Merge branch 'main' into next

Author: krlmlr

AGENTS.md

@@ -49,6 +49,7 @@ pak::pak(dependencies = "Config/Needs/build")
 
 - IMPORTANT: PR titles end up in `NEWS.md` grouped by conventional commit label. PRs and commits use the conventional commit style with backticks for code references such as `function_call()`
 - PRs are generally squashed, a clean history within a PR is not necessary
+- Before resuming work on a PR, always merge the current base branch (typically `main`)
 
 ### Comment Style
 

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: igraph
 Title: Network Analysis and Visualization
-Version: 2.2.1.9003
+Version: 2.2.1.9004
 Authors@R: c(
     person("Gábor", "Csárdi", , "csardi.gabor@gmail.com", role = "aut",
            comment = c(ORCID = "0000-0001-7098-9676")),

R/centrality.R

@@ -588,43 +588,14 @@ closeness <- function(
   normalized = FALSE,
   cutoff = -1
 ) {
-  # Argument checks
-  ensure_igraph(graph)
-
-  vids <- as_igraph_vs(graph, vids)
-  mode <- switch(
-    igraph_match_arg(mode),
-    "out" = 1,
-    "in" = 2,
-    "all" = 3,
-    "total" = 3
-  )
-  if (is.null(weights) && "weight" %in% edge_attr_names(graph)) {
-    weights <- E(graph)$weight
-  }
-  if (!is.null(weights) && any(!is.na(weights))) {
-    weights <- as.numeric(weights)
-  } else {
-    weights <- NULL
-  }
-  normalized <- as.logical(normalized)
-  cutoff <- as.numeric(cutoff)
-
-  on.exit(.Call(R_igraph_finalizer))
-  # Function call
-  res <- .Call(
-    R_igraph_closeness_cutoff,
-    graph,
-    vids - 1,
-    mode,
-    weights,
-    normalized,
-    cutoff
+  closeness_cutoff_impl(
+    graph = graph,
+    vids = vids,
+    mode = mode,
+    weights = weights,
+    normalized = normalized,
+    cutoff = cutoff
   )$res
-  if (igraph_opt("add.vertex.names") && is_named(graph)) {
-    names(res) <- V(graph)$name[vids]
-  }
-  res
 }
 
 #' Deprecated version of `closeness()`

R/community.R

@@ -911,33 +911,18 @@ modularity.igraph <- function(
   directed = TRUE,
   ...
 ) {
-  # Argument checks
-  ensure_igraph(x)
   if (
     is.null(membership) || (!is.numeric(membership) && !is.factor(membership))
   ) {
     cli::cli_abort("Membership is not a numerical vector")
   }
-  membership <- as.numeric(membership)
-  if (!is.null(weights) && any(!is.na(weights))) {
-    weights <- as.numeric(weights)
-  } else {
-    weights <- NULL
-  }
-  resolution <- as.numeric(resolution)
-  directed <- as.logical(directed)
-
-  on.exit(.Call(R_igraph_finalizer))
-  # Function call
-  res <- .Call(
-    R_igraph_modularity,
-    x,
-    membership - 1,
-    weights,
-    resolution,
-    directed
+  modularity_impl(
+    graph = x,
+    membership = membership,
+    weights = weights,
+    resolution = resolution,
+    directed = directed
   )
-  res
 }
 
 #' @rdname communities
@@ -1781,17 +1766,9 @@ cluster_leiden <- function(
 #' g <- make_graph("Zachary")
 #' comms <- cluster_fluid_communities(g, 2)
 cluster_fluid_communities <- function(graph, no.of.communities) {
-  # Argument checks
-  ensure_igraph(graph)
-
-  no.of.communities <- as.numeric(no.of.communities)
-
-  on.exit(.Call(R_igraph_finalizer))
-  # Function call
-  membership <- .Call(
-    R_igraph_community_fluid_communities,
-    graph,
-    no.of.communities
+  membership <- community_fluid_communities_impl(
+    graph = graph,
+    no_of_communities = no.of.communities
   )
 
   res <- list()

R/flow.R

@@ -434,20 +434,12 @@ min_cut <- function(
         capacity
       )
     } else {
-      res <- .Call(
-        R_igraph_st_mincut,
-        graph,
-        as_igraph_vs(graph, source) - 1,
-        as_igraph_vs(graph, target) - 1,
-        capacity
+      res <- st_mincut_impl(
+        graph = graph,
+        source = source,
+        target = target,
+        capacity = capacity
       )
-      # No need to add +1 here; R_igraph_st_mincut() is autogenerated and
-      # adds +1 already
-      if (igraph_opt("return.vs.es")) {
-        res$cut <- create_es(graph, res$cut)
-        res$partition1 <- create_vs(graph, res$partition1)
-        res$partition2 <- create_vs(graph, res$partition2)
-      }
     }
   }
 

