Merge pull request #273 from snlab-ch/develop

v1.0.1

.github/workflows/prchecks.yml

@@ -16,7 +16,7 @@ jobs:
         config:
           - {os: macOS-latest,   r: 'release', artifact_name: '*.tar.gz', asset_name: macOS}
           - {os: windows-latest, r: 'release', artifact_name: '*.zip', asset_name: winOS}
-          - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest", artifact_name: '*.tar.gz', asset_name: linuxOS}
+          - {os: ubuntu-latest, r: 'release', artifact_name: '*.tar.gz', asset_name: linuxOS}
 
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true

.github/workflows/pushrelease.yml

@@ -16,7 +16,7 @@ jobs:
         config:
           - {os: macOS-latest,   r: 'release', artifact_name: '*.tar.gz', asset_name: macOS}
           - {os: windows-latest, r: 'release', artifact_name: '*.zip', asset_name: winOS}
-          - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest", artifact_name: '*.tar.gz', asset_name: linuxOS}
+          - {os: ubuntu-latest, r: 'release', artifact_name: '*.tar.gz', asset_name: linuxOS}
 
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true

DESCRIPTION

@@ -1,17 +1,17 @@
 Package: migraph
 Title: Multimodal Network Analysis and More
-Version: 1.0.0
-Date: 2023-06-13
+Version: 1.0.1
+Date: 2023-07-20
 Description: A set of tools for analysing multimodal networks.
-   All functions operate with matrices, edge lists, 
-   and 'igraph', 'network', and 'tidygraph' objects,
-   and on one-mode, two-mode (bipartite), and sometimes three-mode networks.
    It includes functions for measuring 
-   centrality, centralization, cohesion, closure, and constraint,
+   centrality, centralization, cohesion, closure, constraint and diversity,
    as well as for network block-modelling, regression, and diffusion models.
    The package is released as a complement to 
    'Multimodal Political Networks' (2021, ISBN:9781108985000),
-   and includes various datasets used in the book in addition to other network data.
+   and includes various datasets used in the book.
+   Built on the 'manynet' package, all functions operate with matrices, edge lists, 
+   and 'igraph', 'network', and 'tidygraph' objects,
+   and on one-mode, two-mode (bipartite), and sometimes three-mode networks.
 URL: https://github.com/snlab-ch/migraph
 BugReports: https://github.com/snlab-ch/migraph/issues
 License: MIT + file LICENSE

NAMESPACE

@@ -264,6 +264,7 @@ importFrom(network,as.network)
 importFrom(network,is.network)
 importFrom(purrr,flatten)
 importFrom(rlang,.data)
+importFrom(rlang,`%||%`)
 importFrom(rlang,enquo)
 importFrom(rlang,eval_tidy)
 importFrom(sna,brokerage)

NEWS.md

@@ -1,3 +1,17 @@
+# migraph 1.0.1
+
+## Measures
+
+- Fixed backends in `node_closeness()` and `node_betweenness()` in preparation for an `{igraph}` deprecation (merci @maelle)
+
+## Models
+
+- Fixed #272 by making `test_random()` and `test_permutation()` rely on graph dimensions rather than graph order (thank you finding this @rabenton)
+
+## Tutorials
+
+- Fixed some defunct function names
+
 # migraph 1.0.0
 
 ## Package

R/measure_centrality.R

@@ -207,10 +207,10 @@ node_betweenness <- function(.data, normalized = TRUE,
                                  directed = manynet::is_directed(graph), weights = weights, 
                                  normalized = normalized)
     } else {
-      out <- igraph::estimate_betweenness(graph = graph, vids = igraph::V(graph), 
-                                          directed = manynet::is_directed(graph), 
-                                          cutoff = cutoff, 
-                                          weights = weights)
+      out <- igraph::betweenness(graph = graph, v = igraph::V(graph), 
+                                 directed = manynet::is_directed(graph), 
+                                 cutoff = cutoff, 
+                                 weights = weights)
     }
   }
   out <- make_node_measure(out, .data)
@@ -325,13 +325,9 @@ node_closeness <- function(.data, normalized = TRUE,
     set_size <- ifelse(igraph::V(graph)$type, sum(igraph::V(graph)$type), sum(!igraph::V(graph)$type))
     out <- closeness/(1/(other_set_size+2*set_size-2))
     } else {
-      if (is.null(cutoff)) {
-        out <- igraph::closeness(graph = graph, vids = igraph::V(graph), mode = direction,
-                  weights = weights, normalized = normalized)
-      } else {
-        out <- igraph::estimate_closeness(graph = graph, vids = igraph::V(graph), mode = direction, 
-                           cutoff = cutoff, weights = weights, normalized = normalized)
-      }
+      cutoff <- cutoff %||% -1
+      out <- igraph::closeness(graph = graph, vids = igraph::V(graph), mode = direction, 
+                               cutoff = cutoff, weights = weights, normalized = normalized)
     }
   out <- make_node_measure(out, .data)
   out

R/migraph-package.R

@@ -0,0 +1,7 @@
+#' @importFrom rlang `%||%`
+#' @keywords internal
+"_PACKAGE"
+
+## usethis namespace: start
+## usethis namespace: end
+NULL

