From 9bc84cad4e74fdc5be4aa2bb55e94397de8e829b Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 11:43:21 +0200
Subject: [PATCH 01/12] Fixed logos

---
 inst/migraph.png     | Bin 0 -> 320852 bytes
 man/figures/logo.png | Bin 191801 -> 160443 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 inst/migraph.png

diff --git a/inst/migraph.png b/inst/migraph.png
new file mode 100644
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From c0902b3ca2d12d81bb42eed859da811d2e4f3a13 Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 14:53:38 +0200
Subject: [PATCH 02/12] run_tute() now finds netrics tutorials

---
 R/tutorial_run.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/R/tutorial_run.R b/R/tutorial_run.R
index 3c1af167..6f6c2193 100644
--- a/R/tutorial_run.R
+++ b/R/tutorial_run.R
@@ -18,7 +18,7 @@
 #' @name tutorials
 NULL
 
-stocnet <- c("manynet", "migraph", "autograph")
+stocnet <- c("manynet", "migraph", "autograph", "netrics")
 
 #' @rdname tutorials 
 #' @export
@@ -37,8 +37,9 @@ run_tute <- function(tute) {
     manynet::snet_info("You can run a tutorial by typing e.g `run_tute('tutorial1')` or `run_tute('Data')` into the console.")
   } else {
     try(learnr::run_tutorial(tute, "manynet"), silent = TRUE)
-    try(learnr::run_tutorial(tute, "migraph"), silent = TRUE)
     try(learnr::run_tutorial(tute, "autograph"), silent = TRUE)
+    try(learnr::run_tutorial(tute, "netrics"), silent = TRUE)
+    try(learnr::run_tutorial(tute, "migraph"), silent = TRUE)
     manynet::snet_info("Didn't find a direct match, so looking for close matches...")
     tutelist <- lapply(manynet::snet_progress_along(avail_pkgs, 
                                                name = "Checking tutorials in stocnet packages"), function(p){

From 996e5b72fd85d1673717a9f16796ac52ba582869 Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 15:22:11 +0200
Subject: [PATCH 03/12] Updated diffusion tutorial with new netrics names

---
 inst/tutorials/tutorial7/diffusion.Rmd  |  38 ++---
 inst/tutorials/tutorial7/diffusion.html | 208 ++++++++++++------------
 2 files changed, 123 insertions(+), 123 deletions(-)

diff --git a/inst/tutorials/tutorial7/diffusion.Rmd b/inst/tutorials/tutorial7/diffusion.Rmd
index b64be7e5..b6a70590 100644
--- a/inst/tutorials/tutorial7/diffusion.Rmd
+++ b/inst/tutorials/tutorial7/diffusion.Rmd
@@ -175,10 +175,10 @@ But there is an easier way.
 Play these same diffusions again, this time nesting the call within `net_infection_complete()`.
 
 ```{r completeinfection, exercise = TRUE, purl = FALSE, fig.width=9}
-net_infection_complete(play_diffusion(create_ring(32, width = 2)))
-net_infection_complete(play_diffusion(generate_random(32, 0.15)))
-net_infection_complete(play_diffusion(generate_scalefree(32, 0.025)))
-net_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))
+net_by_infection_complete(play_diffusion(create_ring(32, width = 2)))
+net_by_infection_complete(play_diffusion(generate_random(32, 0.15)))
+net_by_infection_complete(play_diffusion(generate_scalefree(32, 0.025)))
+net_by_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))
 ```
 
 ```{r struct-interp, echo = FALSE, purl = FALSE}
@@ -203,7 +203,7 @@ You can start the infection in California by specifying `seeds = 5`.
 us_diff <- play_diffusion(irps_usgeo, seeds = 5)
 plot(us_diff)
 graphr(us_diff)
-net_infection_complete(us_diff)
+net_by_infection_complete(us_diff)
 ```
 
 What's happening here? Can you interpret this?
@@ -483,9 +483,9 @@ We could use these on degree centrality, or perhaps some other kind of centralit
 sf <- generate_scalefree(32, 0.025)
 sf %>%
   as_tidygraph() %>%
-  mutate(degree = ifelse(node_is_max(node_degree(sf)) == TRUE, "max",
-                      ifelse(node_is_min(node_degree(sf)) == TRUE, "min", "others"))) %>%
-  graphr(node_color = "degree") + guides(color = "legend") + labs(color = "degree")
+  mutate(degree = ifelse(node_is_max(node_by_degree(sf)) == TRUE, "max",
+                      ifelse(node_is_min(node_by_degree(sf)) == TRUE, "min", "others"))) %>%
+  graphr(node_color = "degree") + guides(color = "legend")
 ```
 
 ```{r scale, exercise = TRUE, purl = FALSE, exercise.setup="sf", fig.width=9}
@@ -495,11 +495,11 @@ sf %>%
 ```{r scale-solution}
 plot(play_diffusion(sf, seeds = 10, steps = 10))
 plot(play_diffusion(sf, seeds = node_is_random(sf), steps = 10))
-plot(play_diffusion(sf, seeds = node_is_max(node_degree(sf)), steps = 10))
-plot(play_diffusion(sf, seeds = node_is_min(node_degree(sf)), steps = 10))
+plot(play_diffusion(sf, seeds = node_is_max(node_by_degree(sf)), steps = 10))
+plot(play_diffusion(sf, seeds = node_is_min(node_by_degree(sf)), steps = 10))
 
 # visualise diffusion in scalefree network
-graphs(play_diffusion(sf, seeds = node_is_min(node_degree(sf)), steps = 10))
+graphs(play_diffusion(sf, seeds = node_is_min(node_by_degree(sf)), steps = 10))
 graphs(play_diffusion(sf, seeds = 16, steps = 10), waves = 1:3)
 ```
 
@@ -719,8 +719,8 @@ We can use `net_reproduction()`.
 rd_diff <- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05)
 plot(rd_diff)
 # R-nought
-net_reproduction(rd_diff)
-net_infection_total(rd_diff)
+net_by_reproduction(rd_diff)
+net_by_infection_total(rd_diff)
 ```
 
 ```{r r0-interp, echo = FALSE, purl = FALSE}
@@ -767,8 +767,8 @@ to be recovered or vaccinated for the network to have herd immunity.
 
 ```{r immunity, exercise = TRUE, exercise.setup = "r0"}
 # Herd Immunity Threshold
-net_immunity(rd_diff)
-net_immunity(rd_diff, normalized = FALSE)
+net_by_immunity(rd_diff)
+net_by_immunity(rd_diff, normalized = FALSE)
 ```
 
