igraph · krlmlr · Mar 30, 2023 · Mar 18, 2023 · Mar 18, 2023 · Mar 18, 2023
diff --git a/R/aaa-auto.R b/R/aaa-auto.R
diff --git a/R/bipartite.R b/R/bipartite.R
@@ -195,4 +195,4 @@ bipartite_projection_size <- bipartite_projection_size_impl
 #' bipartite_mapping(g3)
 #' @family bipartite
 #' @export
-bipartite_mapping <- bipartite_mapping_impl
+bipartite_mapping <- is_bipartite_impl
diff --git a/R/centrality.R b/R/centrality.R
@@ -679,7 +679,7 @@ subgraph_centrality <- function(graph, diag = FALSE) {
 #'
 #' @family centrality
 #' @export
-spectrum <- spectrum_impl
+spectrum <- eigen_adjacency_impl
 
 eigen_defaults <- list(
   pos = "LM", howmany = 1L, il = -1L, iu = -1L,
@@ -772,7 +772,7 @@ eigen_defaults <- list(
 #' eigen_centrality(g)
 #' @family centrality
 #' @export
-eigen_centrality <- eigen_centrality_impl
+eigen_centrality <- eigenvector_centrality_impl
 
 
 #' Strength or weighted vertex degree
@@ -1006,7 +1006,7 @@ authority_score <- authority_score_impl
 #' page_rank(g3, personalized = reset)$vector
 #' @family centrality
 #' @export
-page_rank <- page_rank_impl
+page_rank <- personalized_pagerank_impl
 
 #' Harmonic centrality of vertices
 #'
@@ -1053,7 +1053,7 @@ page_rank <- page_rank_impl
 #' harmonic_centrality(g2, mode = "out")
 #' harmonic_centrality(g %du% make_full_graph(5), mode = "all")
 #'
-harmonic_centrality <- harmonic_centrality_impl
+harmonic_centrality <- harmonic_centrality_cutoff_impl
 
 
 

diff --git a/R/centralization.R b/R/centralization.R
@@ -86,7 +86,7 @@ NULL
 #' g1 <- make_star(10, mode = "undirected")
 #' centr_eigen(g0)$centralization
 #' centr_eigen(g1)$centralization
-centralize <- centralize_impl
+centralize <- centralization_impl
 
 #' Centralize a graph according to the degrees of vertices
 #'
@@ -119,7 +119,7 @@ centralize <- centralize_impl
 #' centr_clo(g, mode = "all")$centralization
 #' centr_betw(g, directed = FALSE)$centralization
 #' centr_eigen(g, directed = FALSE)$centralization
-centr_degree <- centr_degree_impl
+centr_degree <- centralization_degree_impl
 
 #' Theoretical maximum for degree centralization
 #'
@@ -249,7 +249,7 @@ centr_betw <- function(graph, directed = TRUE, nobigint = TRUE, normalized = TRU
 #' centr_betw(g, normalized = FALSE)$centralization %>%
 #'   `/`(centr_betw_tmax(g))
 #' centr_betw(g, normalized = TRUE)$centralization
-centr_betw_tmax <- centr_betw_tmax_impl
+centr_betw_tmax <- centralization_betweenness_tmax_impl
 
 #' Centralize a graph according to the closeness of vertices
 #'
@@ -280,7 +280,7 @@ centr_betw_tmax <- centr_betw_tmax_impl
 #' centr_clo(g, mode = "all")$centralization
 #' centr_betw(g, directed = FALSE)$centralization
 #' centr_eigen(g, directed = FALSE)$centralization
-centr_clo <- centr_clo_impl
+centr_clo <- centralization_closeness_impl
 
 #' Theoretical maximum for closeness centralization
 #'
@@ -307,7 +307,7 @@ centr_clo <- centr_clo_impl
 #' centr_clo(g, normalized = FALSE)$centralization %>%
 #'   `/`(centr_clo_tmax(g))
 #' centr_clo(g, normalized = TRUE)$centralization
-centr_clo_tmax <- centr_clo_tmax_impl
+centr_clo_tmax <- centralization_closeness_tmax_impl
 
 #' Centralize a graph according to the eigenvector centrality of vertices
 #'
@@ -349,7 +349,7 @@ centr_clo_tmax <- centr_clo_tmax_impl
 #' g1 <- make_star(10, mode = "undirected")
 #' centr_eigen(g0)$centralization
 #' centr_eigen(g1)$centralization
-centr_eigen <- centr_eigen_impl
+centr_eigen <- centralization_eigenvector_centrality_impl
 
 #' Theoretical maximum for betweenness centralization
 #'
@@ -378,4 +378,4 @@ centr_eigen <- centr_eigen_impl
 #' centr_eigen(g, normalized = FALSE)$centralization %>%
 #'   `/`(centr_eigen_tmax(g))
 #' centr_eigen(g, normalized = TRUE)$centralization
-centr_eigen_tmax <- centr_eigen_tmax_impl
+centr_eigen_tmax <- centralization_eigenvector_centrality_tmax_impl
diff --git a/R/cliques.R b/R/cliques.R
@@ -190,7 +190,7 @@ count_max_cliques <- function(graph, min = NULL, max = NULL,
 
 #' @family cliques
 #' @export
-clique_num <- clique_num_impl
+clique_num <- clique_number_impl
 
