomrom\",\"exercisecontainsblank\":\"Denne oppgaven inneholder {{count}} $t(text.blank).\",\"pleasereplaceblank\":\"Erstatt {{blank}} med gyldig kode.\",\"unparsable\":\"Dette ser ikke ut til å være gyldig R-kode. Slik R ser det, utgjør ikke teksten en fullstendig kommando. Du kan ha glemt å fylle ut en luke, å fjerne en understrek, å inkludere et komma mellom argumenter, eller å lukke et åpnende <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> eller <code>{<\\/code> med et tilsvarende <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> eller <code>}<\\/code>.\\n\",\"unparsablequotes\":\"<p>Det ser ut som om R-koden din inneholder spesielt formaterte anførselstegn eller &quot;krøllete&quot; anførselstegn (<code>{{character}}<\\/code>) rundt tekststrenger, noe som gjør koden din ugyldig. R krever at tekstverdier skal være inneholdt i rette anførselstegn (<code>&quot;<\\/code> eller <code>'<\\/code>).<\\/p> {{code}} <p>Dette er et vanlig problem når du kopierer kode fra et annet program som bruker sin egen formatering på tekst.  Du kan prøve å erstatte koden i den linjen med følgende. 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From 33057e82aef6a8c08f4d238619f70f00aa9b3d41 Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Wed, 22 Apr 2026 08:30:33 +0200
Subject: [PATCH 5/6] Updated topology tutorial

---
 inst/tutorials/tutorial6/topology.Rmd  |  30 +-
 inst/tutorials/tutorial6/topology.html | 524 ++++++++++++-------------
 2 files changed, 266 insertions(+), 288 deletions(-)

diff --git a/inst/tutorials/tutorial6/topology.Rmd b/inst/tutorials/tutorial6/topology.Rmd
index 711c176..2be6e10 100644
--- a/inst/tutorials/tutorial6/topology.Rmd
+++ b/inst/tutorials/tutorial6/topology.Rmd
@@ -298,7 +298,7 @@ but with a rewiring probability of 0.25:
 ```
 
 ```{r smallwtest-solution}
-net_smallworld(generate_smallworld(50, 0.25))
+net_by_smallworld(generate_smallworld(50, 0.25))
 ```
 
 #### Scale-free graphs
@@ -346,7 +346,7 @@ comes from a power-law distribution.
 ```
 
 ```{r scaleftest-solution}
-net_scalefree(generate_scalefree(50, 2))
+net_by_scalefree(generate_scalefree(50, 2))
 ```
 
 ## Core-Periphery
@@ -431,14 +431,14 @@ one on the left and one on the right.
 
 But is it really all that much of a core-periphery structure?
 We can establish how correlated our network is compared to
-a core-periphery model of the same dimension using `net_core()`.
+a core-periphery model of the same dimension using `net_by_core()`.
 
 ```{r netcore, exercise=TRUE, purl = FALSE, exercise.setup="gnet"}
 
 ```
 
 ```{r netcore-solution}
-net_core(lawfirm, node_is_core(lawfirm))
+net_by_core(lawfirm, node_is_core(lawfirm))
 ```
 
 ```{r corecorr-qa, echo=FALSE, purl = FALSE}
@@ -504,12 +504,12 @@ question("There a statistically significant association between the core assignm
 An alternative route is to identify 'core' nodes
 depending on their `r gloss("k-coreness","kcoreness")`.
 In `{manynet}`, we can return nodes _k_-coreness
-with `node_coreness()` instead of 
+with `node_by_kcoreness()` instead of 
 the `node_is_core()` used for core-periphery.
 
 ```{r nodecoren, exercise=TRUE, purl = FALSE, exercise.setup="gnet"}
 lawfirm %>% 
-  mutate(ncn = node_kcoreness()) %>% 
+  mutate(ncn = node_by_kcoreness()) %>% 
   graphr(node_color = "ncn")
 ```
 
@@ -572,12 +572,12 @@ Ok, so let's now take a closer look at how to investigate the degree of
 hierarchy in a given network.
 The classic example would be to look at a tree network,
 like the one constructed earlier.
-Let's try the function `net_by_hierarchy()` on a tree network of 12 nodes.
+Let's try the function `net_x_hierarchy()` on a tree network of 12 nodes.
 
 ```{r treeh, exercise = TRUE, purl = FALSE}
 treeleven <- create_tree(11, directed = TRUE)
-net_by_hierarchy(treeleven)
-rowMeans(net_by_hierarchy(treeleven))
+net_x_hierarchy(treeleven)
+rowMeans(net_x_hierarchy(treeleven))
 ```
 
 We see here four different measures of hierarchy:
@@ -606,9 +606,9 @@ graphr(fict_thrones)
 
 ```{r hierarchy-solution}
 graphr(ison_emotions)
-net_by_hierarchy(ison_emotions)
+net_x_hierarchy(ison_emotions)
 graphr(fict_thrones)
-net_by_hierarchy(fict_thrones)
+net_x_hierarchy(fict_thrones)
 ```
 
 Actually, these two networks have the same average hierarchy score of around 0.525.
@@ -642,7 +642,7 @@ First, we might be interested in whether the network is `r gloss("connected","co
 ```
 
 ```{r connected-solution}
-net_connectedness(ison_adolescents)
+net_by_connectedness(ison_adolescents)
 ```
 