 In this model, the HIT score indicates a good proportion of nodes in the network 
@@ -780,7 +780,7 @@ Ok, so let's try this strategy.
 rd_diff_vacc <- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05, 
                                immune = 2:9)
 plot(rd_diff_vacc)
-net_infection_total(rd_diff_vacc)
+net_by_infection_total(rd_diff_vacc)
 ```
 
 We can see that we more rapidly reach a situation in which vaccinated and
@@ -968,12 +968,12 @@ Which are the highest eigenvector centrality nodes in this network?
 ```
 
 ```{r eigen-hint}
-node_eigenvector(ison_networkers)
+node_by_eigenvector(ison_networkers)
 ison_networkers %>% 
-  mutate(who_to_convince = node_is_max(node_eigenvector(ison_networkers))) %>% 
+  mutate(who_to_convince = node_is_max(node_by_eigenvector(ison_networkers))) %>% 
   graphr(node_color = who_to_convince)
 beliefs2 <- rep(0, net_nodes(ison_networkers))
-beliefs2[node_is_max(node_eigenvector(ison_networkers))] <- 1
+beliefs2[node_is_max(node_by_eigenvector(ison_networkers))] <- 1
 ison_networkers %>% mutate(beliefs = beliefs2) %>% graphr(node_color = "beliefs")
 (netlearn2 <- play_learning(ison_networkers, beliefs2))
 plot(netlearn2)
diff --git a/inst/tutorials/tutorial7/diffusion.html b/inst/tutorials/tutorial7/diffusion.html
index 7a9ee72a..8ec06909 100644
--- a/inst/tutorials/tutorial7/diffusion.html
+++ b/inst/tutorials/tutorial7/diffusion.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>Diffusion and Learning</title>
 
@@ -295,10 +295,10 @@ <h3>Varying network structure</h3>
 <div class="tutorial-exercise" data-label="completeinfection"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_infection_complete(play_diffusion(create_ring(32, width = 2)))
-net_infection_complete(play_diffusion(generate_random(32, 0.15)))
-net_infection_complete(play_diffusion(generate_scalefree(32, 0.025)))
-net_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))</code></pre>
+<pre class="text"><code>net_by_infection_complete(play_diffusion(create_ring(32, width = 2)))
+net_by_infection_complete(play_diffusion(generate_random(32, 0.15)))
+net_by_infection_complete(play_diffusion(generate_scalefree(32, 0.025)))
+net_by_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))</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 class="panel panel-default tutorial-question-container">
@@ -326,7 +326,7 @@ <h3>Free play: US States</h3>
 <pre class="text"><code>us_diff &lt;- play_diffusion(irps_usgeo, seeds = 5)
 plot(us_diff)
 graphr(us_diff)
-net_infection_complete(us_diff)</code></pre>
+net_by_infection_complete(us_diff)</code></pre>
 </div>
 <p>What’s happening here? Can you interpret this?</p>
 </div>
@@ -631,9 +631,9 @@ <h3>Choosing where to seed</h3>
 <pre class="text"><code>sf &lt;- generate_scalefree(32, 0.025)
 sf %&gt;%
   as_tidygraph() %&gt;%
-  mutate(degree = ifelse(node_is_max(node_degree(sf)) == TRUE, &quot;max&quot;,
-                      ifelse(node_is_min(node_degree(sf)) == TRUE, &quot;min&quot;, &quot;others&quot;))) %&gt;%
-  graphr(node_color = &quot;degree&quot;) + guides(color = &quot;legend&quot;) + labs(color = &quot;degree&quot;)</code></pre>
+  mutate(degree = ifelse(node_is_max(node_by_degree(sf)) == TRUE, &quot;max&quot;,
+                      ifelse(node_is_min(node_by_degree(sf)) == TRUE, &quot;min&quot;, &quot;others&quot;))) %&gt;%
+  graphr(node_color = &quot;degree&quot;) + guides(color = &quot;legend&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>
 <div class="tutorial-exercise" data-label="scale" data-completion="1"
@@ -646,11 +646,11 @@ <h3>Choosing where to seed</h3>
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>plot(play_diffusion(sf, seeds = 10, steps = 10))
 plot(play_diffusion(sf, seeds = node_is_random(sf), steps = 10))
-plot(play_diffusion(sf, seeds = node_is_max(node_degree(sf)), steps = 10))
-plot(play_diffusion(sf, seeds = node_is_min(node_degree(sf)), steps = 10))
+plot(play_diffusion(sf, seeds = node_is_max(node_by_degree(sf)), steps = 10))
+plot(play_diffusion(sf, seeds = node_is_min(node_by_degree(sf)), steps = 10))
 
 # visualise diffusion in scalefree network
-graphs(play_diffusion(sf, seeds = node_is_min(node_degree(sf)), steps = 10))
+graphs(play_diffusion(sf, seeds = node_is_min(node_by_degree(sf)), steps = 10))
 graphs(play_diffusion(sf, seeds = 16, steps = 10), waves = 1:3)</code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
@@ -891,8 +891,8 @@ <h2>Make it stop</h2>
 <pre class="text"><code>rd_diff &lt;- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05)
 plot(rd_diff)
 # R-nought
-net_reproduction(rd_diff)
-net_infection_total(rd_diff)</code></pre>
+net_by_reproduction(rd_diff)
+net_by_infection_total(rd_diff)</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 class="panel panel-default tutorial-question-container">
@@ -935,8 +935,8 @@ <h3>How many people do we need to vaccinate?</h3>
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
 <pre class="text"><code># Herd Immunity Threshold
-net_immunity(rd_diff)
-net_immunity(rd_diff, normalized = FALSE)</code></pre>
+net_by_immunity(rd_diff)
+net_by_immunity(rd_diff, normalized = FALSE)</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>In this model, the HIT score indicates a good proportion of nodes in
@@ -950,7 +950,7 @@ <h3>How many people do we need to vaccinate?</h3>
 <pre class="text"><code>rd_diff_vacc &lt;- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05, 
                                immune = 2:9)
 plot(rd_diff_vacc)
-net_infection_total(rd_diff_vacc)</code></pre>
+net_by_infection_total(rd_diff_vacc)</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 can see that we more rapidly reach a situation in which vaccinated
@@ -1118,12 +1118,12 @@ <h3>Free play: Networkers</h3>
 <div class="tutorial-exercise-support" data-label="eigen-hint"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>node_eigenvector(ison_networkers)
+<pre class="text"><code>node_by_eigenvector(ison_networkers)
 ison_networkers %&gt;% 
-  mutate(who_to_convince = node_is_max(node_eigenvector(ison_networkers))) %&gt;% 
+  mutate(who_to_convince = node_is_max(node_by_eigenvector(ison_networkers))) %&gt;% 
   graphr(node_color = who_to_convince)
 beliefs2 &lt;- rep(0, net_nodes(ison_networkers))
-beliefs2[node_is_max(node_eigenvector(ison_networkers))] &lt;- 1
+beliefs2[node_is_max(node_by_eigenvector(ison_networkers))] &lt;- 1
 ison_networkers %&gt;% mutate(beliefs = beliefs2) %&gt;% graphr(node_color = &quot;beliefs&quot;)
 (netlearn2 &lt;- play_learning(ison_networkers, beliefs2))
 plot(netlearn2)</code></pre>
@@ -1536,7 +1536,7 @@ <h2>Glossary</h2>
 learnr:::store_exercise_cache(structure(list(label = "completeinfection", global_setup = structure(c("library(learnr)", 
 "library(migraph)", "clear_glossary()", "knitr::opts_chunk$set(echo = FALSE)"
 ), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "completeinfection", code = "net_infection_complete(play_diffusion(create_ring(32, width = 2)))\nnet_infection_complete(play_diffusion(generate_random(32, 0.15)))\nnet_infection_complete(play_diffusion(generate_scalefree(32, 0.025)))\nnet_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))", 
+    chunks = list(list(label = "completeinfection", code = "net_by_infection_complete(play_diffusion(create_ring(32, width = 2)))\nnet_by_infection_complete(play_diffusion(generate_random(32, 0.15)))\nnet_by_infection_complete(play_diffusion(generate_scalefree(32, 0.025)))\nnet_by_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))", 
         opts = list(label = "\"completeinfection\"", exercise = "TRUE", 
             purl = "FALSE", fig.width = "9"), engine = "r")), 
     code_check = NULL, error_check = NULL, check = NULL, solution = NULL, 
@@ -1557,10 +1557,10 @@ <h2>Glossary</h2>
         message = TRUE, render = NULL, ref.label = NULL, child = NULL, 
         engine = "r", split = FALSE, include = TRUE, purl = FALSE, 
         max.print = 1000, label = "completeinfection", exercise = TRUE, 
-        code = c("net_infection_complete(play_diffusion(create_ring(32, width = 2)))", 
-        "net_infection_complete(play_diffusion(generate_random(32, 0.15)))", 
-        "net_infection_complete(play_diffusion(generate_scalefree(32, 0.025)))", 
-        "net_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))"
+        code = c("net_by_infection_complete(play_diffusion(create_ring(32, width = 2)))", 
+        "net_by_infection_complete(play_diffusion(generate_random(32, 0.15)))", 
+        "net_by_infection_complete(play_diffusion(generate_scalefree(32, 0.025)))", 
+        "net_by_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))"
         ), out.width.px = 864, out.height.px = 384, params.src = "completeinfection, exercise = TRUE, purl = FALSE, fig.width=9", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
     engine = "r", version = "4"), class = c("r", "tutorial_exercise"
@@ -1570,11 +1570,11 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "struct-interp", 
     question = structure("Does the structure of the network matter for whether and when a diffusion process completes?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_def7858", 
+    "character")), answers = list(structure(list(id = "lnr_ans_42a6550", 
         option = "No", value = "No", label = structure("No", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = structure("Take a closer look at the <code>net_infection_complete()<\u002fcode> results. Are they all the same?", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_504ffbd", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_1185b8a", 
         option = "Yes", value = "Yes", label = structure("Yes", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("We can see that the different structures have varying outcomes in terms of when the diffusion completes.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
@@ -1586,7 +1586,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "struct-interp-answer", question = "struct-interp"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 1998846406.56921, options = list()), class = c("learnr_radio", 
+    seed = 1163396271.45825, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
 
@@ -1676,19 +1676,19 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "complex-interp", 
     question = structure("For which seed/threshold combinations was there complete infection?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_45f76f4", 
+    "character")), answers = list(structure(list(id = "lnr_ans_70e4b31", 
         option = "seeds = 1 and thresholds = 1", value = "seeds = 1 and thresholds = 1", 
         label = structure("seeds = 1 and thresholds = 1", 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_ae549b7", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_271d67b", 
         option = "seeds = 1 and thresholds = 2", value = "seeds = 1 and thresholds = 2", 
         label = structure("seeds = 1 and thresholds = 2", 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_1272e89", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_cd366bb", 
         option = "seeds = 1:2 and thresholds = 2", value = "seeds = 1:2 and thresholds = 2", 
         label = structure("seeds = 1:2 and thresholds = 2", 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_8dfa405", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_4b9332a", 
         option = "seeds = c(1,16) and thresholds = 2", value = "seeds = c(1,16) and thresholds = 2", 
         label = structure("seeds = c(1,16) and thresholds = 2", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
@@ -1700,7 +1700,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "complex-interp-answer", question = "complex-interp"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 1953604804.09028, options = list()), class = c("learnr_checkbox", 
+    seed = 576057786.231752, options = list()), class = c("learnr_checkbox", 
 "tutorial_question")), session = session)
 </script>
  
@@ -1746,10 +1746,10 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "sfcomplex-interp", 
     question = structure("Here only one seed was used. Does it matter how many seeds are used?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_2fe707", 
+    "character")), answers = list(structure(list(id = "lnr_ans_1af7979", 
         option = "Yes", value = "Yes", label = structure("Yes", 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_6602979", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_cd74026", 
         option = "No", value = "No", label = structure("No", html = TRUE, class = c("html", 
         "character")), correct = TRUE, 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", 
@@ -1760,7 +1760,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "sfcomplex-interp-answer", question = "sfcomplex-interp"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 1402479305.84692, options = list()), class = c("learnr_radio", 
+    seed = 965895754.55022, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
 
@@ -1810,10 +1810,10 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "sfprop-interp", 
     question = structure("Does the threshold proportion matter?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_55b7c16", 
+    "character")), answers = list(structure(list(id = "lnr_ans_654adb5", 
         option = "Yes", value = "Yes", label = structure("Yes", 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_ae87e66", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_f06ad77", 
         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", 
@@ -1824,7 +1824,7 @@ <h2>Glossary</h2>
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         answer = "sfprop-interp-answer", question = "sfprop-interp"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 1180117750.45046, options = list()), class = c("learnr_radio", 
+    seed = 8538250.49602407, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
  
@@ -1998,10 +1998,10 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "ring2-interp", 
     question = structure("Do you see any differences in infection?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_229cffd", 
+    "character")), answers = list(structure(list(id = "lnr_ans_994eb31", 
         option = "Yes", value = "Yes", label = structure("Yes", 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_17f8c68", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_3309070", 
         option = "No", value = "No", label = structure("No", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("It doesn’t matter where the innovation is seeded", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
@@ -2013,7 +2013,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "ring2-interp-answer", question = "ring2-interp"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 459285010.286129, options = list()), class = c("learnr_radio", 
+    seed = 1951538682.09124, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
 
@@ -2104,10 +2104,10 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "ring4-interp", 
     question = structure("Do you see any differences?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_b383ab8", 
+    "character")), answers = list(structure(list(id = "lnr_ans_86fe7d", 
         option = "Yes", value = "Yes", label = structure("Yes", 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_6c0d3e0", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_9be168d", 
         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", 
@@ -2118,7 +2118,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "ring4-interp-answer", question = "ring4-interp"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 332060209.845372, options = list()), class = c("learnr_radio", 
+    seed = 930835666.566546, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
  
@@ -2172,10 +2172,10 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "lattice-interp", 
     question = structure("Do you see any differences?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_3b6ef6d", 
+    "character")), answers = list(structure(list(id = "lnr_ans_bf0e665", 
         option = "Yes", value = "Yes", label = structure("Yes", 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_d90ec5a", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_438975c", 
         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", 
@@ -2186,7 +2186,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "lattice-interp-answer", question = "lattice-interp"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 114161856.946839, options = list()), class = c("learnr_radio", 
+    seed = 1915828702.60787, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
 
@@ -2202,7 +2202,7 @@ <h2>Glossary</h2>
 learnr:::store_exercise_cache(structure(list(label = "sf", global_setup = structure(c("library(learnr)", 
 "library(migraph)", "clear_glossary()", "knitr::opts_chunk$set(echo = FALSE)"
 ), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, 
-    chunks = list(list(label = "sf", code = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\") + labs(color = \"degree\")", 
+    chunks = list(list(label = "sf", code = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_by_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_by_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\")", 
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         engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = NULL, tests = NULL, options = list(
@@ -2222,9 +2222,9 @@ <h2>Glossary</h2>
         message = TRUE, render = NULL, ref.label = NULL, child = NULL, 
         engine = "r", split = FALSE, include = TRUE, purl = TRUE, 
         max.print = 1000, label = "sf", exercise = TRUE, code = c("sf <- generate_scalefree(32, 0.025)", 
-        "sf %>%", "  as_tidygraph() %>%", "  mutate(degree = ifelse(node_is_max(node_degree(sf)) == TRUE, \"max\",", 
-        "                      ifelse(node_is_min(node_degree(sf)) == TRUE, \"min\", \"others\"))) %>%", 
-        "  graphr(node_color = \"degree\") + guides(color = \"legend\") + labs(color = \"degree\")"
+        "sf %>%", "  as_tidygraph() %>%", "  mutate(degree = ifelse(node_is_max(node_by_degree(sf)) == TRUE, \"max\",", 
+        "                      ifelse(node_is_min(node_by_degree(sf)) == TRUE, \"min\", \"others\"))) %>%", 
+        "  graphr(node_color = \"degree\") + guides(color = \"legend\")"
         ), out.width.px = 864, out.height.px = 384, params.src = "sf, exercise=TRUE, fig.width=9", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
     engine = "r", version = "4"), class = c("r", "tutorial_exercise"
@@ -2242,17 +2242,17 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "scale", global_setup = structure(c("library(learnr)", 
 "library(migraph)", "clear_glossary()", "knitr::opts_chunk$set(echo = FALSE)"
-), chunk_opts = list(label = "setup", include = FALSE)), setup = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\") + labs(color = \"degree\")", 
-    chunks = list(list(label = "sf", code = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\") + labs(color = \"degree\")", 
+), chunk_opts = list(label = "setup", include = FALSE)), setup = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_by_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_by_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\")", 
+    chunks = list(list(label = "sf", code = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_by_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_by_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\")", 
         opts = list(label = "\"sf\"", exercise = "TRUE", fig.width = "9"), 
         engine = "r"), list(label = "scale", code = "", opts = list(
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         exercise.setup = "\"sf\"", fig.width = "9"), engine = "r")), 
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     "plot(play_diffusion(sf, seeds = node_is_random(sf), steps = 10))", 
-    "plot(play_diffusion(sf, seeds = node_is_max(node_degree(sf)), steps = 10))", 
-    "plot(play_diffusion(sf, seeds = node_is_min(node_degree(sf)), steps = 10))", 
-    "", "# visualise diffusion in scalefree network", "graphs(play_diffusion(sf, seeds = node_is_min(node_degree(sf)), steps = 10))", 
+    "plot(play_diffusion(sf, seeds = node_is_max(node_by_degree(sf)), steps = 10))", 
+    "plot(play_diffusion(sf, seeds = node_is_min(node_by_degree(sf)), steps = 10))", 
+    "", "# visualise diffusion in scalefree network", "graphs(play_diffusion(sf, seeds = node_is_min(node_by_degree(sf)), steps = 10))", 
     "graphs(play_diffusion(sf, seeds = 16, steps = 10), waves = 1:3)"
     ), chunk_opts = list(label = "scale-solution")), tests = NULL, 
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@@ -2282,22 +2282,22 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "mindeg-interp", 
     question = structure("Which of these four led to the fastest diffusion process?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_d956d7d", 
+    "character")), answers = list(structure(list(id = "lnr_ans_bcdede4", 
         option = "Minimum degree node(s), because there are many more nodes with the minimum degree.", 
         value = "Minimum degree node(s), because there are many more nodes with the minimum degree.", 
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         "character")), correct = TRUE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_b1c60ae", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_b828e2", 
         option = "Node 10 as seed, because it is the most influential.", 
         value = "Node 10 as seed, because it is the most influential.", 
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         "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_2362684", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_9d9c111", 
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         value = "Maximum degree node(s), because these nodes are the most influential.", 
         label = structure("Maximum degree node(s), because these nodes are the most influential.", 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_da2480c", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_2400981", 
         option = "A random node, because this time it selected a good seed.", 
         value = "A random node, because this time it selected a good seed.", 
         label = structure("A random node, because this time it selected a good seed.", html = TRUE, class = c("html", 
@@ -2310,7 +2310,7 @@ <h2>Glossary</h2>
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         answer = "mindeg-interp-answer", question = "mindeg-interp"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 1566157170.2707, options = list()), class = c("learnr_radio", 
+    seed = 382848835.321722, options = list()), class = c("learnr_radio", 
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 </script>
  
@@ -2325,8 +2325,8 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "indepsets", global_setup = structure(c("library(learnr)", 
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-    chunks = list(list(label = "sf", code = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\") + labs(color = \"degree\")", 
+), chunk_opts = list(label = "setup", include = FALSE)), setup = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_by_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_by_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\")", 
+    chunks = list(list(label = "sf", code = "sf <- generate_scalefree(32, 0.025)\nsf %>%\n  as_tidygraph() %>%\n  mutate(degree = ifelse(node_is_max(node_by_degree(sf)) == TRUE, \"max\",\n                      ifelse(node_is_min(node_by_degree(sf)) == TRUE, \"min\", \"others\"))) %>%\n  graphr(node_color = \"degree\") + guides(color = \"legend\")", 
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@@ -2569,10 +2569,10 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "sirs-interp", 
     question = structure("Does the process reach a reasonably stable state?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_b155562", 
+    "character")), answers = list(structure(list(id = "lnr_ans_29730ed", 
         option = "Yes", value = "Yes", label = structure("Yes", 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_d27a752", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_ef29c8d", 
         option = "No", value = "No", label = structure("No", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", 
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@@ -2583,7 +2583,7 @@ <h2>Glossary</h2>
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 </script>
 
@@ -2634,16 +2634,16 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "sirstimes-interp", 
     question = structure("Select the true statements", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_9cd299e", 
+    "character")), answers = list(structure(list(id = "lnr_ans_44b5c55", 
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         label = structure("There are always some infected nodes.", html = TRUE, class = c("html", 
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-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_b4c81b3", 
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@@ -2655,7 +2655,7 @@ <h2>Glossary</h2>
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@@ -2719,7 +2719,7 @@ <h2>Glossary</h2>
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         opts = list(label = "\"diffusions\"", exercise = "TRUE", 
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-        code = "rd_diff <- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05)\nplot(rd_diff)\n# R-nought\nnet_reproduction(rd_diff)\nnet_infection_total(rd_diff)", 
+        code = "rd_diff <- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05)\nplot(rd_diff)\n# R-nought\nnet_by_reproduction(rd_diff)\nnet_by_infection_total(rd_diff)", 
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@@ -2741,8 +2741,8 @@ <h2>Glossary</h2>
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-        "plot(rd_diff)", "# R-nought", "net_reproduction(rd_diff)", 
-        "net_infection_total(rd_diff)"), out.width.px = 864, 
+        "plot(rd_diff)", "# R-nought", "net_by_reproduction(rd_diff)", 
+        "net_by_infection_total(rd_diff)"), out.width.px = 864, 
         out.height.px = 384, params.src = "r0, exercise = TRUE, exercise.setup = \"diffusions\", fig.width=9", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
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@@ -2752,16 +2752,16 @@ <h2>Glossary</h2>
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@@ -2773,7 +2773,7 @@ <h2>Glossary</h2>
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@@ -2804,7 +2804,7 @@ <h2>Glossary</h2>
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+        code = "rd_diff_vacc <- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05, \n                               immune = 2:9)\nplot(rd_diff_vacc)\nnet_by_infection_total(rd_diff_vacc)", 
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@@ -2893,7 +2893,7 @@ <h2>Glossary</h2>
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-        "net_infection_total(rd_diff_vacc)"), out.width.px = 864, 
+        "net_by_infection_total(rd_diff_vacc)"), out.width.px = 864, 
         out.height.px = 384, params.src = "vaccinate, exercise = TRUE, exercise.setup = \"diffusions\", fig.width=9", 
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@@ -2983,10 +2983,10 @@ <h2>Glossary</h2>
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@@ -3006,20 +3006,20 @@ <h2>Glossary</h2>
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         value = "One node never updates their belief beyond their initial belief.", 
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From bb259694cebb10abaa30c9f581af6cdbe73c0804 Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 15:22:24 +0200
Subject: [PATCH 04/12] Updated diversity tutorial with new netrics names

---
 inst/tutorials/tutorial8/diversity.Rmd  |  56 +++---
 inst/tutorials/tutorial8/diversity.html | 246 ++++++++++++------------
 2 files changed, 151 insertions(+), 151 deletions(-)

diff --git a/inst/tutorials/tutorial8/diversity.Rmd b/inst/tutorials/tutorial8/diversity.Rmd
index d15e1ed2..4fabadfe 100644
--- a/inst/tutorials/tutorial8/diversity.Rmd
+++ b/inst/tutorials/tutorial8/diversity.Rmd
@@ -173,15 +173,15 @@ net_node_attributes(marvel_friends)
 ```
 
 ```{r rich-hint, purl = FALSE}
-net_richness(____, ____)
+net_by_richness(____, ____)
 ```
 
 ```{r rich-solution}
-net_richness(marvel_friends, "Gender")
-net_richness(marvel_friends, "PowerOrigin")
-net_richness(marvel_friends, "Attractive")
-net_richness(marvel_friends, "Rich")
-net_richness(marvel_friends, "Intellect")
+net_by_richness(marvel_friends, "Gender")
+net_by_richness(marvel_friends, "PowerOrigin")
+net_by_richness(marvel_friends, "Attractive")
+net_by_richness(marvel_friends, "Rich")
+net_by_richness(marvel_friends, "Intellect")
 ```
 
 ```{r richness-question, echo=FALSE, purl = FALSE}
@@ -226,15 +226,15 @@ Obtain the network diversity scores for the five attributes used above.
 ```
 
 ```{r blau-hint, purl = FALSE}
-net_diversity(____, ____)
+net_by_diversity(____, ____)
 ```
 
 ```{r blau-solution}
-net_diversity(marvel_friends, "Gender")
-net_diversity(marvel_friends, "PowerOrigin")
-net_diversity(marvel_friends, "Attractive")
-net_diversity(marvel_friends, "Rich")
-net_diversity(marvel_friends, "Intellect")
+net_by_diversity(marvel_friends, "Gender")
+net_by_diversity(marvel_friends, "PowerOrigin")
+net_by_diversity(marvel_friends, "Attractive")
+net_by_diversity(marvel_friends, "Rich")
+net_by_diversity(marvel_friends, "Intellect")
 ```
 