R/games.R

@@ -1016,7 +1016,6 @@ pa <- function(...) constructor_spec(sample_pa, ...)
 #' plot(sample_gnp(6, 0.5))
 sample_gnp <- function(n, p, directed = FALSE, loops = FALSE) {
   type <- "gnp"
-  type1 <- switch(type, "gnp" = 0, "gnm" = 1)
 
   res <- erdos_renyi_game_gnp_impl(
     n = n,
@@ -1067,16 +1066,12 @@ gnp <- function(...) constructor_spec(sample_gnp, ...)
 #' degree_distribution(g)
 sample_gnm <- function(n, m, directed = FALSE, loops = FALSE) {
   type <- "gnm"
-  type1 <- switch(type, "gnp" = 0, "gnm" = 1)
 
-  on.exit(.Call(R_igraph_finalizer))
-  res <- .Call(
-    R_igraph_erdos_renyi_game_gnm,
-    as.numeric(n),
-    as.numeric(m),
-    as.logical(directed),
-    as.logical(loops),
-    FALSE
+  res <- erdos_renyi_game_gnm_impl(
+    n = n,
+    m = m,
+    directed = directed,
+    loops = loops
   )
 
   if (igraph_opt("add.params")) {
@@ -1950,16 +1945,14 @@ sample_pref <- function(
     ))
   }
 
-  on.exit(.Call(R_igraph_finalizer))
-  res <- .Call(
-    R_igraph_preference_game,
-    as.numeric(nodes),
-    as.numeric(types),
-    as.double(type.dist),
-    as.logical(fixed.sizes),
-    matrix(as.double(pref.matrix), types, types),
-    as.logical(directed),
-    as.logical(loops)
+  res <- preference_game_impl(
+    nodes = nodes,
+    types = types,
+    type_dist = type.dist,
+    fixed_sizes = fixed.sizes,
+    pref_matrix = pref.matrix,
+    directed = directed,
+    loops = loops
   )
   V(res[[1]])$type <- res[[2]] + 1
   if (igraph_opt("add.params")) {
@@ -2001,15 +1994,13 @@ sample_asym_pref <- function(
     ))
   }
 
-  on.exit(.Call(R_igraph_finalizer))
-  res <- .Call(
-    R_igraph_asymmetric_preference_game,
-    as.numeric(nodes),
-    as.numeric(types),
-    as.numeric(types),
-    matrix(as.double(type.dist.matrix), types, types),
-    matrix(as.double(pref.matrix), types, types),
-    as.logical(loops)
+  res <- asymmetric_preference_game_impl(
+    nodes = nodes,
+    out_types = types,
+    in_types = types,
+    type_dist_matrix = type.dist.matrix,
+    pref_matrix = pref.matrix,
+    loops = loops
   )
   V(res[[1]])$outtype <- res[[2]] + 1
   V(res[[1]])$intype <- res[[3]] + 1

R/hrg.R

@@ -456,34 +456,12 @@ predict_edges <- function(
   num.samples = 10000,
   num.bins = 25
 ) {
-  # Argument checks
-  ensure_igraph(graph)
-  if (is.null(hrg)) {
-    hrg <- list(
-      left = c(),
-      right = c(),
-      prob = c(),
-      edges = c(),
-      vertices = c()
-    )
-  }
-  hrg <- lapply(
-    hrg[c("left", "right", "prob", "edges", "vertices")],
-    as.numeric
-  )
-  start <- as.logical(start)
-  num.samples <- as.numeric(num.samples)
-  num.bins <- as.numeric(num.bins)
-
-  on.exit(.Call(R_igraph_finalizer))
-  # Function call
-  res <- .Call(
-    R_igraph_hrg_predict,
-    graph,
-    hrg,
-    start,
-    num.samples,
-    num.bins
+  res <- hrg_predict_impl(
+    graph = graph,
+    hrg = hrg,
+    start = start,
+    num_samples = num.samples,
+    num_bins = num.bins
   )
   res$edges <- matrix(res$edges, ncol = 2, byrow = TRUE)
   class(res$hrg) <- "igraphHRG"