 This measure gets at the proportion of dyads that can reach each other in the network.
@@ -675,7 +675,7 @@ or how many dropped nodes it would take to (further) fragment the network.
 ```
 
 ```{r cohesion-solution}
-net_cohesion(ison_adolescents)
+net_by_cohesion(ison_adolescents)
 ```
 
 ```{r cohesion-qa, echo=FALSE, purl = FALSE}
@@ -740,7 +740,7 @@ Here we are interested in identifying which ties are `r gloss("bridges","bridge"
 ```
 
 ```{r tieside-solution}
-net_adhesion(ison_adolescents)
+net_by_adhesion(ison_adolescents)
 ison_adolescents |> mutate_ties(cut = tie_is_bridge(ison_adolescents)) |> 
   graphr(edge_color = "cut")
 ```
@@ -754,7 +754,7 @@ This is called (rather confusingly) tie cohesion.
 ```
 
 ```{r tiecoh-solution}
-ison_adolescents |> mutate_ties(coh = tie_cohesion(ison_adolescents)) |> 
+ison_adolescents |> mutate_ties(coh = tie_by_cohesion(ison_adolescents)) |> 
   graphr(edge_size = "coh")
 ```
 
diff --git a/inst/tutorials/tutorial6/topology.html b/inst/tutorials/tutorial6/topology.html
index 3fc5d78..09805b1 100644
--- a/inst/tutorials/tutorial6/topology.html
+++ b/inst/tutorials/tutorial6/topology.html
@@ -13,7 +13,7 @@
 
 <meta name="progressive" content="false" />
 <meta name="allow-skip" content="false" />
-<meta name="learnr-version-prerender" content="0.11.5" />
+<meta name="learnr-version-prerender" content="0.11.6" />
 
 <title>Topology and Resilience</title>
 
@@ -380,7 +380,7 @@ <h4>Small-world graphs</h4>
 <div class="tutorial-exercise-support" data-label="smallwtest-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_smallworld(generate_smallworld(50, 0.25))</code></pre>
+<pre class="text"><code>net_by_smallworld(generate_smallworld(50, 0.25))</code></pre>
 </div>
 </div>
 <div id="section-scale-free-graphs" class="section level4">
@@ -431,7 +431,7 @@ <h4>Scale-free graphs</h4>
 <div class="tutorial-exercise-support" data-label="scaleftest-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_scalefree(generate_scalefree(50, 2))</code></pre>
+<pre class="text"><code>net_by_scalefree(generate_scalefree(50, 2))</code></pre>
 </div>
 </div>
 </div>
@@ -584,7 +584,7 @@ <h3>Coreness</h3>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>lawfirm %&gt;% 
-  mutate(ncn = node_kcoreness()) %&gt;% 
+  mutate(ncn = node_by_kcoreness()) %&gt;% 
   graphr(node_color = &quot;ncn&quot;)</code></pre>
 <script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{&quot;engine&quot;:&quot;R&quot;}\">R Code<\/span>"}</script>
 </div>
@@ -639,8 +639,8 @@ <h3>Measuring GTDH</h3>
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
 <pre class="text"><code>treeleven &lt;- create_tree(11, directed = TRUE)
-net_by_hierarchy(treeleven)
-rowMeans(net_by_hierarchy(treeleven))</code></pre>
+net_x_hierarchy(treeleven)
+rowMeans(net_x_hierarchy(treeleven))</code></pre>
 <script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{&quot;engine&quot;:&quot;R&quot;}\">R Code<\/span>"}</script>
 </div>
 <p>We see here four different measures of hierarchy:</p>
@@ -675,9 +675,9 @@ <h3>Comparing GTDH</h3>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>graphr(ison_emotions)
-net_by_hierarchy(ison_emotions)
+net_x_hierarchy(ison_emotions)
 graphr(fict_thrones)
-net_by_hierarchy(fict_thrones)</code></pre>
+net_x_hierarchy(fict_thrones)</code></pre>
 </div>
 <p>Actually, these two networks have the same average hierarchy score of
 around 0.525. But they have quite different profiles. Can you make sense
@@ -711,7 +711,7 @@ <h3>How cohesive is the network?</h3>
 <div class="tutorial-exercise-support" data-label="connected-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_connectedness(ison_adolescents)</code></pre>
+<pre class="text"><code>net_by_connectedness(ison_adolescents)</code></pre>
 </div>
 <p>This measure gets at the proportion of dyads that can reach each
 other in the network. In this case, the proportion is 1, i.e. all nodes
@@ -745,7 +745,7 @@ <h3>How cohesive is the network?</h3>
 <div class="tutorial-exercise-support" data-label="cohesion-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_cohesion(ison_adolescents)</code></pre>
+<pre class="text"><code>net_by_cohesion(ison_adolescents)</code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
 <div data-label="cohesion-qa" class="tutorial-question panel-body">
@@ -812,7 +812,7 @@ <h3>Identifying bridges</h3>
 <div class="tutorial-exercise-support" data-label="tieside-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_adhesion(ison_adolescents)
+<pre class="text"><code>net_by_adhesion(ison_adolescents)
 ison_adolescents |&gt; mutate_ties(cut = tie_is_bridge(ison_adolescents)) |&gt; 
   graphr(edge_color = &quot;cut&quot;)</code></pre>
 </div>
@@ -827,7 +827,7 @@ <h3>Identifying bridges</h3>
 <div class="tutorial-exercise-support" data-label="tiecoh-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>ison_adolescents |&gt; mutate_ties(coh = tie_cohesion(ison_adolescents)) |&gt; 
+<pre class="text"><code>ison_adolescents |&gt; mutate_ties(coh = tie_by_cohesion(ison_adolescents)) |&gt; 
   graphr(edge_size = &quot;coh&quot;)</code></pre>
 </div>
 <p>Where would you target your efforts if you wanted to fragment this
@@ -944,8 +944,6 @@ <h2>Glossary</h2>
 <p>
 <script type="application/shiny-prerendered" data-context="server-start">
 library(learnr)
-library(manynet)
-library(autograph)
 knitr::opts_chunk$set(echo = FALSE)
 clear_glossary()
 learnr::random_phrases_add(language = "fr",
@@ -1006,8 +1004,7 @@ <h2>Glossary</h2>
 