R/model_tests.R

@@ -30,7 +30,7 @@ test_random <- function(.data, FUN, ...,
   } else {
     obsd <- FUN(.data)
   }
-  n <- manynet::network_nodes(.data)
+  n <- manynet::network_dims(.data)
   d <- network_density(.data)
   future::plan(strategy)
   rands <- furrr::future_map(1:times, manynet::generate_random, n = n, p = d, 
@@ -79,11 +79,11 @@ test_permutation <- function(.data, FUN, ...,
   } else {
     obsd <- FUN(.data)
   }
-  n <- manynet::network_nodes(.data)
+  n <- manynet::network_dims(.data)
   d <- network_density(.data)
   future::plan(strategy)
   rands <- furrr::future_map(1:times, 
-                  function(x) manynet::as_igraph(manynet::generate_permutation(.data)), 
+                  function(x) manynet::generate_permutation(.data), 
                   .progress = verbose, 
                   .options = furrr::furrr_options(seed = T))
   if (!is.null(args)) {

README.Rmd

@@ -221,7 +221,7 @@ learnr::available_tutorials("migraph")
 ```
 
 You can then choose to begin a tutorial using the following command: e.g.
-`learnr::run_tutorial("tutorial2", "migraph")`.
+`learnr::run_tutorial("tutorial3", "migraph")`.
 For more details on the `{learnr}` package, see [here](https://rstudio.github.io/learnr/).
 
 ## Relationship to other packages

README.md

@@ -231,7 +231,6 @@ suggest that you check to see which vignettes are currently available:
 learnr::available_tutorials("migraph")
 #> Available tutorials:
 #> * migraph
-#>   - tutorial2 : "Visualisation"
 #>   - tutorial3 : "Centrality"
 #>   - tutorial4 : "Community"
 #>   - tutorial5 : "Equivalence"
@@ -241,7 +240,7 @@ learnr::available_tutorials("migraph")
 ```
 
 You can then choose to begin a tutorial using the following command:
-e.g. `learnr::run_tutorial("tutorial2", "migraph")`. For more details on
+e.g. `learnr::run_tutorial("tutorial3", "migraph")`. For more details on
 the `{learnr}` package, see [here](https://rstudio.github.io/learnr/).
 
 ## Relationship to other packages

inst/tutorials/tutorial8/regression.Rmd

@@ -177,6 +177,7 @@ $$\frac{E-I}{E+I}$$
 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 heterophily.
+(This is why the function is called `network_heterophily()`).
 
 Check how homophilic three variables in the network are,
 "Gender", "PowerOrigin", and "Attractive".
@@ -186,7 +187,7 @@ Check how homophilic three variables in the network are,
 ```
 
 ```{r ei-hint}
-network_homophily(____, ____)
+network_heterophily(____, ____)
 ```
 
 ```{r ei-solution}
@@ -244,13 +245,13 @@ plot(rand.____)
 
 ```{r rando-solution}
 rand.gender <- test_random(marvel_friends, 
-                            network_homophily, attribute = "Gender", 
+                            network_heterophily, attribute = "Gender", 
                            times = 1000)
 rand.power <- test_random(marvel_friends, 
-                           network_homophily, attribute = "PowerOrigin", 
+                           network_heterophily, attribute = "PowerOrigin", 
                            times = 1000)
 rand.attract <- test_random(marvel_friends, 
-                             network_homophily, attribute = "Attractive", 
+                             network_heterophily, attribute = "Attractive", 
                            times = 1000)
 plot(rand.gender)
 plot(rand.power)
@@ -311,7 +312,7 @@ attributes, but it is just a single permutation.
 Let's try a test that runs this over a succession of permutations,
 just as we did with random graphs.
 
-```{r testpermute, exercise=TRUE, exercise.setup = "friends-solution"}
+```{r testpermute, exercise=TRUE, exercise.setup = "rando-solution"}
 
 ```
 
@@ -333,7 +334,7 @@ Again, we see that there is perhaps nothing so surprising that we got the homoph
 for gender that we did, but the lack of power origin heterophily is surprising.
 Plot the results for gender and power according to the random and permutation baselines.
 
-```{r cugqap, exercise=TRUE, exercise.setup = "friends-solution"}
+```{r cugqap, exercise=TRUE, exercise.setup = "testpermute-solution"}
 
 ```
 

tests/testthat/test-model_tests.R

@@ -8,6 +8,9 @@ cugtest <- test_random(marvel_friends,
 cugtest2 <- test_random(marvel_friends,
                         network_betweenness,
                         times = 200)
+cugtest3 <- test_random(ison_southern_women, 
+                        network_equivalency, 
+                        times = 200)
 
 test_that("test_random works", {
   # Set the cugtest up
@@ -23,6 +26,7 @@ test_that("test_random works", {
   expect_s3_class(cugtest, "network_test")
   # Test stuff cug2
   expect_equal(as.numeric(cugtest2$testval), 0.238, tolerance = 0.001)
+  expect_equal(mean(cugtest3$testdist), 0.361, tolerance = 0.003)
   expect_equal(length(cugtest2$testdist), 200) # NB: Stochastic
   expect_false(cugtest2$mode)
   expect_false(cugtest2$diag)