v1.6.2

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: migraph
 Title: Inferential Methods for Multimodal and Other Networks 
-Version: 1.6.1
-Date: 2026-04-14
+Version: 1.6.2
+Date: 2026-04-17
 Description: A set of tools for testing networks.
    It includes functions for univariate and multivariate 
    conditional uniform graph and quadratic assignment procedure testing,

NAMESPACE

@@ -33,7 +33,6 @@ export(test_fit)
 export(test_gof)
 export(test_permutation)
 export(test_random)
-export(test_tutorials)
 export(tidy)
 importFrom(autograph,ag_base)
 importFrom(dplyr,"%>%")

NEWS.md

@@ -1,6 +1,23 @@
+# migraph 1.6.2
+
+2026-04-17
+
+## Testing
+
+- Fixed namespace issues
+
+## Tutorials
+
+- Added function testing for migraph tutorials
+- Added function testing for manynet tutorials (> v2.0.2)
+- Updated examples in solutions in migraph tutorials where errors could creep in
+- Skipped extracting some solutions in migraph where errors could creep in
+- Removed `test_tutorials()` from the public API; 
+  use `run_tute()` and `extract_tute()` for supported tutorial execution and extraction workflows
+
 # migraph 1.6.1
 
-2026-04-13
+2026-04-14
 
 ## Package
 

R/model_distrib.R

@@ -57,7 +57,7 @@ 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)
+  if(manynet::is_graph(x)) x <- manynet::as_diffusion(x)
   y <- diff_models
   sim <- `0` <- NULL
   sims <- y %>% dplyr::select(sim, time, I)

cran-comments.md

@@ -9,4 +9,4 @@
 
 0 errors | 0 warnings | 0 notes
 
-- Updates to bring autograph into line with latest version of manynet (> v2.0.0)
+- Updates to improve testing length

inst/tutorials/tutorial0/tutorial0.Rmd

@@ -105,7 +105,7 @@
 Remove the hash symbol at the start of this line to run it:
 
 ```{r comments, exercise = TRUE}
 # 1/5 # this will still be commented...
 ```
 
 In an R script you can toggle commenting for one or more lines using Cmd-Shift-C/Ctrl-Shift-C.
@@ -127,7 +127,7 @@
 It is important to note that R is case-sensitive, 
 i.e. `Print("Hello World")` will not work. Try it!
 
-```{r Print-hello-world, exercise = TRUE}
+```{r Print-hello-world, exercise = TRUE, purl=FALSE}
 Print("Hello World")
 ```
 
@@ -148,9 +148,9 @@
 In R, we can write such logical statements as:
 
 ```{r equivalence, exercise = TRUE}
 "James"=="james" # Try also "James"!="james"
 # Other logical statements include: ">", ">=", "<=", "<".
 # 1 < 5 # Try also "1 <= 5"
 ```
 
 Logical values are always either `TRUE` or `FALSE`,
@@ -168,10 +168,10 @@
 
 ```{r assignment, exercise = TRUE}
 surname <- "Hollway"
 y.chromosome <- T # or TRUE
 siblings <- 1
 age <- NA # This is used for missing information
 # Note that these objects then appear in RStudio's environment pane (by default the top right)
 ```
 
 You can then recover this information by simply calling these objects:
@@ -186,7 +186,7 @@
 And even operate on them:
 
 ```{r mult, exercise = TRUE, exercise.setup = "assignment"}
 siblings*3
 # Try multiplying the other objects by 3
 ```
 
@@ -211,10 +211,10 @@
 ```{r series, exercise = TRUE}
 teenageyrs <- 13:19
 teenageqrtrs <- seq(13, 19.99, by = 0.25)
 # We can recall every third value from this object using a repeating vector 
 teenageqrtrs
 teenageqrtrs[c(FALSE, FALSE, TRUE)]
 # teenageqrtrs[c(F, F, T)] # Also works but it is best practice to write out the logic.
 ```
 
 So R can help us store and recall values and even vectors of values,
@@ -360,7 +360,7 @@
 Compare the above with functions like the following, 
 which enables you to write an object out of R to some path on your hard-drive that you specify:
 
-```{r function-write, exercise = TRUE}
+```{r function-write, exercise = TRUE, purl=FALSE}
 write.csv(x = mydf, file = "~/Desktop/jamesdf.csv")
 ```
 

inst/tutorials/tutorial7/diffusion.Rmd

@@ -202,7 +202,7 @@ You can start the infection in California by specifying `seeds = 5`.
 ```{r usstates-hint}
 us_diff <- play_diffusion(irps_usgeo, seeds = 5)
 plot(us_diff)
-graphr(us_diff)
+# graphr(us_diff)
 net_by_infection_complete(us_diff)
 ```
 