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 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
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+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "empty", 
+    code = "", opts = list(label = "\"empty\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("(graphr(create_empty(50), \"circle\") + ggtitle(\"Empty graph\"))", 
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     "(graphr(create_filled(50/2)) + ggtitle(\"Complete graph (smaller)\"))"
@@ -1067,8 +1064,7 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "star", global_setup = structure(c("library(learnr)", 
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 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
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@@ -1087,10 +1083,10 @@ <h2>Glossary</h2>
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+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "star", 
+    code = "", opts = list(label = "\"star\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("(graphr(create_star(50)) + ggtitle(\"Star graph\"))", 
     "(graphr(create_star(50, directed = TRUE)) + ggtitle(\"Star out\"))", 
     "(graphr(to_redirected(create_star(50, directed = TRUE))) + ggtitle(\"Star in\"))"
@@ -1128,8 +1124,7 @@ <h2>Glossary</h2>
 
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@@ -1148,10 +1143,10 @@ <h2>Glossary</h2>
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+    eval = TRUE)), setup = NULL, chunks = list(list(label = "tree", 
+    code = "", opts = list(label = "\"tree\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("# width argument specifies the breadth of the branches", 
     "(graphr(create_tree(50, width = 2)) + ggtitle(\"Tree graph\"))", 
     "(graphr(create_tree(50, width = 2, directed = TRUE)) + ggtitle(\"Tree out\"))", 
@@ -1182,27 +1177,27 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
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-    "character")), correct = FALSE, message = structure("Try it again. You get better each time.", html = TRUE, class = c("html", 
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@@ -1211,7 +1206,7 @@ <h2>Glossary</h2>
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@@ -1226,8 +1221,7 @@ <h2>Glossary</h2>
  
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+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
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@@ -1286,8 +1280,7 @@ <h2>Glossary</h2>
  
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@@ -1306,10 +1299,10 @@ <h2>Glossary</h2>
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+    code = "", opts = list(label = "\"rings\"", exercise = "TRUE", 
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     check = NULL, solution = structure(c("(graphr(create_ring(50)) + ggtitle(\"Ring graph\", subtitle = \"Starring Naomi Watts\"))", 
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@@ -1347,8 +1340,7 @@ <h2>Glossary</h2>
  