 Looks like there is more diversity in terms of where these characters got
@@ -255,9 +255,9 @@ over_membership(.data, FUN, ..., membership)
 ```
 
 ```{r crossref-solution}
-over_membership(marvel_friends, net_diversity, "Gender", 
+over_membership(marvel_friends, net_by_diversity, "Gender", 
                 membership = "PowerOrigin")
-over_membership(marvel_friends, net_diversity, "Intellect", 
+over_membership(marvel_friends, net_by_diversity, "Intellect", 
                 membership = "Gender")
 ```
 
@@ -296,13 +296,13 @@ Please assign the results so that we can use them later.
 ```
 
 ```{r ei-hint, purl = FALSE}
-net_heterophily(____, ____)
+net_by_heterophily(____, ____)
 ```
 
 ```{r ei-solution}
-(obs.gender <- net_heterophily(marvel_friends, "Gender"))
-(obs.powers <- net_heterophily(marvel_friends, "PowerOrigin")) 
-(obs.attract <- net_heterophily(marvel_friends, "Attractive")) 
+(obs.gender <- net_by_heterophily(marvel_friends, "Gender"))
+(obs.powers <- net_by_heterophily(marvel_friends, "PowerOrigin")) 
+(obs.attract <- net_by_heterophily(marvel_friends, "Attractive")) 
 ```
 
 ```{r homophily-present, echo=FALSE, purl = FALSE}
@@ -354,13 +354,13 @@ plot(rand.____)
 
 ```{r rando-hint-3, purl = FALSE}
 rand.gender <- test_random(marvel_friends, 
-                           net_heterophily, attribute = "Gender", 
+                           net_by_heterophily, attribute = "Gender", 
                            times = 1000)
 rand.power <- test_random(marvel_friends, 
-                          net_heterophily, attribute = "PowerOrigin", 
+                          net_by_heterophily, attribute = "PowerOrigin", 
                           times = 1000)
 rand.attract <- test_random(marvel_friends, 
-                            net_heterophily, attribute = "Attractive", 
+                            net_by_heterophily, attribute = "Attractive", 
                             times = 1000)
 plot(rand.gender) + ggtitle("CUG test results for 'Gender' attribute")
 plot(rand.power) + ggtitle("CUG test results for 'PowerOrigin' attribute")
@@ -369,13 +369,13 @@ plot(rand.attract) + ggtitle("CUG test results for 'Attractive' attribute")
 
 ```{r rando-solution}
 rand.gender <- test_random(marvel_friends, 
-                           net_heterophily, attribute = "Gender", 
+                           net_by_heterophily, attribute = "Gender", 
                            times = 1000)
 rand.power <- test_random(marvel_friends, 
-                          net_heterophily, attribute = "PowerOrigin", 
+                          net_by_heterophily, attribute = "PowerOrigin", 
                           times = 1000)
 rand.attract <- test_random(marvel_friends, 
-                            net_heterophily, attribute = "Attractive", 
+                            net_by_heterophily, attribute = "Attractive", 
                             times = 1000)
 plot(rand.gender) + ggtitle("CUG test results for 'Gender' attribute")
 plot(rand.power) + ggtitle("CUG test results for 'PowerOrigin' attribute")
@@ -438,7 +438,7 @@ Let's first plot the observed data and some permuted data next to each other.
 ```
 
 ```{r perm-hint, purl = FALSE}
-graphr(generate_permutation(____, with_attr = TRUE), ____)
+graphr(to_permuted(____, with_attr = TRUE), ____)
 ```
 
 ```{r perm-solution}
@@ -446,7 +446,7 @@ old <- graphr(marvel_friends,
               labels = FALSE, node_size = 6,
               node_color = "PowerOrigin",
               node_shape = "Gender") + ggtitle("Original network")
-new <- graphr(generate_permutation(marvel_friends, with_attr = TRUE),
+new <- graphr(to_permuted(marvel_friends, with_attr = TRUE),
               labels = FALSE, node_size = 6,
               node_color = "PowerOrigin",
               node_shape = "Gender") + ggtitle("Permuted network")
@@ -487,10 +487,10 @@ test_permutation(____, FUN = ____, attribute = ____,
 
 ```{r testperm-solution}
 (perm.gender <- test_permutation(marvel_friends, 
-                                net_heterophily, attribute = "Gender",
+                                net_by_heterophily, attribute = "Gender",
                                 times = 1000))
 (perm.power <- test_permutation(marvel_friends, 
-                                net_heterophily, attribute = "PowerOrigin",
+                                net_by_heterophily, attribute = "PowerOrigin",
                                 times = 1000))
 
 (plot(rand.gender) + ggtitle("CUG test results for 'Gender' attribute") + 
diff --git a/inst/tutorials/tutorial8/diversity.html b/inst/tutorials/tutorial8/diversity.html
index 9dbe332e..63db1c3b 100644
--- a/inst/tutorials/tutorial8/diversity.html
+++ b/inst/tutorials/tutorial8/diversity.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>Diversity and Regression</title>
 
