feat: All arguments in calls to _impl() functions are named

R/assortativity.R

@@ -212,7 +212,13 @@ assortativity <- function(
     values.in <- types2
   }
 
-  assortativity_impl(graph, values, values.in, directed, normalized)
+  assortativity_impl(
+    graph = graph,
+    values = values,
+    values.in = values.in,
+    directed = directed,
+    normalized = normalized
+  )
 }
 
 assortativity_legacy <- function(
@@ -221,7 +227,12 @@ assortativity_legacy <- function(
   types2 = NULL,
   directed = TRUE
 ) {
-  assortativity_impl(graph, types1, types2, directed)
+  assortativity_impl(
+    graph = graph,
+    values = types1,
+    values.in = types2,
+    directed = directed
+  )
 }
 
 #' @param types Vector giving the vertex types. They as assumed to be integer

R/centrality.R

@@ -1245,7 +1245,7 @@ spectrum <- function(
   }
 
   eigen_adjacency_impl(
-    graph,
+    graph = graph,
     algorithm = algorithm,
     which = which,
     options = options

R/centralization.R

@@ -424,7 +424,7 @@ centr_degree_tmax <- function(
 
   # Function call
   res <- centralization_degree_tmax_impl(
-    graph,
+    graph = graph,
     nodes = nodes,
     mode = mode,
     loops = loops
@@ -676,9 +676,9 @@ centr_eigen <- function(
   centralization_eigenvector_centrality_impl(
     graph = graph,
     directed = directed,
+    scale = TRUE,
     options = options,
-    normalized = normalized,
-    scale = TRUE
+    normalized = normalized
   )
 }
 

R/cliques.R

@@ -611,9 +611,17 @@ independence_number <- ivs_size
 #' @cdocs igraph_clique_size_hist
 clique_size_counts <- function(graph, min = 0, max = 0, maximal = FALSE) {
   if (maximal) {
-    maximal_cliques_hist_impl(graph, min, max)
+    maximal_cliques_hist_impl(
+      graph = graph,
+      min.size = min,
+      max.size = max
+    )
   } else {
-    clique_size_hist_impl(graph, min, max)
+    clique_size_hist_impl(
+      graph = graph,
+      min.size = min,
+      max.size = max
+    )
   }
 }
 

R/community.R

@@ -1698,33 +1698,33 @@ cluster_leiden <- function(
   membership <- initial_membership
   if (n_iterations > 0) {
     res <- community_leiden_impl(
-      graph,
+      graph = graph,
       weights = weights,
-      # FIXME: Also check below, might not be covered by tests
       vertex.weights = vertex_weights,
       resolution = resolution,
       beta = beta,
       start = !is.null(membership),
       n.iterations = n_iterations,
       membership = membership
     )
+
     membership <- res$membership
   } else {
     prev_quality <- -Inf
     quality <- 0.0
     while (prev_quality < quality) {
       prev_quality <- quality
       res <- community_leiden_impl(
-        graph,
+        graph = graph,
         weights = weights,
-        # FIXME: Also check above, might not be covered by tests
         vertex.weights = vertex_weights,
         resolution = resolution,
         beta = beta,
         start = !is.null(membership),
         n.iterations = 1,
         membership = membership
       )
+
       membership <- res$membership
       quality <- res$quality
     }
@@ -2413,12 +2413,13 @@ cluster_label_prop0 <- function(
 
   # Function call
   membership <- community_label_propagation_impl(
-    graph,
+    graph = graph,
     mode = mode,
     weights = weights,
     initial = initial,
     fixed = fixed
   )
+
   res <- list()
   if (igraph_opt("add.vertex.names") && is_named(graph)) {
     res$names <- V(graph)$name
@@ -2605,9 +2606,10 @@ cluster_optimal <- function(graph, weights = NULL) {
 
   # Function call
   res <- community_optimal_modularity_impl(
-    graph,
+    graph = graph,
     weights = weights
   )
+
   if (igraph_opt("add.vertex.names") && is_named(graph)) {
     res$names <- V(graph)$name
   }
@@ -3304,7 +3306,6 @@ voronoi_cells <- function(
   voronoi_impl(
     graph = graph,
     generators = generators,
-    ...,
     weights = weights,
     mode = mode,
     tiebreaker = tiebreaker

R/components.R

@@ -258,14 +258,18 @@ decompose <- function(
 #' @export
 #' @cdocs igraph_articulation_points
 articulation_points <- function(graph) {
-  articulation_points_impl(graph = graph)
+  articulation_points_impl(
+    graph = graph
+  )
 }
 
 #' @rdname articulation_points
 #' @export
 #' @cdocs igraph_bridges
 bridges <- function(graph) {
-  bridges_impl(graph = graph)
+  bridges_impl(
+    graph = graph
+  )
 }
 
 
@@ -319,7 +323,9 @@ bridges <- function(graph) {
 #' @cdocs igraph_biconnected_components
 biconnected_components <- function(graph) {
   # Function call
-  res <- biconnected_components_impl(graph)
+  res <- biconnected_components_impl(
+    graph = graph
+  )
 
   # TODO: Clean up after fixing "." / "_" problem.
   # See https://github.com/igraph/rigraph/issues/1203
@@ -388,7 +394,9 @@ biconnected_components <- function(graph) {
 #' @export
 #' @cdocs igraph_is_biconnected
 is_biconnected <- function(graph) {
-  is_biconnected_impl(graph = graph)
+  is_biconnected_impl(
+    graph = graph
+  )
 }
 
 