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 learnr:::store_exercise_cache(structure(list(label = "random", global_setup = structure(c("library(learnr)", 
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-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1367,10 +1359,10 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "random", code = "", opts = list(
-        label = "\"random\"", exercise = "TRUE", purl = "FALSE"), 
-        engine = "r")), code_check = NULL, error_check = NULL, 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "random", 
+    code = "", opts = list(label = "\"random\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("(graphr(generate_random(50, 0.08)) + ggtitle(\"Random 1 graph\"))", 
     "(graphr(generate_random(50, 0.08)) + ggtitle(\"Random 2 graph\"))", 
     "(graphr(generate_random(50, 0.08)) + ggtitle(\"Random 3 graph\"))"
@@ -1408,8 +1400,7 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "randomno", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1428,10 +1419,10 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "randomno", code = "", opts = list(
-        label = "\"randomno\"", exercise = "TRUE", purl = "FALSE"), 
-        engine = "r")), code_check = NULL, error_check = NULL, 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "randomno", 
+    code = "", opts = list(label = "\"randomno\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure("(erdren4 <- graphr(generate_random(50, 200)) + ggtitle(\"Random 1 graph\"))", chunk_opts = list(
         label = "randomno-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
@@ -1466,8 +1457,7 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "smallw", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1486,10 +1476,10 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "smallw", code = "", opts = list(
-        label = "\"smallw\"", exercise = "TRUE", purl = "FALSE"), 
-        engine = "r")), code_check = NULL, error_check = NULL, 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "smallw", 
+    code = "", opts = list(label = "\"smallw\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("(graphr(generate_smallworld(50, 0.025)) + ggtitle(\"Smallworld 1 graph\"))", 
     "(graphr(generate_smallworld(50, 0.025)) + ggtitle(\"Smallworld 2 graph\"))", 
     "(graphr(generate_smallworld(50, 0.025)) + ggtitle(\"Smallworld 3 graph\"))"
@@ -1527,8 +1517,7 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "smallwtest", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1547,11 +1536,11 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "smallwtest", code = "", opts = list(
-        label = "\"smallwtest\"", exercise = "TRUE", purl = "FALSE"), 
-        engine = "r")), code_check = NULL, error_check = NULL, 
-    check = NULL, solution = structure("net_smallworld(generate_smallworld(50, 0.25))", chunk_opts = list(
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "smallwtest", 
+    code = "", opts = list(label = "\"smallwtest\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
+    check = NULL, solution = structure("net_by_smallworld(generate_smallworld(50, 0.25))", chunk_opts = list(
         label = "smallwtest-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
         tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA, 
@@ -1585,8 +1574,7 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "scalef", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1605,10 +1593,10 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "scalef", code = "", opts = list(
-        label = "\"scalef\"", exercise = "TRUE", purl = "FALSE"), 
-        engine = "r")), code_check = NULL, error_check = NULL, 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "scalef", 
+    code = "", opts = list(label = "\"scalef\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("(graphr(generate_scalefree(50, 0.5)) +", 
     "    ggtitle(\"Scalefree 1 graph\", subtitle = \"Power = .5\"))", 
     "(graphr(generate_scalefree(50, 1)) +", "    ggtitle(\"Scalefree 2 graph\", subtitle = \"Power = 1\"))", 
@@ -1647,8 +1635,7 @@ <h2>Glossary</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "scaleftest", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1667,11 +1654,11 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "scaleftest", code = "", opts = list(
-        label = "\"scaleftest\"", exercise = "TRUE", purl = "FALSE"), 
-        engine = "r")), code_check = NULL, error_check = NULL, 
-    check = NULL, solution = structure("net_scalefree(generate_scalefree(50, 2))", chunk_opts = list(
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "scaleftest", 
+    code = "", opts = list(label = "\"scaleftest\"", exercise = "TRUE", 
+        purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
+    check = NULL, solution = structure("net_by_scalefree(generate_scalefree(50, 2))", chunk_opts = list(
         label = "scaleftest-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
         tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA, 
@@ -1699,11 +1686,11 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "corevcomm-qa", 
     question = structure("Can a single network have both a community structure and a core-periphery structure?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_45abc9d", 
+    "character")), answers = list(structure(list(id = "lnr_ans_a6551e5", 
         option = "No", value = "No", label = structure("No", html = TRUE, class = c("html", 
-        "character")), correct = FALSE, message = structure("Let’s try it again.", html = TRUE, class = c("html", 
+        "character")), correct = FALSE, message = structure("Try it again. You get better each time.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_a80c984", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_1fbe9f9", 
         option = "Yes", value = "Yes", label = structure("Yes", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("That’s right. For example, the core and periphery might represent two or more communities.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
@@ -1715,7 +1702,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "corevcomm-qa-answer", question = "corevcomm-qa"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 853665372.602481, options = list()), class = c("learnr_radio", 
+    seed = 1776283964.67285, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
  