@@ -137,11 +137,11 @@ <h2>Initial visualisation</h2>
 <p>For this session, we’ll explore a couple of different datasets.
 First, let’s examine
 homogeneity/<abbr><dfn title='A measure of how varied the nodes in a network are.'>
-heterogeneity </dfn></abbr> in the Marvel relationships dataset from
-<code>{manynet}</code>, <code>ison_marvel_relationships</code>. The
-dataset is quite complicated, so to make this simpler, let’s concentrate
-on:</p>
+heterogeneity </dfn></abbr> in the Marvel dataset from
+<code>{manynet}</code>, <code>fict_marvel</code>. The dataset is quite
+complicated, so to make this simpler, let’s concentrate on:</p>
 <ul>
+<li>just the relationships part of the network</li>
 <li>just the positive (friendship) ties and not the negative (enmity)
 ties</li>
 <li>the main (giant) component without any isolates</li>
@@ -159,27 +159,26 @@ <h2>Initial visualisation</h2>
 <div class="tutorial-exercise-support" data-label="friends-hint-1"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
+<pre class="text"><code>to_uniplex(____, &quot;relationship&quot;)</code></pre>
+</div>
+<div class="tutorial-exercise-support" data-label="friends-hint-2"
+data-completion="1" data-diagnostics="1" data-startover="1"
+data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code># since the dataset is a &#39;signed&#39; graph, we want to get just the
 # positively signed ties to get the friendship graph 
 # (and lose the enmity relations)
 to_unsigned(____, keep = &quot;positive&quot;)</code></pre>
 </div>
-<div class="tutorial-exercise-support" data-label="friends-hint-2"
+<div class="tutorial-exercise-support" data-label="friends-hint-3"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code># to_giant() is a quick easy way to get the giant/main component
 to_giant(____)</code></pre>
 </div>
-<div class="tutorial-exercise-support" data-label="friends-hint-3"
-data-completion="1" data-diagnostics="1" data-startover="1"
-data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>to_subgraph(____, Appearances &gt;= mean(Appearances))</code></pre>
-</div>
 <div class="tutorial-exercise-support" data-label="friends-hint-4"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code># don&#39;t forget to assign the results!
-marvel_friends &lt;- ____</code></pre>
+<pre class="text"><code>to_subgraph(____, Appearances &gt;= mean(Appearances))</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="friends-hint-5"
 data-completion="1" data-diagnostics="1" data-startover="1"
@@ -256,16 +255,16 @@ <h2>Measuring richness</h2>
 <div class="tutorial-exercise-support" data-label="rich-hint"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_richness(____, ____)</code></pre>
+<pre class="text"><code>net_by_richness(____, ____)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="rich-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_richness(marvel_friends, &quot;Gender&quot;)
-net_richness(marvel_friends, &quot;PowerOrigin&quot;)
-net_richness(marvel_friends, &quot;Attractive&quot;)
-net_richness(marvel_friends, &quot;Rich&quot;)
-net_richness(marvel_friends, &quot;Intellect&quot;)</code></pre>
+<pre class="text"><code>net_by_richness(marvel_friends, &quot;Gender&quot;)
+net_by_richness(marvel_friends, &quot;PowerOrigin&quot;)
+net_by_richness(marvel_friends, &quot;Attractive&quot;)
+net_by_richness(marvel_friends, &quot;Rich&quot;)
+net_by_richness(marvel_friends, &quot;Intellect&quot;)</code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
 <div data-label="richness-question" class="tutorial-question panel-body">
@@ -306,16 +305,16 @@ <h2>Measuring diversity</h2>
 <div class="tutorial-exercise-support" data-label="blau-hint"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_diversity(____, ____)</code></pre>
+<pre class="text"><code>net_by_diversity(____, ____)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="blau-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_diversity(marvel_friends, &quot;Gender&quot;)
-net_diversity(marvel_friends, &quot;PowerOrigin&quot;)
-net_diversity(marvel_friends, &quot;Attractive&quot;)
-net_diversity(marvel_friends, &quot;Rich&quot;)
-net_diversity(marvel_friends, &quot;Intellect&quot;)</code></pre>
+<pre class="text"><code>net_by_diversity(marvel_friends, &quot;Gender&quot;)
+net_by_diversity(marvel_friends, &quot;PowerOrigin&quot;)
+net_by_diversity(marvel_friends, &quot;Attractive&quot;)
+net_by_diversity(marvel_friends, &quot;Rich&quot;)
+net_by_diversity(marvel_friends, &quot;Intellect&quot;)</code></pre>
 </div>
 <p>Looks like there is more diversity in terms of where these characters
 got their powers, whether they have significant intellectual powers, and
@@ -338,9 +337,9 @@ <h2>Measuring diversity</h2>
 <div class="tutorial-exercise-support" data-label="crossref-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>over_membership(marvel_friends, net_diversity, &quot;Gender&quot;, 
+<pre class="text"><code>over_membership(marvel_friends, net_by_diversity, &quot;Gender&quot;, 
                 membership = &quot;PowerOrigin&quot;)
-over_membership(marvel_friends, net_diversity, &quot;Intellect&quot;, 
+over_membership(marvel_friends, net_by_diversity, &quot;Intellect&quot;, 
                 membership = &quot;Gender&quot;)</code></pre>
 </div>
 <p>Note that the length of the vector returned as a result is the number
@@ -392,14 +391,14 @@ <h2>Measuring heterophily</h2>
 <div class="tutorial-exercise-support" data-label="ei-hint"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>net_heterophily(____, ____)</code></pre>
+<pre class="text"><code>net_by_heterophily(____, ____)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="ei-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>(obs.gender &lt;- net_heterophily(marvel_friends, &quot;Gender&quot;))
-(obs.powers &lt;- net_heterophily(marvel_friends, &quot;PowerOrigin&quot;)) 
-(obs.attract &lt;- net_heterophily(marvel_friends, &quot;Attractive&quot;)) </code></pre>
+<pre class="text"><code>(obs.gender &lt;- net_by_heterophily(marvel_friends, &quot;Gender&quot;))
+(obs.powers &lt;- net_by_heterophily(marvel_friends, &quot;PowerOrigin&quot;)) 
+(obs.attract &lt;- net_by_heterophily(marvel_friends, &quot;Attractive&quot;)) </code></pre>
 </div>
 <div class="panel panel-default tutorial-question-container">
 <div data-label="homophily-present" class="tutorial-question panel-body">
@@ -451,13 +450,13 @@ <h2>CUG tests</h2>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>rand.gender &lt;- test_random(marvel_friends, 
-                           net_heterophily, attribute = &quot;Gender&quot;, 
+                           net_by_heterophily, attribute = &quot;Gender&quot;, 
                            times = 1000)
 rand.power &lt;- test_random(marvel_friends, 
-                          net_heterophily, attribute = &quot;PowerOrigin&quot;, 
+                          net_by_heterophily, attribute = &quot;PowerOrigin&quot;, 
                           times = 1000)
 rand.attract &lt;- test_random(marvel_friends, 
-                            net_heterophily, attribute = &quot;Attractive&quot;, 
+                            net_by_heterophily, attribute = &quot;Attractive&quot;, 
                             times = 1000)
 plot(rand.gender) + ggtitle(&quot;CUG test results for &#39;Gender&#39; attribute&quot;)
 plot(rand.power) + ggtitle(&quot;CUG test results for &#39;PowerOrigin&#39; attribute&quot;)
@@ -520,7 +519,7 @@ <h2>QAP tests</h2>
 <div class="tutorial-exercise-support" data-label="perm-hint"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>graphr(generate_permutation(____, with_attr = TRUE), ____)</code></pre>
+<pre class="text"><code>graphr(to_permuted(____, with_attr = TRUE), ____)</code></pre>
 </div>
 <div class="tutorial-exercise-support" data-label="perm-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
@@ -529,7 +528,7 @@ <h2>QAP tests</h2>
               labels = FALSE, node_size = 6,
               node_color = &quot;PowerOrigin&quot;,
               node_shape = &quot;Gender&quot;) + ggtitle(&quot;Original network&quot;)
-new &lt;- graphr(generate_permutation(marvel_friends, with_attr = TRUE),
+new &lt;- graphr(to_permuted(marvel_friends, with_attr = TRUE),
               labels = FALSE, node_size = 6,
               node_color = &quot;PowerOrigin&quot;,
               node_shape = &quot;Gender&quot;) + ggtitle(&quot;Permuted network&quot;)
@@ -564,10 +563,10 @@ <h2>QAP tests</h2>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>(perm.gender &lt;- test_permutation(marvel_friends, 
-                                net_heterophily, attribute = &quot;Gender&quot;,
+                                net_by_heterophily, attribute = &quot;Gender&quot;,
                                 times = 1000))
 (perm.power &lt;- test_permutation(marvel_friends, 
-                                net_heterophily, attribute = &quot;PowerOrigin&quot;,
+                                net_by_heterophily, attribute = &quot;PowerOrigin&quot;,
                                 times = 1000))
 
 (plot(rand.gender) + ggtitle(&quot;CUG test results for &#39;Gender&#39; attribute&quot;) + 
@@ -688,7 +687,8 @@ <h2>Network regression</h2>
 <div class="tutorial-exercise-support" data-label="qapinterp-solution"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>tidy(model1)
+<pre class="text"><code>model1
+tidy(model1)
 glance(model1)
 plot(model1)</code></pre>
 </div>
@@ -854,7 +854,7 @@ <h2>Glossary</h2>
                                       "Bravo!",
                                       "Super!"),
                            encouragement = c("Bon effort"))
-marvel_friends <- to_unsigned(ison_marvel_relationships, keep = "positive")
+marvel_friends <- to_unsigned(to_uniplex(fict_marvel, "relationship"), keep = "positive")
 marvel_friends <- to_giant(marvel_friends)
 marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))
 </script>
@@ -909,7 +909,7 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "friends", 
@@ -971,7 +971,7 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "plotfriends", 
@@ -1033,16 +1033,16 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "rich", 
     code = "net_node_attributes(marvel_friends)", opts = list(
         label = "\"rich\"", exercise = "TRUE", purl = "FALSE"), 
     engine = "r")), code_check = NULL, error_check = NULL, check = NULL, 
-    solution = structure(c("net_richness(marvel_friends, \"Gender\")", 
-    "net_richness(marvel_friends, \"PowerOrigin\")", "net_richness(marvel_friends, \"Attractive\")", 
-    "net_richness(marvel_friends, \"Rich\")", "net_richness(marvel_friends, \"Intellect\")"
+    solution = structure(c("net_by_richness(marvel_friends, \"Gender\")", 
+    "net_by_richness(marvel_friends, \"PowerOrigin\")", "net_by_richness(marvel_friends, \"Attractive\")", 
+    "net_by_richness(marvel_friends, \"Rich\")", "net_by_richness(marvel_friends, \"Intellect\")"
     ), chunk_opts = list(label = "rich-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -1070,20 +1070,20 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "richness-question", 
     question = structure("Which variable is the most ‘diverse’ according to this richness measure?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_469aa68", 
+    "character")), answers = list(structure(list(id = "lnr_ans_969f2ce", 
         option = "Gender", value = "Gender", label = structure("Gender", 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_9174fb2", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_5cd7bb8", 
         option = "PowerOrigin", value = "PowerOrigin", label = structure("PowerOrigin", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("There are four categories available in this data for power origin, while the other variables include only two categories each.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_d7e4f08", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_a9fae42", 
         option = "Attractive", value = "Attractive", label = structure("Attractive", 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_8f601fb", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_c2aa83b", 
         option = "Rich", value = "Rich", label = structure("Rich", 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_d3cadce", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_f9fc387", 
         option = "Intellect", value = "Intellect", label = structure("Intellect", 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", 
@@ -1094,7 +1094,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "richness-question-answer", question = "richness-question"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 1227695456.42831, options = list()), class = c("learnr_radio", 
+    seed = 1963900272.58549, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
 
@@ -1128,15 +1128,15 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "blau", 
     code = "", opts = list(label = "\"blau\"", exercise = "TRUE", 
         purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
-    check = NULL, solution = structure(c("net_diversity(marvel_friends, \"Gender\")", 
-    "net_diversity(marvel_friends, \"PowerOrigin\")", "net_diversity(marvel_friends, \"Attractive\")", 
-    "net_diversity(marvel_friends, \"Rich\")", "net_diversity(marvel_friends, \"Intellect\")"
+    check = NULL, solution = structure(c("net_by_diversity(marvel_friends, \"Gender\")", 
+    "net_by_diversity(marvel_friends, \"PowerOrigin\")", "net_by_diversity(marvel_friends, \"Attractive\")", 
+    "net_by_diversity(marvel_friends, \"Rich\")", "net_by_diversity(marvel_friends, \"Intellect\")"
     ), chunk_opts = list(label = "blau-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -1191,14 +1191,14 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "crossref", 
     code = "", opts = list(label = "\"crossref\"", exercise = "TRUE", 
         purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
-    check = NULL, solution = structure(c("over_membership(marvel_friends, net_diversity, \"Gender\", ", 
-    "                membership = \"PowerOrigin\")", "over_membership(marvel_friends, net_diversity, \"Intellect\", ", 
+    check = NULL, solution = structure(c("over_membership(marvel_friends, net_by_diversity, \"Gender\", ", 
+    "                membership = \"PowerOrigin\")", "over_membership(marvel_friends, net_by_diversity, \"Intellect\", ", 
     "                membership = \"Gender\")"), chunk_opts = list(
         label = "crossref-solution")), tests = NULL, options = list(
         eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, 
@@ -1253,15 +1253,15 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "ei", 
     code = "", opts = list(label = "\"ei\"", exercise = "TRUE", 
         purl = "FALSE"), engine = "r")), code_check = NULL, error_check = NULL, 
-    check = NULL, solution = structure(c("(obs.gender <- net_heterophily(marvel_friends, \"Gender\"))", 
-    "(obs.powers <- net_heterophily(marvel_friends, \"PowerOrigin\")) ", 
-    "(obs.attract <- net_heterophily(marvel_friends, \"Attractive\")) "
+    check = NULL, solution = structure(c("(obs.gender <- net_by_heterophily(marvel_friends, \"Gender\"))", 
+    "(obs.powers <- net_by_heterophily(marvel_friends, \"PowerOrigin\")) ", 
+    "(obs.attract <- net_by_heterophily(marvel_friends, \"Attractive\")) "
     ), chunk_opts = list(label = "ei-solution")), tests = NULL, 
     options = list(eval = FALSE, echo = TRUE, results = "markup", 
         tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, 
@@ -1290,15 +1290,15 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "homophily-present", 
     question = structure("For which variables is there a sign of some homophily effect according to the EI index? (Choose all that apply)", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_2ba801d", 
+    "character")), answers = list(structure(list(id = "lnr_ans_73b08cc", 
         option = "Gender", value = "Gender", label = structure("Gender", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("Yes, looks like there might be some gender homophily present.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_d51ea35", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_e72cbb3", 
         option = "PowerOrigin", value = "PowerOrigin", label = structure("PowerOrigin", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = structure("The score for power origin homophily is so close to 0 that it does not seem to signal much.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_e826df4", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_64662ea", 
         option = "Attractive", value = "Attractive", label = structure("Attractive", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("And looks like a fairly large effect for homophily on the basis of looks…", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
@@ -1310,7 +1310,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "homophily-present-answer", question = "homophily-present"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 2139222341.50385, options = list()), class = c("learnr_checkbox", 
+    seed = 1332730199.8794, options = list()), class = c("learnr_checkbox", 
 "tutorial_question")), session = session)
 </script>
  