@@ -577,7 +577,7 @@ to indicate that in only 1/2 cases is contagion successful.
 ```{r diffusions, exercise = TRUE, fig.width=9}
 rando <- generate_random(32, 0.1)
 graphr(rando)
-plot(play_diffusions(rando, transmissibility = 0.5, times = 5, steps = 10))
+plot(play_diffusions(ison_lawfirm, transmissibility = 0.5, times = 5, steps = 10))
 ```
 
 Note that in this plot the number of new infections is not plotted
@@ -610,7 +610,7 @@ plot(play_diffusions(____, recovery = ____))
 ```
 
 ```{r sir-solution}
-plot(play_diffusions(rando, recovery = 0.2))
+plot(play_diffusions(ison_lawfirm, recovery = 0.2))
 ```
 
 What we see in these kinds of models is typically a spike in infections
@@ -624,8 +624,8 @@ which should average out these differences and make the results more reliable.
 
 ```
 
-```{r sirtimes-solution}
-plot(play_diffusions(rando, recovery = 0.2, times = 100))
+```{r sirtimes-solution, purl=FALSE}
+plot(play_diffusions(ison_lawfirm, recovery = 0.2, times = 100))
 ```
 
 ### SIRS models
@@ -642,7 +642,7 @@ Play a single diffusion so that you can see what's going on in a particular run.
 ```
 
 ```{r sirs-solution}
-plot(play_diffusion(rando, recovery = 0.2, waning = 0.05))
+plot(play_diffusion(ison_lawfirm, recovery = 0.2, waning = 0.05))
 ```
 
 <!-- # visualise diffusion within random graph -->
@@ -659,7 +659,7 @@ Depending on your particular simulation,
 there might be some variation,
 so let's run this same diffusion but multiple (100?) times.
 
-```{r sirstimes-solution, exercise.setup = "diffusions", exercise = TRUE, fig.width=9}
+```{r sirstimes-solution, exercise.setup = "diffusions", exercise = TRUE, fig.width=9, purl=FALSE}
 plot(play_diffusions(rando, recovery = 0.2, waning = 0.05, times = 100))
 ```
 
@@ -689,10 +689,10 @@ Play a single diffusion so that you can see what's going on in a particular run.
 
 ```{r seir-solution}
 set.seed(123)
-plot(play_diffusion(rando, seeds = 10, latency = 0.25, recovery = 0.2))
+plot(play_diffusion(ison_lawfirm, seeds = 10, latency = 0.75, recovery = 0.2))
 
 # visualise diffusion with latency and recovery
-graphs(play_diffusion(rando, seeds = 10, latency = 0.25, recovery = 0.2), waves = c(1,5,10))
+# graphs(play_diffusion(ison_lawfirm, seeds = 10, latency = 0.75, recovery = 0.2), waves = c(1,5,10))
 ```
 
 ## Make it stop
@@ -716,7 +716,7 @@ So how can we establish the $R_0$ here?
 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)
+rd_diff <- play_diffusion(ison_lawfirm, transmissibility = 0.25, recovery = 0.05)
 plot(rd_diff)
 # R-nought
 net_by_reproduction(rd_diff)
@@ -777,7 +777,7 @@ The unnormalised version gives the number of nodes that would need to be vaccina
 Ok, so let's try this strategy.
 
 ```{r vaccinate, exercise = TRUE, exercise.setup = "diffusions", fig.width=9}
-rd_diff_vacc <- play_diffusion(rando, transmissibility = 0.25, recovery = 0.05, 
+rd_diff_vacc <- play_diffusion(ison_lawfirm, transmissibility = 0.25, recovery = 0.05, 
                                immune = 2:9)
 plot(rd_diff_vacc)
 net_by_infection_total(rd_diff_vacc)

inst/tutorials/tutorial8/diversity.Rmd

@@ -118,7 +118,8 @@ marvel_friends
 ```
 
 ```{r friends-solution}
-marvel_friends <- to_unsigned(ison_marvel_relationships, keep = "positive")
+marvel_friends <- to_uniplex(fict_marvel, "relationship")
+marvel_friends <- to_unsigned(marvel_friends, keep = "positive")
 marvel_friends <- to_giant(marvel_friends)
 marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances))
 marvel_friends

inst/tutorials/tutorial9/ergm.Rmd

@@ -573,7 +573,7 @@ see  papers in e.g. *Social Networks* v.29 and *J Stat Software* v. 24.
 