@@ -1729,8 +1716,7 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "core", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1749,10 +1735,10 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "core", code = "", opts = list(
-        label = "\"core\"", exercise = "TRUE", purl = "FALSE", 
-        fig.width = "9"), engine = "r")), code_check = NULL, 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "core", 
+    code = "", opts = list(label = "\"core\"", exercise = "TRUE", 
+        purl = "FALSE", fig.width = "9"), engine = "r")), code_check = NULL, 
     error_check = NULL, check = NULL, solution = structure("(graphr(create_core(50)) + ggtitle(\"Core\"))", chunk_opts = list(
         label = "core-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
@@ -1787,8 +1773,7 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "gnet", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1807,10 +1792,11 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "gnet", code = "lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
-        opts = list(label = "\"gnet\"", exercise = "TRUE", purl = "FALSE", 
-            fig.width = "9"), engine = "r")), code_check = NULL, 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "gnet", 
+    code = "lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
+    opts = list(label = "\"gnet\"", exercise = "TRUE", purl = "FALSE", 
+        fig.width = "9"), engine = "r")), code_check = NULL, 
     error_check = NULL, check = NULL, solution = structure(c("lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
     "graphr(lawfirm, node_color = \"school\", edge_color = \"darkgray\")", 
     "graphr(lawfirm, node_color = \"gender\", edge_color = \"darkgray\")", 
@@ -1850,8 +1836,7 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "nodecore", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1870,7 +1855,8 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = "lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = "lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
     chunks = list(list(label = "gnet", code = "lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
         opts = list(label = "\"gnet\"", exercise = "TRUE", purl = "FALSE", 
             fig.width = "9"), engine = "r"), list(label = "nodecore", 
@@ -1913,8 +1899,7 @@ <h2>Glossary</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "netcore", global_setup = structure(c("library(learnr)", 
-"library(manynet)", "library(autograph)", "knitr::opts_chunk$set(echo = FALSE)", 
-"clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
+"knitr::opts_chunk$set(echo = FALSE)", "clear_glossary()", "learnr::random_phrases_add(language = \"fr\",", 
 "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail\",", "                                      \"Excellent travail!\",", 
 "                                      \"Bravo!\",", "                                      \"Super!\",", 
@@ -1933,7 +1918,8 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = "lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = "lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
     chunks = list(list(label = "gnet", code = "lawfirm <- ison_lawfirm |> to_uniplex(\"friends\") |> to_undirected()", 
         opts = list(label = "\"gnet\"", exercise = "TRUE", purl = "FALSE", 
             fig.width = "9"), engine = "r"), list(label = "netcore", 
@@ -1967,30 +1953,30 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "corecorr-qa", 
     question = structure("What can we say about this correlation.", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_9fd470e", 
+    "character")), answers = list(structure(list(id = "lnr_ans_b2b9bd0", 
         option = "It is a perfect negative relationship", value = "It is a perfect negative relationship", 
         label = structure("It is a perfect negative relationship", html = TRUE, class = c("html", 
-        "character")), correct = FALSE, message = structure("Try it again. Perseverence is the key to success.", html = TRUE, class = c("html", 
+        "character")), correct = FALSE, message = structure("Please try again.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_53b516c", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_fe0d352", 
         option = "It is fairly strong", value = "It is fairly strong", 
         label = structure("It is fairly strong", html = TRUE, class = c("html", 
-        "character")), correct = FALSE, message = structure("Give it another try.", html = TRUE, class = c("html", 
+        "character")), correct = FALSE, message = structure("Try it again. You get better each time.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_9f88d65", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_d1b42ad", 
         option = "It is positive", value = "It is positive", 
         label = structure("It is positive", html = TRUE, class = c("html", 
-        "character")), correct = FALSE, message = structure("Don’t give up now, try it one more time.", html = TRUE, class = c("html", 
+        "character")), correct = FALSE, message = structure("Let’s try it again.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_fc1c04a", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_e5eb03c", 
         option = "There is absolutely no correlation", value = "There is absolutely no correlation", 