@@ -1344,7 +1344,7 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "rando", 
@@ -1352,11 +1352,11 @@ <h2>Glossary</h2>
         purl = "FALSE", exercise.timelimit = "120", fig.width = "9"), 
     engine = "r")), code_check = NULL, error_check = NULL, check = NULL, 
     solution = structure(c("rand.gender <- test_random(marvel_friends, ", 
-    "                           net_heterophily, attribute = \"Gender\", ", 
+    "                           net_by_heterophily, attribute = \"Gender\", ", 
     "                           times = 1000)", "rand.power <- test_random(marvel_friends, ", 
-    "                          net_heterophily, attribute = \"PowerOrigin\", ", 
+    "                          net_by_heterophily, attribute = \"PowerOrigin\", ", 
     "                          times = 1000)", "rand.attract <- test_random(marvel_friends, ", 
-    "                            net_heterophily, attribute = \"Attractive\", ", 
+    "                            net_by_heterophily, attribute = \"Attractive\", ", 
     "                            times = 1000)", "plot(rand.gender) + ggtitle(\"CUG test results for 'Gender' attribute\")", 
     "plot(rand.power) + ggtitle(\"CUG test results for 'PowerOrigin' attribute\")", 
     "plot(rand.attract) + ggtitle(\"CUG test results for 'Attractive' attribute\")"
@@ -1387,25 +1387,25 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "cuginterp-qn", 
     question = structure("Which of the following is true?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_63434c6", 
+    "character")), answers = list(structure(list(id = "lnr_ans_7171350", 
         option = "Attractive superheros only hang out with other attractive superheros.", 
         value = "Attractive superheros only hang out with other attractive superheros.", 
         label = structure("Attractive superheros only hang out with other attractive superheros.", html = TRUE, class = c("html", 
-        "character")), correct = FALSE, message = structure("Don’t give up now, try it one more time. <img src=\"https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExeW9jbXg5dDBndjRpcWc2enhyOTZvbjloeGFpbmJndXNrMm05MHdtZiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/ziLadIVnOGCKk/giphy.gif\" alt=\"gif of thor frustration\"/>", html = TRUE, class = c("html", 
+        "character")), correct = FALSE, message = structure("Please try again. <img src=\"https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExeW9jbXg5dDBndjRpcWc2enhyOTZvbjloeGFpbmJndXNrMm05MHdtZiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/ziLadIVnOGCKk/giphy.gif\" alt=\"gif of thor frustration\"/>", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_93c40f3", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_5e40db8", 
         option = "There is significantly less power origin heterophily than expected.", 
         value = "There is significantly less power origin heterophily than expected.", 
         label = structure("There is significantly less power origin heterophily than expected.", html = TRUE, class = c("html", 
-        "character")), correct = TRUE, message = structure("Out of this world!", 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")), structure(list(id = "lnr_ans_e8b0308", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_fdd5cfd", 
         option = "There is no evidence of gender homophily.", 
         value = "There is no evidence of gender homophily.", 
         label = structure("There is no evidence of gender homophily.", html = TRUE, class = c("html", 
-        "character")), correct = TRUE, message = structure("What first-rate work!", html = TRUE, class = c("html", 
+        "character")), correct = TRUE, message = structure("Spectacular job!", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_ccf507e", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_f961356", 
         option = "There is significant power origin homophily.", 
         value = "There is significant power origin homophily.", 
         label = structure("There is significant power origin homophily.", html = TRUE, class = c("html", 
@@ -1419,7 +1419,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "cuginterp-qn-answer", question = "cuginterp-qn"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 1565501347.27101, options = list()), class = c("learnr_checkbox", 
+    seed = 629599135.70682, options = list()), class = c("learnr_checkbox", 
 "tutorial_question")), session = session)
 </script>
 
@@ -1453,7 +1453,7 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "perm", 
@@ -1462,7 +1462,7 @@ <h2>Glossary</h2>
     error_check = NULL, check = NULL, solution = structure(c("old <- graphr(marvel_friends,", 
     "              labels = FALSE, node_size = 6,", "              node_color = \"PowerOrigin\",", 
     "              node_shape = \"Gender\") + ggtitle(\"Original network\")", 
-    "new <- graphr(generate_permutation(marvel_friends, with_attr = TRUE),", 
+    "new <- graphr(to_permuted(marvel_friends, with_attr = TRUE),", 
     "              labels = FALSE, node_size = 6,", "              node_color = \"PowerOrigin\",", 
     "              node_shape = \"Gender\") + ggtitle(\"Permuted network\")", 
     "old + new"), chunk_opts = list(label = "perm-solution")), 
@@ -1493,25 +1493,25 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "cupqap-qn", 
     question = structure("Which of the following is true?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_7510d0f", 
+    "character")), answers = list(structure(list(id = "lnr_ans_b311340", 
         option = "Random networks retain the structure of the original network.", 
         value = "Random networks retain the structure of the original network.", 
         label = structure("Random networks retain the structure of the original network.", html = TRUE, class = c("html", 
-        "character")), correct = FALSE, message = structure("Try it again. Perseverence is the key to success. <img src=\"https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExeW9jbXg5dDBndjRpcWc2enhyOTZvbjloeGFpbmJndXNrMm05MHdtZiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/ziLadIVnOGCKk/giphy.gif\" alt=\"gif of thor frustration\"/>", html = TRUE, class = c("html", 
+        "character")), correct = FALSE, message = structure("Give it another try. <img src=\"https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExeW9jbXg5dDBndjRpcWc2enhyOTZvbjloeGFpbmJndXNrMm05MHdtZiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/ziLadIVnOGCKk/giphy.gif\" alt=\"gif of thor frustration\"/>", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_1c9c31b", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_d8971e1", 
         option = "Permuted networks retain the structure of the original network.", 
         value = "Permuted networks retain the structure of the original network.", 
         label = structure("Permuted networks retain the structure of the original network.", html = TRUE, class = c("html", 
-        "character")), correct = TRUE, message = structure("Absolutely fabulous!", html = TRUE, class = c("html", 
+        "character")), correct = TRUE, message = structure("Beautiful!", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_cc63c0", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_ab79add", 
         option = "Both random and permuted networks retain the proportion of attributes from the original network.", 
         value = "Both random and permuted networks retain the proportion of attributes from the original network.", 
         label = structure("Both random and permuted networks retain the proportion of attributes from the original network.", html = TRUE, class = c("html", 
-        "character")), correct = TRUE, message = structure("Smashing!", html = TRUE, class = c("html", 
+        "character")), correct = TRUE, message = structure("Resplendent!", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_5a53c96", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_ce0b308", 
         option = "Permuted networks retain the ties among the same nodes from the original network.", 
         value = "Permuted networks retain the ties among the same nodes from the original network.", 
         label = structure("Permuted networks retain the ties among the same nodes from the original network.", html = TRUE, class = c("html", 
@@ -1525,7 +1525,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "cupqap-qn-answer", question = "cupqap-qn"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 697811175.175056, options = list()), class = c("learnr_checkbox", 
+    seed = 646220648.19908, options = list()), class = c("learnr_checkbox", 
 "tutorial_question")), session = session)
 </script>
  
@@ -1559,20 +1559,20 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "rand.gender <- test_random(marvel_friends, \n                           net_heterophily, attribute = \"Gender\", \n                           times = 1000)\nrand.power <- test_random(marvel_friends, \n                          net_heterophily, attribute = \"PowerOrigin\", \n                          times = 1000)\nrand.attract <- test_random(marvel_friends, \n                            net_heterophily, attribute = \"Attractive\", \n                            times = 1000)\nplot(rand.gender) + ggtitle(\"CUG test results for 'Gender' attribute\")\nplot(rand.power) + ggtitle(\"CUG test results for 'PowerOrigin' attribute\")\nplot(rand.attract) + ggtitle(\"CUG test results for 'Attractive' attribute\")", 
-    chunks = list(list(label = "rando-solution", code = "rand.gender <- test_random(marvel_friends, \n                           net_heterophily, attribute = \"Gender\", \n                           times = 1000)\nrand.power <- test_random(marvel_friends, \n                          net_heterophily, attribute = \"PowerOrigin\", \n                          times = 1000)\nrand.attract <- test_random(marvel_friends, \n                            net_heterophily, attribute = \"Attractive\", \n                            times = 1000)\nplot(rand.gender) + ggtitle(\"CUG test results for 'Gender' attribute\")\nplot(rand.power) + ggtitle(\"CUG test results for 'PowerOrigin' attribute\")\nplot(rand.attract) + ggtitle(\"CUG test results for 'Attractive' attribute\")", 
+    eval = TRUE)), setup = "rand.gender <- test_random(marvel_friends, \n                           net_by_heterophily, attribute = \"Gender\", \n                           times = 1000)\nrand.power <- test_random(marvel_friends, \n                          net_by_heterophily, attribute = \"PowerOrigin\", \n                          times = 1000)\nrand.attract <- test_random(marvel_friends, \n                            net_by_heterophily, attribute = \"Attractive\", \n                            times = 1000)\nplot(rand.gender) + ggtitle(\"CUG test results for 'Gender' attribute\")\nplot(rand.power) + ggtitle(\"CUG test results for 'PowerOrigin' attribute\")\nplot(rand.attract) + ggtitle(\"CUG test results for 'Attractive' attribute\")", 
+    chunks = list(list(label = "rando-solution", code = "rand.gender <- test_random(marvel_friends, \n                           net_by_heterophily, attribute = \"Gender\", \n                           times = 1000)\nrand.power <- test_random(marvel_friends, \n                          net_by_heterophily, attribute = \"PowerOrigin\", \n                          times = 1000)\nrand.attract <- test_random(marvel_friends, \n                            net_by_heterophily, attribute = \"Attractive\", \n                            times = 1000)\nplot(rand.gender) + ggtitle(\"CUG test results for 'Gender' attribute\")\nplot(rand.power) + ggtitle(\"CUG test results for 'PowerOrigin' attribute\")\nplot(rand.attract) + ggtitle(\"CUG test results for 'Attractive' attribute\")", 
         opts = list(label = "\"rando-solution\""), engine = "r"), 
         list(label = "testperm", code = "", opts = list(label = "\"testperm\"", 
             exercise = "TRUE", exercise.setup = "\"rando-solution\"", 
             purl = "FALSE", exercise.timelimit = "120", fig.width = "9"), 
             engine = "r")), code_check = NULL, error_check = NULL, 
     check = NULL, solution = structure(c("(perm.gender <- test_permutation(marvel_friends, ", 
-    "                                net_heterophily, attribute = \"Gender\",", 
+    "                                net_by_heterophily, attribute = \"Gender\",", 
     "                                times = 1000))", "(perm.power <- test_permutation(marvel_friends, ", 
-    "                                net_heterophily, attribute = \"PowerOrigin\",", 
+    "                                net_by_heterophily, attribute = \"PowerOrigin\",", 
     "                                times = 1000))", "", "(plot(rand.gender) + ggtitle(\"CUG test results for 'Gender' attribute\") + ", 
     "    theme(plot.title = element_text(size=8)) | ", "    plot(rand.power) + ggtitle(\"CUG test results for 'PowerOrigin' attribute\") + ", 
     "    theme(plot.title = element_text(size=8))) /", "(plot(perm.gender) + ggtitle(\"QAP test results for 'Gender' attribute\") + ", 
@@ -1605,25 +1605,25 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "cupqap-results", 
     question = structure("What can we say from these results?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_28ec989", 
+    "character")), answers = list(structure(list(id = "lnr_ans_1861932", 
         option = "We can reject the null hypothesis that there is no homophily for gender in the comparison with random networks", 
         value = "We can reject the null hypothesis that there is no homophily for gender in the comparison with random networks", 
         label = structure("We can reject the null hypothesis that there is no homophily for gender in the comparison with random networks", html = TRUE, class = c("html", 
         "character")), correct = FALSE, message = structure("We cannot reject the null hypothesis since the result is in the range of expected scores across randomised networks, indicated by the red line between the grey shaded zones.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_e8ba458", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_9cadb53", 
         option = "We can reject the null hypothesis that there is no homophily for power origin in the comparison with random networks", 
         value = "We can reject the null hypothesis that there is no homophily for power origin in the comparison with random networks", 
         label = structure("We can reject the null hypothesis that there is no homophily for power origin in the comparison with random networks", html = TRUE, class = c("html", 
-        "character")), correct = TRUE, message = structure("Bravo!", html = TRUE, class = c("html", 
+        "character")), correct = TRUE, message = structure("You rock!", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_a2d1373", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_4220e67", 
         option = "We can reject the null hypothesis that there is no homophily for power origin in both CUG and QAP tests", 
         value = "We can reject the null hypothesis that there is no homophily for power origin in both CUG and QAP tests", 
         label = structure("We can reject the null hypothesis that there is no homophily for power origin in both CUG and QAP tests", html = TRUE, class = c("html", 
-        "character")), correct = TRUE, message = structure("Absolutely fabulous!", html = TRUE, class = c("html", 
+        "character")), correct = TRUE, message = structure("You rock!", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_2a8f1eb", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_b5c4484", 
         option = "We can reject the null hypothesis that there is no homophily for gender in both CUG and QAP tests", 
         value = "We can reject the null hypothesis that there is no homophily for gender in both CUG and QAP tests", 
         label = structure("We can reject the null hypothesis that there is no homophily for gender in both CUG and QAP tests", html = TRUE, class = c("html", 
@@ -1637,7 +1637,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "cupqap-results-answer", question = "cupqap-results"), 
     loading = NULL, random_answer_order = FALSE, allow_retry = TRUE, 
-    seed = 1407181169.84473, options = list()), class = c("learnr_checkbox", 
+    seed = 1590058257.25957, options = list()), class = c("learnr_checkbox", 
 "tutorial_question")), session = session)
 </script>
 