 Now let's try to fit a triangle model like with the Florentine dataset above:
 
-```{r magnmark1, exercise = TRUE}
+```{r magnmark1, exercise = TRUE, purl=FALSE}
 magn.mark1 <- ergm(magnolia ~ edges + triangle)
 ```
 

tests/testthat/helper-functions.R

@@ -25,3 +25,107 @@ bot5 <- function(res, dec = 4){
     unname(round(res, dec))[(lr-4):lr]
   } else unname(res)[(lr-2):lr]
 }
+
+find_pkg_tutorial_paths <- function(pkg) {
+  tute_folders <- list.dirs(system.file("tutorials", package = pkg),
+                             recursive = F)
+  tute_files <- unlist(lapply(tute_folders, function(folder) {
+    list.files(folder, pattern = "*.Rmd", full.names = TRUE)
+  }))
+  tute_files
+}
+
+check_tute_rendering <- function(path, quiet = TRUE){
+
+  skip_if_not_installed("rmarkdown")
+  stopifnot(all(file.exists(path)))
+
+  for(i in path){
+    if(!quiet) message("Rendering: ", basename(i))
+    tryCatch({
+      rmarkdown::render(input = i, 
+                        output_dir = tempdir(),
+                        intermediates_dir = tempdir(), quiet = quiet)
+      # Note that the Debian setup on CRAN does not allow for writing files to any
+      # location other than the temporary directory, which is why we must specify
+      # tempdir() in the two dir arguments.
+      if(!quiet) message("Successfully rendered: ", basename(i))
+    }, error = function(e) {
+      stop("Failed to render ", i, ": ", e$message, call. = FALSE)
+    })
+  }
+  invisible(NULL)
+}
+
+check_tute_functions <- function(path, skip = "ergm\\(", quiet = TRUE){
+  tmp <- tempfile(fileext = ".R")
+  knitr::purl(
+    input  = path,
+    output = tmp,
+    quiet  = quiet
+  )
+  exprs <- parse(tmp)  # your purled file
+  env <- new.env(parent = globalenv())
+
+  skip_rest <- FALSE
+  is_skipped_call <- function(expr) {
+    any(grepl(skip, deparse(expr)))
+  }
+
+  for (i in seq_along(exprs)) {
+    if (skip_rest) {
+      skip(paste("Skipping dependent expressions in", basename(path)))
+      next
+    }
+
+    if (is_skipped_call(exprs[[i]])) {
+      skip_rest <- TRUE
+      skip(paste("Skipping slow functions in", basename(path)))
+      next
+    }
+
+    w <- NULL
+    e <- NULL
+    m <- NULL
+
+    not_out <- withCallingHandlers(
+      tryCatch(
+        eval(exprs[[i]], envir = env),
+        error = function(err) {
+          e <<- err
+          NULL
+        }
+      ),
+      warning = function(wrn) {
+        w <<- wrn
+        invokeRestart("muffleWarning")
+      },
+      message = function(msg) {
+        m <<- c(m, conditionMessage(msg))
+        invokeRestart("muffleMessage")
+      }
+    )
+
+    # If there *was* a warning, check if it's a deprecated/defunct one
+    if (!is.null(w)) {
+      msg <- conditionMessage(w)
+
+      # Only fail if it's a deprecated/defunct warning
+      if (!grepl("deprecate|defunct|moved", msg, ignore.case = TRUE)) {
+        w <- NULL
+      }
+    }
+
+    # Now test what happened
+    expect_null(
+      e,
+      info = paste0("Error in expression ", i,
+                    " of ", basename(path), ": ", deparse(exprs[[i]]))
+    )
+
+    expect_null(
+      w,
+      info = paste("Warning in expression", i, ":", deparse(exprs[[i]]))
+    )
+  }
+}

tests/testthat/test-model_distrib.R

@@ -7,6 +7,6 @@ test_that("test_distribution works", {
 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)
+  res <- test_fit(x, y)
   expect_output(print(res), "statistic")
 })

tests/testthat/test-model_predict.R

@@ -1,7 +1,7 @@
 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)
+                        networkers, times = 5)
   pred <- predict(model1, matrix(c(1,10,5,2),1,4))
   expect_length(pred, 1)
   expect_type(pred, "double")
@@ -11,7 +11,7 @@ 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)
+                        networkers, times = 5)
   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")