         label = structure("There is absolutely no correlation", html = TRUE, class = c("html", 
-        "character")), correct = FALSE, message = structure("Try it again. You get better each time.", html = TRUE, class = c("html", 
+        "character")), correct = FALSE, message = structure("Let’s try it again.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_85910b4", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_edf5f6a", 
         option = "None of the above", value = "None of the above", 
         label = structure("None of the above", html = TRUE, class = c("html", 
-        "character")), correct = TRUE, message = structure("That’s glorious!", html = TRUE, class = c("html", 
+        "character")), correct = TRUE, message = structure("Bravo!", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
     "tutorial_quiz_answer"))), button_labels = list(submit = structure("<span data-i18n=\"button.questionsubmit\">Submit Answer<\u002fspan>", html = TRUE, class = c("html", 
     "character")), try_again = structure("<span data-i18n=\"button.questiontryagain\">Try Again<\u002fspan>", html = TRUE, class = c("html", 
@@ -2000,7 +1986,7 @@ <h2>Glossary</h2>
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@@ -2887,15 +2865,15 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "freeplay", code = "", opts = list(
-        label = "\"freeplay\"", exercise = "TRUE"), engine = "r")), 
-    code_check = NULL, error_check = NULL, check = NULL, solution = NULL, 
-    tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup", 
-        tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
-        comment = NA, highlight = FALSE, size = "normalsize", 
-        background = "#F7F7F7", strip.white = TRUE, cache = 0, 
-        cache.path = "topology_cache/html/", cache.vars = NULL, 
+), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
+    eval = TRUE)), setup = NULL, chunks = list(list(label = "freeplay", 
+    code = "", opts = list(label = "\"freeplay\"", exercise = "TRUE"), 
+    engine = "r")), code_check = NULL, error_check = NULL, check = NULL, 
+    solution = NULL, tests = NULL, options = list(eval = FALSE, 
+        echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL, 
+        collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE, 
+        size = "normalsize", background = "#F7F7F7", strip.white = TRUE, 
+        cache = 0, cache.path = "topology_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
         fig.align = "default", fig.path = "topology_files/figure-html/", 
@@ -2916,12 +2894,12 @@ <h2>Glossary</h2>
 </p>
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(tüm egzersiz ilerlemesi kaybolacak)\",\"youmustcomplete\":\"Tamamlamalısın\",\"exercise\":\"egzersiz\",\"exercise_plural\":\"egzersizler\",\"inthissection\":\"devam etmeden önce bu bölümde\",\"code\":\"Kod\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Sınav\",\"oxfordcomma\":\"\"}}},\"emo\":{\"translation\":{\"button\":{\"runcode\":\"🏃\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"💡\",\"hint_plural\":\"$t(button.hint)\",\"hinttitle\":\"$t(button.hint)\",\"solution\":\"🎯\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"📋\",\"startover\":\"⏮\",\"startovertitle\":\"Start Over\",\"continue\":\"✅\",\"submitanswer\":\"🆗\",\"submitanswertitle\":\"Submit Answer\",\"previoustopic\":\"⬅\",\"nexttopic\":\"➡\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"🔁\"},\"text\":{\"startover\":\"⏮\",\"areyousure\":\"🤔\",\"youmustcomplete\":\"⚠️ 👉 🧑‍💻\",\"exercise\":\"\",\"exercise_plural\":\"\",\"inthissection\":\"\",\"code\":\"💻\",\"enginecap\":\"$t(text.code) {{engine}}\",\"oxfordcomma\":\"\"}}},\"eu\":{\"translation\":{\"button\":{\"runcode\":\"Kodea egikaritu\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Laguntza\",\"hint_plural\":\"Laguntza\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Aurreko laguntza\",\"hintprev\":\"Hurrengo laguntza\",\"solution\":\"Ebazpena\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Arbelean kopiatu\",\"startover\":\"Berrabiarazi\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Jarraitu\",\"submitanswer\":\"Erantzuna bidali\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Aurreko atala\",\"nexttopic\":\"Hurrengo atala\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Berriro saiatu\"},\"text\":{\"startover\":\"Berrabiarazi\",\"areyousure\":\"Berriro hasi nahi duzu? (egindako lana galdu egingo da)\",\"youmustcomplete\":\"Aurrera egin baino lehen atal honetako\",\"exercise\":\"ariketa egin behar duzu.\",\"exercise_plural\":\"ariketak egin behar dituzu.\",\"inthissection\":\"\",\"code\":\"Kodea\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Galdetegia\",\"oxfordcomma\":\"\"}}},\"de\":{\"translation\":{\"button\":{\"runcode\":\"Code ausführen\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Tipp\",\"hint_plural\":\"Tipps\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Nächster Tipp\",\"hintprev\":\"Vorheriger Tipp\",\"solution\":\"Lösung\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"In die Zwischenablage kopieren\",\"startover\":\"Neustart\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Weiter\",\"submitanswer\":\"Antwort einreichen\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Vorheriges Kapitel\",\"nexttopic\":\"Nächstes Kapitel\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Nochmal versuchen\"},\"text\":{\"startover\":\"Neustart\",\"areyousure\":\"Bist du sicher, dass du neustarten willst? (der gesamte Lernfortschritt wird gelöscht)\",\"youmustcomplete\":\"Vervollstädinge\",\"exercise\":\"die Übung\",\"exercise_plural\":\"die Übungen\",\"inthissection\":\"in diesem Kapitel, bevor du fortfährst.