@@ -1671,7 +1671,7 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "introeies", 
@@ -1733,7 +1733,7 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "qapmax", 
@@ -1796,7 +1796,7 @@ <h2>Glossary</h2>
 "learnr::random_phrases_add(language = \"en\", ", "                           praise = c(\"C'est génial!\",", 
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))", 
-"marvel_friends <- to_unsigned(ison_marvel_relationships, keep = \"positive\")", 
+"marvel_friends <- to_unsigned(to_uniplex(fict_marvel, \"relationship\"), keep = \"positive\")", 
 "marvel_friends <- to_giant(marvel_friends)", "marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = "model1 <- net_regression(weight ~ ego(Citations) + alter(Citations) + sim(Citations) + \n                      ego(Discipline) + same(Discipline), \n                      ison_networkers, times = 200)", 
@@ -1805,16 +1805,16 @@ <h2>Glossary</h2>
         list(label = "qapinterp", code = "", opts = list(label = "\"qapinterp\"", 
             exercise = "TRUE", exercise.setup = "\"qapmax-solution\"", 
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From 9c3e49a133c23b8909aff67c4b19ef8b6b7107df Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 15:22:33 +0200
Subject: [PATCH 05/12] Updated ergm tutorial with new netrics names

---
 inst/tutorials/tutorial9/ergm.html            | 207 +++++++++---------
 .../ergm_files/figure-html/visflo2-1.png      | Bin 200646 -> 205219 bytes
 2 files changed, 104 insertions(+), 103 deletions(-)

diff --git a/inst/tutorials/tutorial9/ergm.html b/inst/tutorials/tutorial9/ergm.html
index a988109a..57cd9d6e 100644
--- a/inst/tutorials/tutorial9/ergm.html
+++ b/inst/tutorials/tutorial9/ergm.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>Modelling with ERGMs</title>
 
@@ -139,10 +139,10 @@ <h3>Data</h3>
 <div class="tutorial-exercise" data-label="ergm-data"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
-<pre class="text"><code>data(package=&#39;ergm&#39;) # tells us the datasets in our packages
+<pre class="text"><code># data(package=&#39;ergm&#39;) # tells us the datasets in our packages
 data(florentine) # loads flomarriage and flobusiness data
 flomarriage # Data on marriage alliances among Renaissance Florentine families
-?florentine # You can see more information about the attributes in the help file.</code></pre>
+# ?florentine # You can see more information about the attributes in the help file.</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 16 nodes and 20 edges in this undirected network. Let’s get a
@@ -152,7 +152,7 @@ <h3>Data</h3>
 <div class="tutorial-exercise" data-label="density" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
-<pre class="text"><code>net_density(flomarriage)
+<pre class="text"><code>net_by_density(flomarriage)
 net_node_attributes(flomarriage)
 net_tie_attributes(flomarriage)</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>
@@ -169,7 +169,7 @@ <h3>Visualisation</h3>
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
 <pre class="text"><code>graphr(flomarriage, node_size = &quot;wealth&quot;)
-as_tidygraph(flomarriage) %&gt;% mutate_nodes(Degree = node_deg()) %&gt;%  
+as_tidygraph(flomarriage) %&gt;% mutate_nodes(Degree = node_by_deg()) %&gt;%  
   mutate_ties(Triangles = tie_is_triangular()) %&gt;% 
   graphr(node_size = &quot;Degree&quot;, edge_color = &quot;Triangles&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>
@@ -357,7 +357,7 @@ <h3>Predicted Probabilities</h3>
 one-unit change in <span class="math inline">\(X\)</span> on <span
 class="math inline">\(Pr(Y_i = 1)\)</span>, how much change there is
 depends critically on “where you are on the curve”.</p>
-<p><img src="ergm_files/figure-html/predprobplot-1.png" width="768" /></p>
+<p><img src="ergm_files/figure-html/predprobplot-1.png" alt="" width="768" /></p>
 <p>This means we need to calculate predicted probabilities of <span
 class="math inline">\(Y\)</span> at specific values of key predictors.
 In our simple Bernoulli model, we’re modelling only the number of edges,
@@ -468,7 +468,7 @@ <h3>GOFing</h3>
 </div>
 <div id="section-markov-model" class="section level2">
 <h2>Markov model</h2>
-<p><img src="ergm_files/figure-html/visflo2-1.png" width="864" /></p>
+<p><img src="ergm_files/figure-html/visflo2-1.png" alt="" width="864" /></p>
 <p>Let’s review what we know about the marriage network to get a better
 fit. There are some nodes with more activity (higher degree) in this
 network than others, and some triangle configurations appear. Yet our
@@ -811,6 +811,7 @@ <h2>Free play</h2>
 <p>
 <script type="application/shiny-prerendered" data-context="server-start">
 library(manynet)
+library(netrics)
 library(autograph)
 library(migraph)
 library(ergm)
@@ -880,8 +881,8 @@ <h2>Free play</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "ergm-data", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -906,7 +907,7 @@ <h2>Free play</h2>
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
     eval = TRUE)), setup = NULL, chunks = list(list(label = "ergm-data", 
-    code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
     opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
     engine = "r")), code_check = NULL, error_check = NULL, check = NULL, 
     solution = NULL, tests = NULL, options = list(eval = FALSE, 
@@ -926,10 +927,10 @@ <h2>Free play</h2>
         message = TRUE, render = NULL, ref.label = NULL, child = NULL, 
         engine = "r", split = FALSE, include = TRUE, purl = TRUE, 
         max.print = 1000, label = "ergm-data", exercise = TRUE, 
-        code = c("data(package='ergm') # tells us the datasets in our packages", 
+        code = c("# data(package='ergm') # tells us the datasets in our packages", 
         "data(florentine) # loads flomarriage and flobusiness data", 
         "flomarriage # Data on marriage alliances among Renaissance Florentine families", 
-        "?florentine # You can see more information about the attributes in the help file."
+        "# ?florentine # You can see more information about the attributes in the help file."
         ), out.width.px = 624, out.height.px = 384, params.src = "ergm-data, exercise = TRUE", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
     engine = "r", version = "4"), class = c("r", "tutorial_exercise"
@@ -946,8 +947,8 @@ <h2>Free play</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "density", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -971,10 +972,10 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
-        engine = "r"), list(label = "density", code = "net_density(flomarriage)\nnet_node_attributes(flomarriage)\nnet_tie_attributes(flomarriage)", 
+        engine = "r"), list(label = "density", code = "net_by_density(flomarriage)\nnet_node_attributes(flomarriage)\nnet_tie_attributes(flomarriage)", 
         opts = list(label = "\"density\"", exercise = "TRUE", 
             exercise.setup = "\"ergm-data\""), engine = "r")), 
     code_check = NULL, error_check = NULL, check = NULL, solution = NULL, 
@@ -994,7 +995,7 @@ <h2>Free play</h2>
         warning = TRUE, error = FALSE, message = TRUE, render = NULL, 
         ref.label = NULL, child = NULL, engine = "r", split = FALSE, 
         include = TRUE, purl = TRUE, max.print = 1000, label = "density", 
-        exercise = TRUE, exercise.setup = "ergm-data", code = c("net_density(flomarriage)", 
+        exercise = TRUE, exercise.setup = "ergm-data", code = c("net_by_density(flomarriage)", 
         "net_node_attributes(flomarriage)", "net_tie_attributes(flomarriage)"
         ), out.width.px = 624, out.height.px = 384, params.src = "density, exercise=TRUE, exercise.setup = \"ergm-data\"", 
         fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"), 
@@ -1012,8 +1013,8 @@ <h2>Free play</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "vis-flomarriage", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1037,10 +1038,10 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
-        engine = "r"), list(label = "vis-flomarriage", code = "graphr(flomarriage, node_size = \"wealth\")\nas_tidygraph(flomarriage) %>% mutate_nodes(Degree = node_deg()) %>%  \n  mutate_ties(Triangles = tie_is_triangular()) %>% \n  graphr(node_size = \"Degree\", edge_color = \"Triangles\")", 
+        engine = "r"), list(label = "vis-flomarriage", code = "graphr(flomarriage, node_size = \"wealth\")\nas_tidygraph(flomarriage) %>% mutate_nodes(Degree = node_by_deg()) %>%  \n  mutate_ties(Triangles = tie_is_triangular()) %>% \n  graphr(node_size = \"Degree\", edge_color = \"Triangles\")", 
         opts = list(label = "\"vis-flomarriage\"", exercise = "TRUE", 
             exercise.setup = "\"ergm-data\"", fig.width = "8"), 
         engine = "r")), code_check = NULL, error_check = NULL, 
@@ -1062,7 +1063,7 @@ <h2>Free play</h2>
         engine = "r", split = FALSE, include = TRUE, purl = TRUE, 
         max.print = 1000, label = "vis-flomarriage", exercise = TRUE, 
         exercise.setup = "ergm-data", code = c("graphr(flomarriage, node_size = \"wealth\")", 
-        "as_tidygraph(flomarriage) %>% mutate_nodes(Degree = node_deg()) %>%  ", 
+        "as_tidygraph(flomarriage) %>% mutate_nodes(Degree = node_by_deg()) %>%  ", 
         "  mutate_ties(Triangles = tie_is_triangular()) %>% ", 
         "  graphr(node_size = \"Degree\", edge_color = \"Triangles\")"
         ), out.width.px = 768, out.height.px = 384, params.src = "vis-flomarriage, exercise=TRUE, exercise.setup = \"ergm-data\", fig.width=8", 
@@ -1081,8 +1082,8 @@ <h2>Free play</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "flombern", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1106,8 +1107,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flombern", code = "flom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
         opts = list(label = "\"flombern\"", exercise = "TRUE", 
@@ -1148,8 +1149,8 @@ <h2>Free play</h2>
 
