docs: fix use of deprecated functions in examples

R/community.R

@@ -1236,7 +1236,7 @@ cluster_leiden <- function(graph, objective_function = c("CPM", "modularity"),
 #' @export
 #' @keywords graphs
 #' @examples
-#' g <- graph.famous("Zachary")
+#' g <- make_graph("Zachary")
 #' comms <- cluster_fluid_communities(g, 2)
 cluster_fluid_communities <- function(graph, no.of.communities) {
   # Argument checks

R/fit.R

@@ -113,7 +113,7 @@
 #' @examples
 #'
 #' # This should approximately yield the correct exponent 3
-#' g <- barabasi.game(1000) # increase this number to have a better estimate
+#' g <- sample_pa(1000) # increase this number to have a better estimate
 #' d <- degree(g, mode = "in")
 #' fit1 <- fit_power_law(d + 1, 10)
 #' fit2 <- fit_power_law(d + 1, 10, implementation = "R.mle")

R/flow.R

@@ -207,9 +207,9 @@ min_cut <- function(graph, source = NULL, target = NULL, capacity = NULL, value.
 #' @keywords graphs
 #' @examples
 #'
-#' g <- barabasi.game(100, m = 1)
+#' g <- sample_pa(100, m = 1)
 #' g <- delete_edges(g, E(g)[100 %--% 1])
-#' g2 <- barabasi.game(100, m = 5)
+#' g2 <- sample_pa(100, m = 5)
 #' g2 <- delete_edges(g2, E(g2)[100 %--% 1])
 #' vertex_connectivity(g, 100, 1)
 #' vertex_connectivity(g2, 100, 1)
@@ -299,8 +299,8 @@ vertex_connectivity <- function(graph, source = NULL, target = NULL, checks = TR
 #' @keywords graphs
 #' @examples
 #'
-#' g <- barabasi.game(100, m = 1)
-#' g2 <- barabasi.game(100, m = 5)
+#' g <- sample_pa(100, m = 1)
+#' g2 <- sample_pa(100, m = 5)
 #' edge_connectivity(g, 100, 1)
 #' edge_connectivity(g2, 100, 1)
 #' edge_disjoint_paths(g2, 100, 1)
@@ -785,11 +785,11 @@ is_min_separator <- is_minimal_separator_impl
 #' )
 #'
 #' # Cohesive subgraphs
-#' mw1 <- induced.subgraph(mw, as.character(c(1:7, 17:23)))
-#' mw2 <- induced.subgraph(mw, as.character(7:16))
-#' mw3 <- induced.subgraph(mw, as.character(17:23))
-#' mw4 <- induced.subgraph(mw, as.character(c(7, 8, 11, 14)))
-#' mw5 <- induced.subgraph(mw, as.character(1:7))
+#' mw1 <- induced_subgraph(mw, as.character(c(1:7, 17:23)))
+#' mw2 <- induced_subgraph(mw, as.character(7:16))
+#' mw3 <- induced_subgraph(mw, as.character(17:23))
+#' mw4 <- induced_subgraph(mw, as.character(c(7, 8, 11, 14)))
+#' mw5 <- induced_subgraph(mw, as.character(1:7))
 #'
 #' min_separators(mw)
 #' min_separators(mw1)

R/motifs.R

@@ -47,7 +47,7 @@
 #' @family graph motifs
 #'
 #' @examples
-#' g <- barabasi.game(100)
+#' g <- sample_pa(100)
 #' motifs(g, 3)
 #' count_motifs(g, 3)
 #' sample_motifs(g, 3)
@@ -91,7 +91,7 @@ motifs <- function(graph, size = 3, cut.prob = rep(0, size)) {
 #' @family graph motifs
 #'
 #' @examples
-#' g <- barabasi.game(100)
+#' g <- sample_pa(100)
 #' motifs(g, 3)
 #' count_motifs(g, 3)
 #' sample_motifs(g, 3)
@@ -139,7 +139,7 @@ count_motifs <- function(graph, size = 3, cut.prob = rep(0, size)) {
 #' @family graph motifs
 #'
 #' @examples
-#' g <- barabasi.game(100)
+#' g <- sample_pa(100)
 #' motifs(g, 3)
 #' count_motifs(g, 3)
 #' sample_motifs(g, 3)

