v1.6.1

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: 2026-04-14
 Description: A set of tools for testing networks.
    It includes functions for univariate and multivariate 
    conditional uniform graph and quadratic assignment procedure testing,

NEWS.md

@@ -1,3 +1,24 @@
+# migraph 1.6.1
+
+2026-04-13
+
+## Package
+
+- Fixed logo sizing and location
+- Added tests for `predict()` methods
+- Added testing for `{netrics}` tutorials
+
+## Tutorials
+
+- 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
+
+## Testing
+
+- Fixed how `test_fit()` handles diffusion models and non-equal length step results
+
 # migraph 1.6.0
 
 2026-04-04

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)

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
@@ -33,12 +33,15 @@ 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)
-    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){
@@ -71,7 +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")
@@ -80,9 +85,13 @@ 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)

inst/tutorials/tutorial7/diffusion.Rmd

@@ -172,18 +172,18 @@ 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_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}
 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
@@ -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)
 ```
 
@@ -713,14 +713,14 @@ 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)
 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}
@@ -762,13 +762,13 @@ 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"}
 # 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)