R/conversion.R

@@ -965,7 +965,10 @@ get.incidence.dense <- function(
 ) {
   if (is.null(attr)) {
     ## Function call
-    res <- get_biadjacency_impl(graph, types)
+    res <- get_biadjacency_impl(
+      graph = graph,
+      types = types
+    )
 
     if (names && "name" %in% vertex_attr_names(graph)) {
       rownames(res$res) <- V(graph)$name[res$row_ids]

R/cycles.R

@@ -97,5 +97,12 @@ simple_cycles <- function(
   # Argument checks
   ensure_igraph(graph)
 
-  simple_cycles_impl(graph, mode, min %||% -1, max %||% -1)
+  simple_cycles_impl(
+    graph = graph,
+    mode = mode,
+    min.cycle.length = min %||%
+      -1,
+    max.cycle.length = max %||%
+      -1
+  )
 }

R/embedding.R

@@ -183,7 +183,9 @@ embed_adjacency_matrix <- function(
 #' @export
 #' @cdocs igraph_dim_select
 dim_select <- function(sv) {
-  dim_select_impl(sv = sv)
+  dim_select_impl(
+    sv = sv
+  )
 }
 
 
@@ -398,5 +400,8 @@ sample_sphere_volume <- function(dim, n = 1, radius = 1, positive = TRUE) {
 #' colSums(lpvs.dir)
 sample_dirichlet <- function(n, alpha) {
   # Use the _impl function
-  sample_dirichlet_impl(n = n, alpha = alpha)
+  sample_dirichlet_impl(
+    n = n,
+    alpha = alpha
+  )
 }

R/eulerian.R

@@ -68,14 +68,20 @@
 #' @export
 #' @cdocs igraph_is_eulerian
 has_eulerian_path <- function(graph) {
-  is_eulerian_impl(graph)$has_path
+  res <- is_eulerian_impl(
+    graph = graph
+  )
+  res$has_path
 }
 
 #' @rdname has_eulerian_path
 #' @export
 #' @cdocs igraph_is_eulerian
 has_eulerian_cycle <- function(graph) {
-  is_eulerian_impl(graph)$has_cycle
+  res <- is_eulerian_impl(
+    graph = graph
+  )
+  res$has_cycle
 }
 
 #' @rdname has_eulerian_path

R/flow.R

@@ -978,7 +978,9 @@ dominator_tree <- function(graph, root, mode = c("out", "in", "all", "total")) {
 #' @family flow
 #' @cdocs igraph_all_minimal_st_separators
 min_st_separators <- function(graph) {
-  all_minimal_st_separators_impl(graph = graph)
+  all_minimal_st_separators_impl(
+    graph = graph
+  )
 }
 
 
@@ -1214,5 +1216,7 @@ is_min_separator <- function(graph, candidate) {
 #' min_separators(camp)
 #' @cdocs igraph_minimum_size_separators
 min_separators <- function(graph) {
-  minimum_size_separators_impl(graph = graph)
+  minimum_size_separators_impl(
+    graph = graph
+  )
 }

R/games.R

@@ -1469,7 +1469,6 @@ sample_growing <- function(n, m = 1, ..., directed = TRUE, citation = FALSE) {
   growing_random_game_impl(
     n = n,
     m = m,
-    ...,
     directed = directed,
     citation = citation
   )
@@ -2576,7 +2575,13 @@ sample_hierarchical_sbm <- function(n, m, rho, C, p) {
   commonlen <- unique(c(mlen, rholen, Clen))
 
   if (length(commonlen) == 1 && commonlen == 1) {
-    hsbm_game_impl(n, m, rho, C, p)
+    hsbm_game_impl(
+      n = n,
+      m = m,
+      rho = rho,
+      C = C,
+      p = p
+    )
   } else {
     commonlen <- setdiff(commonlen, 1)
     if (length(commonlen) != 1) {
@@ -2593,7 +2598,13 @@ sample_hierarchical_sbm <- function(n, m, rho, C, p) {
     } else {
       rep(list(C), length.out = commonlen)
     }
-    hsbm_list_game_impl(n, m, rho, C, p)
+    hsbm_list_game_impl(
+      n = n,
+      mlist = m,
+      rholist = rho,
+      Clist = C,
+      p = p
+    )
   }
 }
 
@@ -2900,7 +2911,6 @@ sample_chung_lu <- function(
   chung_lu_game_impl(
     out.weights = out.weights,
     in.weights = in.weights,
-    ...,
     loops = loops,
     variant = variant
   )

R/hrg.R

@@ -357,7 +357,9 @@ hrg_tree <- function(hrg) {
     )
   }
 
-  out <- from_hrg_dendrogram_impl(hrg)
+  out <- from_hrg_dendrogram_impl(
+    hrg = hrg
+  )
 
   g <- out$graph
   set_vertex_attr(g, "probability", value = out$prob)
@@ -382,7 +384,9 @@ sample_hrg <- function(hrg) {
     )
   }
 
-  hrg_game_impl(hrg)
+  hrg_game_impl(
+    hrg = hrg
+  )
 }
 #' Predict edges based on a hierarchical random graph model
 #'

R/interface.R

@@ -356,7 +356,11 @@ neighbors <- function(graph, v, mode = c("out", "in", "all", "total")) {
     stop("No vertex was specified")
   }
 
-  neighbors_impl(graph, vid = v, mode = mode)
+  neighbors_impl(
+    graph = graph,
+    vid = v,
+    mode = mode
+  )
 }
 
 #' Incident edges of a vertex in a graph
@@ -613,7 +617,9 @@ get.edge.ids <- function(
 #' vcount(g)
 #' @cdocs igraph_vcount
 vcount <- function(graph) {
-  as.numeric(vcount_impl(graph))
+  as.numeric(vcount_impl(
+    graph = graph
+  ))
 }
 
 #' @export