R/rewire.R

@@ -123,7 +123,7 @@ rewire_keeping_degseq <- function(graph, loops, niter) {
 #'
 #' # Rewiring the start of each directed edge preserves the in-degree distribution
 #' # but not the out-degree distribution
-#' g <- barabasi.game(1000)
+#' g <- sample_pa(1000)
 #' g2 <- g %>% rewire(each_edge(mode = "in", multiple = TRUE, prob = 0.2))
 #' degree(g, mode = "in") == degree(g2, mode = "in")
 each_edge <- function(prob, loops = FALSE, multiple = FALSE, mode = c("all", "out", "in", "total")) {

R/sgm.R

@@ -66,8 +66,8 @@ solve_LSAP <- function(x, maximum = FALSE) {
 #' g1 <- sample_gnp(10, 0.1)
 #' randperm <- c(1:3, 3 + sample(7))
 #' g2 <- sample_correlated_gnp(g1, corr = 1, p = g1$p, permutation = randperm)
-#' A <- as.matrix(get.adjacency(g1))
-#' B <- as.matrix(get.adjacency(g2))
+#' A <- as_adjacency_matrix(g1)
+#' B <- as_adjacency_matrix(g2)
 #' P <- match_vertices(A, B, m = 3, start = diag(rep(1, nrow(A) - 3)), 20)
 #' P
 #' @family sgm

R/structural.properties.R

@@ -1327,7 +1327,7 @@ coreness <- function(graph, mode = c("all", "out", "in")) {
 #' @export
 #' @examples
 #'
-#' g <- barabasi.game(100)
+#' g <- sample_pa(100)
 #' topo_sort(g)
 #'
 topo_sort <- function(graph, mode = c("out", "all", "in")) {
@@ -1486,19 +1486,19 @@ girth <- function(graph, circle = TRUE) {
 #' which_loop(g)
 #'
 #' # Multiple edges
-#' g <- barabasi.game(10, m = 3, algorithm = "bag")
+#' g <- sample_pa(10, m = 3, algorithm = "bag")
 #' any_multiple(g)
 #' which_multiple(g)
 #' count_multiple(g)
 #' which_multiple(simplify(g))
 #' all(count_multiple(simplify(g)) == 1)
 #'
 #' # Direction of the edge is important
-#' which_multiple(graph(c(1, 2, 2, 1)))
-#' which_multiple(graph(c(1, 2, 2, 1), dir = FALSE))
+#' which_multiple(make_graph(c(1, 2, 2, 1)))
+#' which_multiple(make_graph(c(1, 2, 2, 1), dir = FALSE))
 #'
 #' # Remove multiple edges but keep multiplicity
-#' g <- barabasi.game(10, m = 3, algorithm = "bag")
+#' g <- sample_pa(10, m = 3, algorithm = "bag")
 #' E(g)$weight <- count_multiple(g)
 #' g <- simplify(g, edge.attr.comb = list(weight = "min"))
 #' any(which_multiple(g))

R/topology.R

@@ -276,7 +276,7 @@ graph.subisomorphic.lad <- function(pattern, target, domains = NULL,
 #' isomorphic(g1, g2)
 #'
 #' # create two isomorphic graphs, by permuting the vertices of the first
-#' g1 <- barabasi.game(30, m = 2, directed = FALSE)
+#' g1 <- sample_pa(30, m = 2, directed = FALSE)
 #' g2 <- permute(g1, sample(vcount(g1)))
 #' # should be TRUE
 #' isomorphic(g1, g2)