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "flombee", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1173,8 +1174,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flombern", code = "flom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
         opts = list(label = "\"flombern\"", exercise = "TRUE", 
@@ -1208,26 +1209,26 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "signfq", question = structure("Which statement is most detailed and correct?", html = TRUE, class = c("html", 
-"character")), answers = list(structure(list(id = "lnr_ans_8852990", 
+"character")), answers = list(structure(list(id = "lnr_ans_f2151fa", 
     option = "There is a statistically significant effect here.", 
     value = "There is a statistically significant effect here.", 
     label = structure("There is a statistically significant effect here.", 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_e5f7ede", 
+"tutorial_quiz_answer")), structure(list(id = "lnr_ans_b7661da", 
     option = "The edges coefficient is negative, indicating that as the number of edges increases, the probability of a tie forming between any two nodes decreases.", 
     value = "The edges coefficient is negative, indicating that as the number of edges increases, the probability of a tie forming between any two nodes decreases.", 
     label = structure("The edges coefficient is negative, indicating that as the number of edges increases, the probability of a tie forming between any two nodes decreases.", 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_fc133e1", 
+"tutorial_quiz_answer")), structure(list(id = "lnr_ans_807a216", 
     option = "The edges coefficient is negative and statistically significant, indicating that as the number of edges increases, the probability of a tie forming between any two nodes decreases.", 
     value = "The edges coefficient is negative and statistically significant, indicating that as the number of edges increases, the probability of a tie forming between any two nodes decreases.", 
     label = structure("The edges coefficient is negative and statistically significant, indicating that as the number of edges increases, the probability of a tie forming between any two nodes decreases.", 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_1d52ec5", 
+"tutorial_quiz_answer")), structure(list(id = "lnr_ans_c3212c3", 
     option = "There are -1.609438 ties in this network.", value = "There are -1.609438 ties in this network.", 
     label = structure("There are -1.609438 ties in this network.", 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_f100194", 
+"tutorial_quiz_answer")), structure(list(id = "lnr_ans_25182ab", 
     option = "There is no statistically significant effect here.", 
     value = "There is no statistically significant effect here.", 
     label = structure("There is no statistically significant effect here.", html = TRUE, class = c("html", 
@@ -1239,7 +1240,7 @@ <h2>Free play</h2>
 "character")), incorrect = structure("Incorrect", html = TRUE, class = c("html", 
 "character")), message = NULL, post_message = NULL), ids = list(
     answer = "signfq-answer", question = "signfq"), loading = NULL, 
-    random_answer_order = TRUE, allow_retry = TRUE, seed = 1088774493.493, 
+    random_answer_order = TRUE, allow_retry = TRUE, seed = 329707004.346468, 
     options = list()), class = c("learnr_radio", "tutorial_question"
 )), session = session)
 </script>
@@ -1254,8 +1255,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "oddsratio", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1279,8 +1280,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flombern", code = "flom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
         opts = list(label = "\"flombern\"", exercise = "TRUE", 
@@ -1323,8 +1324,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "predprob", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1348,8 +1349,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flombern", code = "flom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
         opts = list(label = "\"flombern\"", exercise = "TRUE", 
@@ -1393,8 +1394,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "flom-sim", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1418,8 +1419,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flombern", code = "flom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
         opts = list(label = "\"flombern\"", exercise = "TRUE", 
@@ -1463,8 +1464,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "flom-gof", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1488,8 +1489,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.\nflom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flombern", code = "flom.bern <- ergm(flomarriage ~ edges)  \ntidy(flom.bern) # To return the estimated coefficients, with p-values etc.\nglance(flom.bern, deviance = TRUE) # To collect the deviance, AIC and BIC, etc.", 
         opts = list(label = "\"flombern\"", exercise = "TRUE", 
@@ -1536,8 +1537,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "flom-markov", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1561,8 +1562,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flom-markov", code = "flom.mark1 <- ergm(flomarriage ~ edges + triangle) \n# Add verbose=T to watch for the convergence test p-value and \n# log-likelihood improvement to go down!\nplot(flom.mark1)\ntidy(flom.mark1)\nglance(flom.mark1)", 
         opts = list(label = "\"flom-markov\"", exercise = "TRUE", 
@@ -1604,8 +1605,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "markov-predict", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1629,8 +1630,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.\nflom.mark1 <- ergm(flomarriage ~ edges + triangle) \n# Add verbose=T to watch for the convergence test p-value and \n# log-likelihood improvement to go down!\nplot(flom.mark1)\ntidy(flom.mark1)\nglance(flom.mark1)", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.\nflom.mark1 <- ergm(flomarriage ~ edges + triangle) \n# Add verbose=T to watch for the convergence test p-value and \n# log-likelihood improvement to go down!\nplot(flom.mark1)\ntidy(flom.mark1)\nglance(flom.mark1)", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flom-markov", code = "flom.mark1 <- ergm(flomarriage ~ edges + triangle) \n# Add verbose=T to watch for the convergence test p-value and \n# log-likelihood improvement to go down!\nplot(flom.mark1)\ntidy(flom.mark1)\nglance(flom.mark1)", 
         opts = list(label = "\"flom-markov\"", exercise = "TRUE", 
@@ -1676,8 +1677,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "flom-mark1-gof", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1701,8 +1702,8 @@ <h2>Free play</h2>
 "                                      \"Beau travail!\",", "                                      \"Bravo!\",", 
 "                                      \"Super!\"),", "                           encouragement = c(\"Bon effort\"))"
 ), chunk_opts = list(label = "setup", include = FALSE, purl = FALSE, 
-    eval = TRUE)), setup = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.\nflom.mark1 <- ergm(flomarriage ~ edges + triangle) \n# Add verbose=T to watch for the convergence test p-value and \n# log-likelihood improvement to go down!\nplot(flom.mark1)\ntidy(flom.mark1)\nglance(flom.mark1)", 
-    chunks = list(list(label = "ergm-data", code = "data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n?florentine # You can see more information about the attributes in the help file.", 
+    eval = TRUE)), setup = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.\nflom.mark1 <- ergm(flomarriage ~ edges + triangle) \n# Add verbose=T to watch for the convergence test p-value and \n# log-likelihood improvement to go down!\nplot(flom.mark1)\ntidy(flom.mark1)\nglance(flom.mark1)", 
+    chunks = list(list(label = "ergm-data", code = "# data(package='ergm') # tells us the datasets in our packages\ndata(florentine) # loads flomarriage and flobusiness data\nflomarriage # Data on marriage alliances among Renaissance Florentine families\n# ?florentine # You can see more information about the attributes in the help file.", 
         opts = list(label = "\"ergm-data\"", exercise = "TRUE"), 
         engine = "r"), list(label = "flom-markov", code = "flom.mark1 <- ergm(flomarriage ~ edges + triangle) \n# Add verbose=T to watch for the convergence test p-value and \n# log-likelihood improvement to go down!\nplot(flom.mark1)\ntidy(flom.mark1)\nglance(flom.mark1)", 
         opts = list(label = "\"flom-markov\"", exercise = "TRUE", 
@@ -1747,8 +1748,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "markov-freeplay", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1809,8 +1810,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "magnolia-data", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1875,8 +1876,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "magncov1", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -1939,8 +1940,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "magnracefreq", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -2003,8 +2004,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "magncov2", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -2067,8 +2068,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "magnmark1", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -2130,8 +2131,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "magnstr", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -2193,8 +2194,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "magnattr", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
 "flom.bern <- ergm(flomarriage ~ edges)", "flom.bern.gof <- gof(flom.bern,  GOF = ~ distance + degree + espartners)", 
 "magnolia <- to_giant(faux.magnolia.high) ", "knitr::opts_chunk$set(echo = FALSE)", 
@@ -2257,8 +2258,8 @@ <h2>Free play</h2>
  
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::store_exercise_cache(structure(list(label = "magnfit", global_setup = structure(c("library(manynet)", 
-"library(autograph)", "library(migraph)", "library(ergm)", "library(learnr)", 
-"data(florentine) # loads flomarriage and flobusiness data", 
+"library(netrics)", "library(autograph)", "library(migraph)", 
+"library(ergm)", "library(learnr)", "data(florentine) # loads flomarriage and flobusiness data", 
 "data(faux.magnolia.high) # loads flomarriage and flobusiness data", 
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(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 b87ac6f11ffb3035efc983aefb25a5c8111ba16b Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 15:24:19 +0200
Subject: [PATCH 06/12] #patch bump

---
 DESCRIPTION |  4 ++--
 NEWS.md     | 15 +++++++++++++++
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 10c19cfc..94f2b7c1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: migraph
 Title: Inferential Methods for Multimodal and Other Networks 
-Version: 1.6.0
-Date: 2026-04-04
+Version: 1.6.1
+Date: 2026-04-13
 Description: A set of tools for testing networks.
    It includes functions for univariate and multivariate 
    conditional uniform graph and quadratic assignment procedure testing,
diff --git a/NEWS.md b/NEWS.md
index b095cbd7..f1ce3497 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,18 @@
+# migraph 1.6.1
+
+2026-04-13
+
+## Package
+
+- Fixed logo sizing and location
+
+## Tutorials
+
+- Fixed `run_tute()` to identify and use tutorials in `{netrics}`
+- Updated diversity tutorial with new netrics names
+- Updated diffusion tutorial with new netrics names
+- Updated ergm tutorial with new netrics names
+
 # migraph 1.6.0
 
 2026-04-04

From 6e2aa89db0b9ce8215257cd1a179b4b7761b79fb Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 15:33:50 +0200
Subject: [PATCH 07/12] Fixed extract_tute() to use tutorials in netrics

---
 NEWS.md          | 2 +-
 R/tutorial_run.R | 6 +++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index f1ce3497..29538762 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -8,7 +8,7 @@
 
 ## Tutorials
 
-- Fixed `run_tute()` to identify and use tutorials in `{netrics}`
+- Fixed `run_tute()` and `extract_tute()` to identify and use tutorials in `{netrics}`
 - Updated diversity tutorial with new netrics names
 - Updated diffusion tutorial with new netrics names
 - Updated ergm tutorial with new netrics names
diff --git a/R/tutorial_run.R b/R/tutorial_run.R
index 6f6c2193..56689ca9 100644
--- a/R/tutorial_run.R
+++ b/R/tutorial_run.R
@@ -81,9 +81,13 @@ extract_tute <- function(tute) {
       thisRequires("autograph")
       pth <- gsub("manynet", "autograph", pth)
     }
+    if(!dir.exists(pth)) {
+      thisRequires("netrics")
+      pth <- gsub("autograph", "netrics", pth)
+    }
     if(!dir.exists(pth)) {
       thisRequires("migraph")
-      pth <- gsub("autograph", "migraph", pth)
+      pth <- gsub("netrics", "migraph", pth)
     }
     knitr::purl(file.path(pth, list.files(pth, pattern = "*.Rmd")),
                 documentation = 1)

From a4156c85a3a0ce4f9e6530df61ac9d0f8716bf6c Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 15:38:23 +0200
Subject: [PATCH 08/12] Fixed inconsistencies in function referencing

---
 inst/tutorials/tutorial7/diffusion.Rmd  |   8 +-
 inst/tutorials/tutorial7/diffusion.html | 122 ++++++++++++------------
 inst/tutorials/tutorial8/diversity.Rmd  |   2 +-
 inst/tutorials/tutorial8/diversity.html |  80 ++++++++--------
 4 files changed, 106 insertions(+), 106 deletions(-)

diff --git a/inst/tutorials/tutorial7/diffusion.Rmd b/inst/tutorials/tutorial7/diffusion.Rmd
index b6a70590..c5042758 100644
--- a/inst/tutorials/tutorial7/diffusion.Rmd
+++ b/inst/tutorials/tutorial7/diffusion.Rmd
@@ -172,7 +172,7 @@ and `graphs()` (at least by default) only graphs the first and last step.
 <!-- `grapht()` will show if and when there is complete infection, -->
 <!-- but we need to sit through each 'movie'. -->
 But there is an easier way.
-Play these same diffusions again, this time nesting the call within `net_infection_complete()`.
+Play these same diffusions again, this time nesting the call within `net_by_infection_complete()`.
 
 ```{r completeinfection, exercise = TRUE, purl = FALSE, fig.width=9}
 net_by_infection_complete(play_diffusion(create_ring(32, width = 2)))
@@ -183,7 +183,7 @@ net_by_infection_complete(play_diffusion(generate_smallworld(32, 0.025)))
 
 ```{r struct-interp, echo = FALSE, purl = FALSE}
 question("Does the structure of the network matter for whether and when a diffusion process completes?",
-  answer("No", message = "Take a closer look at the `net_infection_complete()` results. Are they all the same?"),
+  answer("No", message = "Take a closer look at the `net_by_infection_complete()` results. Are they all the same?"),
   answer("Yes", correct = TRUE, message = "We can see that the different structures have varying outcomes in terms of when the diffusion completes."),
   random_answer_order = TRUE,
   allow_retry = TRUE
@@ -713,7 +713,7 @@ It can be interpreted as follows:
 - Where $R_0$ = 1, the 'disease' will continue as endemic, if conditions allow.  
 
 So how can we establish the $R_0$ here?
-We can use `net_reproduction()`.
+We can use `net_by_reproduction()`.
 
 ```{r r0, exercise = TRUE, exercise.setup = "diffusions", fig.width=9}
 rd_diff <- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05)
@@ -762,7 +762,7 @@ the `r gloss("Herd Immunity Threshold", "hit")` or HIT.
 HIT indicates the threshold at which the reduction of susceptible members 
 of the network means that infections will no longer keep increasing, 
 allowing herd immunity to be achieved.
-`net_immunity()` gives us the proportion of the population that would need
+`net_by_immunity()` gives us the proportion of the population that would need
 to be recovered or vaccinated for the network to have herd immunity.
 
 ```{r immunity, exercise = TRUE, exercise.setup = "r0"}
diff --git a/inst/tutorials/tutorial7/diffusion.html b/inst/tutorials/tutorial7/diffusion.html
index 8ec06909..9459e1bf 100644
--- a/inst/tutorials/tutorial7/diffusion.html
+++ b/inst/tutorials/tutorial7/diffusion.html
@@ -291,7 +291,7 @@ <h3>Varying network structure</h3>
 <!-- `grapht()` will show if and when there is complete infection, -->
 <!-- but we need to sit through each 'movie'. --> But there is an easier
 way. Play these same diffusions again, this time nesting the call within
-<code>net_infection_complete()</code>.</p>
+<code>net_by_infection_complete()</code>.</p>
 <div class="tutorial-exercise" data-label="completeinfection"
 data-completion="1" data-diagnostics="1" data-startover="1"
 data-lines="0" data-pipe="|&gt;">
@@ -884,7 +884,7 @@ <h2>Make it stop</h2>
 - Where <span class="math inline">\(R_0\)</span> = 1, the ‘disease’ will
 continue as endemic, if conditions allow.</p>
 <p>So how can we establish the <span class="math inline">\(R_0\)</span>
-here? We can use <code>net_reproduction()</code>.</p>
+here? We can use <code>net_by_reproduction()</code>.</p>
 <div class="tutorial-exercise" data-label="r0" data-completion="1"
 data-diagnostics="1" data-startover="1" data-lines="0"
 data-pipe="|&gt;">
@@ -928,7 +928,7 @@ <h3>How many people do we need to vaccinate?</h3>
 Herd Immunity Threshold </dfn></abbr> or HIT. HIT indicates the
 threshold at which the reduction of susceptible members of the network
 means that infections will no longer keep increasing, allowing herd
-immunity to be achieved. <code>net_immunity()</code> gives us the
+immunity to be achieved. <code>net_by_immunity()</code> gives us the
 proportion of the population that would need to be recovered or
 vaccinated for the network to have herd immunity.</p>
 <div class="tutorial-exercise" data-label="immunity" data-completion="1"
@@ -1570,11 +1570,11 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "struct-interp", 
     question = structure("Does the structure of the network matter for whether and when a diffusion process completes?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_42a6550", 
+    "character")), answers = list(structure(list(id = "lnr_ans_1629e12", 
         option = "No", value = "No", label = structure("No", html = TRUE, class = c("html", 
-        "character")), correct = FALSE, message = structure("Take a closer look at the <code>net_infection_complete()<\u002fcode> results. Are they all the same?", html = TRUE, class = c("html", 
+        "character")), correct = FALSE, message = structure("Take a closer look at the <code>net_by_infection_complete()<\u002fcode> results. Are they all the same?", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
-    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_1185b8a", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_9933677", 
         option = "Yes", value = "Yes", label = structure("Yes", html = TRUE, class = c("html", 
         "character")), correct = TRUE, message = structure("We can see that the different structures have varying outcomes in terms of when the diffusion completes.", html = TRUE, class = c("html", 
         "character")), type = "literal"), class = c("tutorial_question_answer", 
@@ -1586,7 +1586,7 @@ <h2>Glossary</h2>
     "character")), message = NULL, post_message = NULL), ids = list(
         answer = "struct-interp-answer", question = "struct-interp"), 
     loading = NULL, random_answer_order = TRUE, allow_retry = TRUE, 
-    seed = 1163396271.45825, options = list()), class = c("learnr_radio", 
+    seed = 516887361.759306, options = list()), class = c("learnr_radio", 
 "tutorial_question")), session = session)
 </script>
 