\",\"code\":\"Code\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"blank\":\"Lücke\",\"blank_plural\":\"Lücken\",\"pleasereplaceblank\":\"Bitte ersetze {{blank}} mit gültigem Code.\",\"unparsable\":\"Dies scheint kein gültiger R Code zu sein. R kann deinen Text nicht in einen gültigen Befehl übersetzen. Du hast vielleicht vergessen, die Lücke zu füllen, einen Unterstrich zu entfernen, ein Komma zwischen Argumente zu setzen oder ein eröffnendes <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> oder <code>{<\\/code> mit einem zugehörigen <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> oder <code>}<\\/code> zu schließen.\\n\",\"and\":\"und\",\"or\":\"oder\",\"listcomma\":\", \",\"oxfordcomma\":\",\"}}},\"ko\":{\"translation\":{\"button\":{\"runcode\":\"코드 실행\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"힌트\",\"hint_plural\":\"힌트들\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"다음 힌트\",\"hintprev\":\"이전 힌트\",\"solution\":\"솔루션\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"클립보드에 복사\",\"startover\":\"재학습\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"다음 학습으로\",\"submitanswer\":\"정답 제출\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"이전 토픽\",\"nexttopic\":\"다음 토픽\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"재시도\"},\"text\":{\"startover\":\"재학습\",\"areyousure\":\"다시 시작 하시겠습니까? (모든 예제의 진행 정보가 재설정됩니다)\",\"youmustcomplete\":\"당신은 완료해야 합니다\",\"exercise\":\"연습문제\",\"exercise_plural\":\"연습문제들\",\"inthissection\":\"이 섹션을 실행하기 전에\",\"code\":\"코드\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"퀴즈\",\"blank\":\"공백\",\"blank_plural\":\"공백들\",\"exercisecontainsblank\":\"이 연습문제에는 {{count}}개의 $t(text.blank)이 포함되어 있습니다.\",\"pleasereplaceblank\":\"{{blank}}를 유효한 코드로 바꾸십시오.\",\"unparsable\":\"이것은 유효한 R 코드가 아닐 수 있습니다. R은 텍스트를 완전한 명령으로 변환하는 방법을 결정할 수 없습니다. 당신은 공백이나 밑줄을 대체하여 채우기, 인수를 컴마로 구분하기, 또는 <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> , <code>{<\\/code>로 시작하는 구문을 닫는 <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code>, <code>}<\\/code>을 잊었을 수도 있습니다.\\n\",\"and\":\"그리고\",\"or\":\"혹은\",\"listcomma\":\", \",\"oxfordcomma\":\"\"}}},\"zh\":{\"translation\":{\"button\":{\"runcode\":\"运行代码\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"提示\",\"hint_plural\":\"提示\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"下一个提示\",\"hintprev\":\"上一个提示\",\"solution\":\"答案\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"复制到剪切板\",\"startover\":\"重新开始\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"继续\",\"submitanswer\":\"提交答案\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"上一专题\",\"nexttopic\":\"下一专题\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"再试一次\"},\"text\":{\"startover\":\"重置\",\"areyousure\":\"你确定要重新开始吗? (所有当前进度将被重置)\",\"youmustcomplete\":\"你必须完成\",\"exercise\":\"练习\",\"exercise_plural\":\"练习\",\"inthissection\":\"在进行本节之前\",\"code\":\"代码\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"测试\",\"blank\":\"空\",\"blank_plural\":\"空\",\"exercisecontainsblank\":\"本练习包含{{count}}个$t(text.blank)\",\"pleasereplaceblank\":\"请在{{blank}}内填写恰当的代码\",\"unparsable\":\"这似乎不是有效的R代码。 R不知道如何将您的文本转换为完整的命令。 您是否忘了填空，忘了删除下划线，忘了在参数之间包含逗号，或者是忘了用<code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code>,<code>}<\\/code>来封闭<code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code>。 or <code>{<\\/code>。\\n\",\"unparsablequotes\":\"<p>您的R代码中似乎含有特殊格式的引号，或者弯引号(<code>{{character}}<\\/code>) 在字符串前后，在R中字符串应该被直引号(<code>&quot;<\\/code> 或者 <code>'<\\/code>)包裹。<\\/p> {{code}} <p>别担心，该错误经常在复制粘贴包含格式的代码时遇到， 您可以尝试将该行中的代码替换为以下代码，也许还有其他地方需要修改。<\\/p> {{suggestion}}\\n\",\"unparsableunicode\":\"<p>您的代码中似乎包含有异常字符(<code>{{character}}<\\/code>),导致代码无效。<\\/p> {{code}} <p>有时候你的代码可能含有看似正常字符的特殊字符，特别是当你复制粘贴其他来源代码的时候。 请试着删除这些特殊字符,重新输入<\\/p>\\n\",\"unparsableunicodesuggestion\":\"<p>您的代码中似乎包含有异常字符(<code>{{character}}<\\/code>),导致代码无效。<\\/p> {{code}} <p>有时候你的代码可能含有看似正常字符的特殊字符，特别是当你复制粘贴其他来源代码的时候。 请试着删除这些特殊字符,重新输入<\\/p>\\n\",\"and\":\"且\",\"or\":\"或\",\"listcomma\":\",\",\"oxfordcomma\":\",\"}}},\"pl\":{\"translation\":{\"button\":{\"runcode\":\"Uruchom kod\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Podpowiedź\",\"hint_plural\":\"Podpowiedzi\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Następna podpowiedź\",\"hintprev\":\"Poprzednia podpowiedź\",\"solution\":\"Rozwiązanie\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Kopiuj do schowka\",\"startover\":\"Zacznij od początku\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Kontynuuj\",\"submitanswer\":\"Wyślij\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Poprzednia sekcja\",\"nexttopic\":\"Następna sekcja\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Spróbuj ponownie\"},\"text\":{\"startover\":\"Zacznij od początku\",\"areyousure\":\"Czy na pewno chcesz zacząć od początku? (cały postęp w zadaniu zostanie utracony)\",\"youmustcomplete\":\"Musisz ukończyć\",\"exercise\":\"ćwiczenie\",\"exercise_plural\":\"ćwiczenia\",\"inthissection\":\"w tej sekcji przed kontynuowaniem\",\"code\":\"Kod\",\"enginecap\":\"$t(text.code) {{engine}}\",\"quiz\":\"Quiz\",\"blank\":\"luka\",\"blank_plural\":\"luk(i)\",\"exercisecontainsblank\":\"To ćwiczenie zawiera {{count}} $t(text.blank).