@@ -1676,19 +1676,19 @@ <h2>Glossary</h2>
 <script type="application/shiny-prerendered" data-context="server">
 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "complex-interp", 
     question = structure("For which seed/threshold combinations was there complete infection?", html = TRUE, class = c("html", 
-    "character")), answers = list(structure(list(id = "lnr_ans_70e4b31", 
+    "character")), answers = list(structure(list(id = "lnr_ans_7f4c38f", 
         option = "seeds = 1 and thresholds = 1", value = "seeds = 1 and thresholds = 1", 
         label = structure("seeds = 1 and thresholds = 1", 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_271d67b", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_a6c224a", 
         option = "seeds = 1 and thresholds = 2", value = "seeds = 1 and thresholds = 2", 
         label = structure("seeds = 1 and thresholds = 2", 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_cd366bb", 
+    "tutorial_quiz_answer")), structure(list(id = "lnr_ans_f5fa8a4", 
         option = "seeds = 1:2 and thresholds = 2", value = "seeds = 1:2 and thresholds = 2", 
         label = structure("seeds = 1:2 and thresholds = 2", html = TRUE, class = c("html", 
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@@ -3081,27 +3081,27 @@ <h2>Glossary</h2>
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 learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "degroot-interp", 
     question = structure("What are some true statements about these results?", html = TRUE, class = c("html", 
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@@ -3115,7 +3115,7 @@ <h2>Glossary</h2>
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diff --git a/inst/tutorials/tutorial8/diversity.Rmd b/inst/tutorials/tutorial8/diversity.Rmd
index 4fabadfe..b0906e65 100644
--- a/inst/tutorials/tutorial8/diversity.Rmd
+++ b/inst/tutorials/tutorial8/diversity.Rmd
@@ -285,7 +285,7 @@ $$\frac{E-I}{E+I}$$
 where $E$ is the number of ties present between a variable's categories (i.e. external),
 and $I$ is the number of ties present within a variable's categories (i.e. internal).
 As such, an EI index of -1 suggests perfect homophily, whereas an EI index of +1 suggests perfect `r gloss("heterophily.","heterophily")`
-(This is why the function is called `net_heterophily()`).
+(This is why the function is called `net_by_heterophily()`).
 
 Check how homophilic three variables in the network are,
 "Gender", "PowerOrigin", and "Attractive".
diff --git a/inst/tutorials/tutorial8/diversity.html b/inst/tutorials/tutorial8/diversity.html
index 63db1c3b..9dceb6fb 100644
--- a/inst/tutorials/tutorial8/diversity.html
+++ b/inst/tutorials/tutorial8/diversity.html
@@ -379,7 +379,7 @@ <h2>Measuring heterophily</h2>
 suggests perfect homophily, whereas an EI index of +1 suggests perfect
 <abbr><dfn title='A tendency for nodes to connect to dissimilar nodes.'>
 heterophily. </dfn></abbr> (This is why the function is called
-<code>net_heterophily()</code>).</p>
+<code>net_by_heterophily()</code>).</p>
 <p>Check how homophilic three variables in the network are, “Gender”,
 “PowerOrigin”, and “Attractive”. Please assign the results so that we
 can use them later.</p>
@@ -1070,20 +1070,20 @@ <h2>Glossary</h2>
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@@ -1419,7 +1419,7 @@ <h2>Glossary</h2>
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 "character")), message = NULL, post_message = NULL), ids = list(
     answer = "qap-interp-answer", question = "qap-interp"), loading = NULL, 
-    random_answer_order = FALSE, allow_retry = TRUE, seed = 2003649979.06698, 
+    random_answer_order = FALSE, allow_retry = TRUE, seed = 299979252.860311, 
     options = list()), class = c("learnr_radio", "tutorial_question"
 )), session = session)
 </script>

From c04ec7e294bf0081b7985632933ad9fcaf379358 Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 18:23:44 +0200
Subject: [PATCH 09/12] Added tests for predict functions and netrics tutorials

---
 tests/testthat/test-model_predict.R     | 23 +++++++++++++++++++++++
 tests/testthat/test-tutorials_netrics.R |  7 +++++++
 2 files changed, 30 insertions(+)
 create mode 100644 tests/testthat/test-model_predict.R
 create mode 100644 tests/testthat/test-tutorials_netrics.R

diff --git a/tests/testthat/test-model_predict.R b/tests/testthat/test-model_predict.R
new file mode 100644
index 00000000..1940ee1b
--- /dev/null
+++ b/tests/testthat/test-model_predict.R
@@ -0,0 +1,23 @@
+test_that("predict.netlm works", {
+  networkers <- ison_networkers %>% to_subgraph(Discipline == "Sociology")
+  model1 <- net_regression(weight ~ ego(Citations) + alter(Citations) + sim(Citations), 
+                        networkers, times = 10)
+  pred <- predict(model1, matrix(c(1,10,5,2),1,4))
+  expect_length(pred, 1)
+  expect_type(pred, "double")
+})
+
+test_that("predict.netlogit works", {
+  networkers <- ison_networkers %>% to_subgraph(Discipline == "Sociology") %>% 
+    to_unweighted()
+  model1 <- net_regression(. ~ ego(Citations) + alter(Citations) + sim(Citations), 
+                        networkers, times = 10)
+  pred_link <- predict(model1, matrix(c(1,10,5,2),1,4), type = "link")
+  pred_response <- predict(model1, matrix(c(1,10,5,2),1,4), type = "response")
+  
+  expect_length(pred_link, 1)
+  expect_type(pred_link, "double")
+  
+  expect_length(pred_response, 1)
+  expect_type(pred_response, "double")
+})
\ No newline at end of file
diff --git a/tests/testthat/test-tutorials_netrics.R b/tests/testthat/test-tutorials_netrics.R
new file mode 100644
index 00000000..912f638a
--- /dev/null
+++ b/tests/testthat/test-tutorials_netrics.R
@@ -0,0 +1,7 @@
+test_that("netrics tutorials work", {
+  for(tute.dir in list.dirs(system.file("tutorials", package = "netrics"), 
+                            recursive = F)){
+    tute.file <- list.files(tute.dir, pattern = "*.Rmd", full.names = T)
+    expect_null(test_tutorials(tute.file))
+  }
+})
\ No newline at end of file

From b1d79798dae6a5c0a55935e8d5a9ac61368a5231 Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Mon, 13 Apr 2026 18:24:30 +0200
Subject: [PATCH 10/12] Updated news

---
 NEWS.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 29538762..6791ede5 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -13,6 +13,11 @@
 - Updated diffusion tutorial with new netrics names
 - Updated ergm tutorial with new netrics names
 
+## Testing
+
+- Added tests for `predict()` methods
+- Added testing for `{netrics}` tutorials
+
 # migraph 1.6.0
 
 2026-04-04

From 237ef8b649e0493c96c7757d42f2d8445f87f1bc Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Tue, 14 Apr 2026 08:53:22 +0200
Subject: [PATCH 11/12] Added basic testing for run_tute() and extract_tute()

---
 DESCRIPTION                         | 2 +-
 R/tutorial_run.R                    | 8 ++++++--
 tests/testthat/test-tutorials_run.R | 9 +++++++++
 3 files changed, 16 insertions(+), 3 deletions(-)
 create mode 100644 tests/testthat/test-tutorials_run.R

diff --git a/DESCRIPTION b/DESCRIPTION
index 94f2b7c1..28ff21df 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: migraph
 Title: Inferential Methods for Multimodal and Other Networks 
 Version: 1.6.1
-Date: 2026-04-13
+Date: 2026-04-14
 Description: A set of tools for testing networks.
    It includes functions for univariate and multivariate 
    conditional uniform graph and quadratic assignment procedure testing,
diff --git a/R/tutorial_run.R b/R/tutorial_run.R
index 56689ca9..efad953c 100644
--- a/R/tutorial_run.R
+++ b/R/tutorial_run.R
@@ -33,7 +33,9 @@ run_tute <- function(tute) {
                          dplyr::as_tibble(learnr::available_tutorials(package = avail_pkgs[p]),
                                           silent = TRUE) %>% dplyr::select(1:3)
                        })
-    dplyr::bind_rows(tutelist) %>% dplyr::arrange(name) %>% print()
+    dplyr::bind_rows(tutelist) %>% 
+      dplyr::arrange(dplyr::across(dplyr::any_of("name"))) %>% 
+      print()
     manynet::snet_info("You can run a tutorial by typing e.g `run_tute('tutorial1')` or `run_tute('Data')` into the console.")
   } else {
     try(learnr::run_tutorial(tute, "manynet"), silent = TRUE)
@@ -72,7 +74,9 @@ extract_tute <- function(tute) {
                                                  dplyr::as_tibble(learnr::available_tutorials(package = avail_pkgs[p]),
                                                                   silent = TRUE) %>% dplyr::select(1:3)
                                                })
-    dplyr::bind_rows(tutelist) %>% dplyr::arrange(name) %>% print()
+    dplyr::bind_rows(tutelist) %>% 
+      dplyr::arrange(dplyr::across(dplyr::any_of("name"))) %>% 
+      print()
     manynet::snet_info("You can extract the code from one of these tutorials by typing e.g `extract_tute('tutorial1')` into the console.")
   } else {
     thisRequires("knitr")
diff --git a/tests/testthat/test-tutorials_run.R b/tests/testthat/test-tutorials_run.R
new file mode 100644
index 00000000..fa838886
--- /dev/null
+++ b/tests/testthat/test-tutorials_run.R
@@ -0,0 +1,9 @@
+test_that("run_tute() runs without error when missing argument", {
+  skip_if_not_installed("learnr")
+  expect_error(run_tute(), NA)
+})
+
+test_that("extract_tute() runs without error when missing argument", {
+  skip_if_not_installed("learnr")
+  expect_error(extract_tute(), NA)
+})
\ No newline at end of file

From 2f70e638e37fa0d553782e051a27991bdee45fab Mon Sep 17 00:00:00 2001
From: James Hollway <james.hollway@graduateinstitute.ch>
Date: Tue, 14 Apr 2026 10:03:38 +0200
Subject: [PATCH 12/12] Fixed how test_fit() handles diffusion models and
 non-equal length step results

---
 NEWS.md                             |  5 +++--
 R/model_distrib.R                   | 10 ++++++++--
 tests/testthat/test-model_distrib.R |  7 +++++++
 3 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 6791ede5..612d868f 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -5,6 +5,8 @@
 ## Package
 
 - Fixed logo sizing and location
+- Added tests for `predict()` methods
+- Added testing for `{netrics}` tutorials
 
 ## Tutorials
 
@@ -15,8 +17,7 @@
 
 ## Testing
 
-- Added tests for `predict()` methods
-- Added testing for `{netrics}` tutorials
+- Fixed how `test_fit()` handles diffusion models and non-equal length step results
 
 # migraph 1.6.0
 
diff --git a/R/model_distrib.R b/R/model_distrib.R
index 9fb39165..40874cbd 100644
--- a/R/model_distrib.R
+++ b/R/model_distrib.R
@@ -57,10 +57,16 @@ test_distribution <- function(diff_model1, diff_model2){
 #' @export
 test_fit <- function(diff_model, diff_models){ # make into method?
   x <- diff_model
+  if(is_graph(x)) x <- as_diffusion(x)
   y <- diff_models
   sim <- `0` <- NULL
-  sims <- y %>% dplyr::select(sim, t, I)
-  sims <- as.data.frame.matrix(stats::xtabs(I ~ sim + t, sims)) # tidyr::pivot_wider replacement
+  sims <- y %>% dplyr::select(sim, time, I)
+  if(max(x$time) < max(sims$time)){
+    x <- dplyr::bind_rows(x, 
+               dplyr::tibble(time = (max(x$time)+1):max(sims$time), 
+                             S = x$S[nrow(x)], I = x$I[nrow(x)]))
+  }
+  sims <- as.data.frame.matrix(stats::xtabs(I ~ sim + time, sims)) # tidyr::pivot_wider replacement
   sims <- sims[,colSums(stats::cov(sims))!=0]
   mah <- stats::mahalanobis(x$I[-1], colMeans(sims), stats::cov(sims))
   pval <- pchisq(mah, df=length(x$I[-1]), lower.tail=FALSE)
diff --git a/tests/testthat/test-model_distrib.R b/tests/testthat/test-model_distrib.R
index b41a1a91..70c8b481 100644
--- a/tests/testthat/test-model_distrib.R
+++ b/tests/testthat/test-model_distrib.R
@@ -3,3 +3,10 @@ test_that("test_distribution works", {
                     as_diffusion(play_diffusion(ison_networkers, thresholds = 75)))
   expect_output(print(res), "statistic")
 })
+
+test_that("test_fit works", {
+  x <- play_diffusion(generate_random(15), transmissibility = 0.7)
+  y <- play_diffusions(generate_random(15), transmissibility = 0.1, times = 40)
+  res <- test_fit(as_diffusion(x), y)
+  expect_output(print(res), "statistic")
+})
\ No newline at end of file