\",\"pleasereplaceblank\":\"Proszę uzupełnić {{blank}} prawidłowym kodem.\",\"unparsable\":\"Wygląda na to, że może to nie być prawidłowy kod R. R nie jest w stanie przetworzyć Twojego tekstu na polecenie. Mogłeś(-aś) zapomnieć wypełnić luki, usunąć podkreślnik, umieścić przecinka między argumentami, lub zamknąć znak <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> lub <code>{<\\/code> odpowiadającym <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> lub <code>}<\\/code>.\\n\",\"unparsablequotes\":\"<p>Wygląda na to, że Twój kod zawiera szczególnie sformatowane cudzysłowy lub cudzysłowy typograficzne (<code>{{character}}<\\/code>) przy ciągach znaków, co sprawia, że kod jest niepoprawny. R wymaga cudzysłowów prostych (<code>&quot;<\\/code> albo <code>'<\\/code>).<\\/p> {{code}} <p>Nie martw się, to powszechne źródło błędów, gdy kopiuje się kod z innego programu, który sam formatuje teskt. Możesz spróbować zastąpić swój kod następującym kodem. Mogą być też inne miejsca, które wymagają poprawienia.<\\/p> {{suggestion}}\\n\",\"unparsableunicode\":\"<p>Wygląda na to, że Twój kod zawiera niespodziewany znak specjalny (<code>{{character}}<\\/code>), co sprawia, że kod jest niepoprawny.<\\/p> {{code}} <p>Czasami Twój kod może zawierać znak specjalny, który wygląda jak zwykły znak, zwłaszcza jeśli kopiujesz kod z innego programu. Spróbuj usunąć znak specjalny i wpisać do ponownie ręcznie.<\\/p>\\n\",\"unparsableunicodesuggestion\":\"<p>Wygląda na to, że Twój kod zawiera niespodziewany znak specjalny (<code>{{character}}<\\/code>), co sprawia, że kod jest niepoprawny.<\\/p> {{code}} <p>Czasami Twój kod może zawierać znak specjalny, który wygląda jak zwykły znak, zwłaszcza jeśli kopiujesz kod z innego programu. Możesz spróbować zastąpić swój kod następującym kodem. Mogą być też inne miejsca, które wymagają poprawienia.<\\/p> {{suggestion}}\\n\",\"and\":\"i\",\"or\":\"lub\",\"listcomma\":\", \",\"oxfordcomma\":\"\"}}},\"no\":{\"translation\":{\"button\":{\"runcode\":\"Kjør kode\",\"runcodetitle\":\"$t(button.runcode) ({{kbd}})\",\"hint\":\"Hint\",\"hint_plural\":\"Hint\",\"hinttitle\":\"$t(button.hint)\",\"hintnext\":\"Neste hint\",\"hintprev\":\"Forrige hint\",\"solution\":\"Løsning\",\"solutiontitle\":\"$t(button.solution)\",\"copyclipboard\":\"Kopier til utklippstavle\",\"startover\":\"Start på nytt\",\"startovertitle\":\"$t(button.startover)\",\"continue\":\"Fortsett\",\"submitanswer\":\"Send inn svar\",\"submitanswertitle\":\"$t(button.submitanswer)\",\"previoustopic\":\"Forrige tema\",\"nexttopic\":\"Neste tema\",\"questionsubmit\":\"$t(button.submitanswer)\",\"questiontryagain\":\"Prøv igjen\"},\"text\":{\"startover\":\"Start på nytt\",\"areyousure\":\"Er du sikker på at du vil starte på nytt? (All fremgang vil bli slettet)\",\"youmustcomplete\":\"Du må fullføre\",\"exercise\":\"oppgave\",\"exercise_plural\":\"oppgaver\",\"inthissection\":\"i denne seksjonen før du fortsetter.\",\"code\":\"kode\",\"enginecap\":\"{{engine}}-$t(text.code)\",\"quiz\":\"Quiz\",\"blank\":\"tomrom\",\"blank_plural\":\"tomrom\",\"exercisecontainsblank\":\"Denne oppgaven inneholder {{count}} $t(text.blank).\",\"pleasereplaceblank\":\"Erstatt {{blank}} med gyldig kode.\",\"unparsable\":\"Dette ser ikke ut til å være gyldig R-kode. Slik R ser det, utgjør ikke teksten en fullstendig kommando. Du kan ha glemt å fylle ut en luke, å fjerne en understrek, å inkludere et komma mellom argumenter, eller å lukke et åpnende <code>&quot;<\\/code>, <code>'<\\/code>, <code>(<\\/code> eller <code>{<\\/code> med et tilsvarende <code>&quot;<\\/code>, <code>'<\\/code>, <code>)<\\/code> eller <code>}<\\/code>.\\n\",\"unparsablequotes\":\"<p>Det ser ut som om R-koden din inneholder spesielt formaterte anførselstegn eller &quot;krøllete&quot; anførselstegn (<code>{{character}}<\\/code>) rundt tekststrenger, noe som gjør koden din ugyldig. R krever at tekstverdier skal være inneholdt i rette anførselstegn (<code>&quot;<\\/code> eller <code>'<\\/code>).<\\/p> {{code}} <p>Dette er et vanlig problem når du kopierer kode fra et annet program som bruker sin egen formatering på tekst.  Du kan prøve å erstatte koden i den linjen med følgende. 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From d1a2aa0cf50917e1d90e59df21eae367bd1a14c7 Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Fri, 24 Apr 2026 11:51:17 +0200
Subject: [PATCH 6/6] #patch update

---
 DESCRIPTION |  4 ++--
 NEWS.md     | 13 +++++++++++++
 2 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 5c8076c..f36cf78 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: netrics
 Title: Many Ways to Measure and Classify Membership for Networks, Nodes, and Ties
-Version: 0.2.1
-Date: 2026-04-04
+Version: 0.2.2
+Date: 2026-04-24
 Description: Many tools for calculating network, node, or tie
    marks, measures, motifs and memberships of many different types of networks.
    Marks identify structural positions, measures quantify network properties,
diff --git a/NEWS.md b/NEWS.md
index 97389bc..00a7dc4 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,16 @@
+# netrics 0.2.2
+
+## Package
+
+- Updated logos
+
+## Tutorials
+
+- Updated centrality tutorial
+- Updated community tutorial
+- Updated position tutorial
+- Updated topology tutorial
+
 # netrics 0.2.1
 
 ## Package