 
 #' Functions to find weighted cliques, i.e. weighted complete subgraphs in a graph
@@ -257,7 +257,7 @@ weighted_cliques <- weighted_cliques_impl
 #' @export
 largest_weighted_cliques <- largest_weighted_cliques_impl
 #' @export
-weighted_clique_num <- weighted_clique_num_impl
+weighted_clique_num <- weighted_clique_number_impl
 
 #' Independent vertex sets
 #'
@@ -402,8 +402,8 @@ ivs_size <- function(graph) {
 #' @export
 clique_size_counts <- function(graph, min = 0, max = 0, maximal = FALSE, ...) {
   if (maximal) {
-    maximal_clique_size_counts_impl(graph, min, max, ...)
+    maximal_cliques_hist_impl(graph, min, max, ...)
   } else {
-    all_clique_size_counts_impl(graph, min, max, ...)
+    clique_size_hist_impl(graph, min, max, ...)
   }
 }
diff --git a/R/coloring.R b/R/coloring.R
@@ -26,4 +26,4 @@
 #' col <- greedy_vertex_coloring(g)
 #' plot(g, vertex.color = col)
 #'
-greedy_vertex_coloring <- greedy_vertex_coloring_impl
+greedy_vertex_coloring <- vertex_coloring_greedy_impl
diff --git a/R/community.R b/R/community.R
@@ -2550,4 +2550,4 @@ communities <- groups.communities
 #'
 #' @export
 #' @family functions for manipulating graph structure
-contract <- contract_impl
+contract <- contract_vertices_impl
diff --git a/R/conversion.R b/R/conversion.R
@@ -354,7 +354,7 @@ as_edgelist <- function(graph, names = TRUE) {
 #' )
 #' print(ug4, e = TRUE)
 #'
-as.directed <- as.directed_impl
+as.directed <- to_directed_impl
 
 #' @rdname as.directed
 #' @param edge.attr.comb Specifies what to do with edge attributes, if
@@ -971,7 +971,7 @@ as_data_frame <- function(x, what = c("edges", "vertices", "both")) {
 #' which_multiple(g3)
 #' @family conversion
 #' @export
-graph_from_adj_list <- graph_from_adj_list_impl
+graph_from_adj_list <- adjlist_impl
 
 
 #' Convert a graph to a long data frame

diff --git a/R/embedding.R b/R/embedding.R
@@ -90,7 +90,7 @@
 #' embed <- embed_adjacency_matrix(RDP, 5)
 #' @family embedding
 #' @export
-embed_adjacency_matrix <- embed_adjacency_matrix_impl
+embed_adjacency_matrix <- adjacency_spectral_embedding_impl
 
 
 #' Dimensionality selection for singular values using profile likelihood.
@@ -230,7 +230,7 @@ dim_select <- dim_select_impl
 #' RDP <- sample_dot_product(lpvs)
 #' embed <- embed_laplacian_matrix(RDP, 5)
 #' @family embedding
-embed_laplacian_matrix <- embed_laplacian_matrix_impl
+embed_laplacian_matrix <- laplacian_spectral_embedding_impl
 
 
 #' Sample vectors uniformly from the surface of a sphere

diff --git a/R/flow.R b/R/flow.R
@@ -445,7 +445,7 @@ cohesion.igraph <- function(x, checks = TRUE, ...) {
 #' st_cuts(g2, source = "s", target = "t")
 #' @family flow
 #' @export
-st_cuts <- st_cuts_impl
+st_cuts <- all_st_cuts_impl
 
 
 #' List all minimum \eqn{(s,t)}-cuts of a graph
@@ -496,7 +496,7 @@ st_cuts <- st_cuts_impl
 #' st_min_cuts(g, source = "s", target = "t")
 #' @family flow
 #' @export
-st_min_cuts <- st_min_cuts_impl
+st_min_cuts <- all_st_mincuts_impl
 
 
 #' Dominator tree
@@ -616,7 +616,7 @@ dominator_tree <- function(graph, root, mode = c("out", "in", "all", "total")) {
 #' chvatal <- make_graph("chvatal")
 #' min_st_separators(chvatal)
 #' @family flow
-min_st_separators <- min_st_separators_impl
+min_st_separators <- all_minimal_st_separators_impl
 
 
 #' Maximum flow in a graph
@@ -679,7 +679,7 @@ min_st_separators <- min_st_separators_impl
 #' max_flow(g1, source = V(g1)["1"], target = V(g1)["2"])
 #' @family flow
 #' @export
-max_flow <- max_flow_impl
+max_flow <- maxflow_impl
 
 
 #' Vertex separators
@@ -758,7 +758,7 @@ is_separator <- is_separator_impl
 #'
 #' @family flow
 #' @export
-is_min_separator <- is_min_separator_impl
+is_min_separator <- is_minimal_separator_impl
 
 
 #' Minimum size vertex separators
@@ -830,4 +830,4 @@ is_min_separator <- is_min_separator_impl
 #'   John - Gery:Russ:Michael
 #' )
 #' min_separators(camp)
-min_separators <- min_separators_impl
+min_separators <- minimum_size_separators_impl
diff --git a/R/games.R b/R/games.R
@@ -1405,7 +1405,7 @@ bipartite <- function(...) constructor_spec(sample_bipartite, ...)
 #' g
 #' @family games
 #' @export
-sample_sbm <- sample_sbm_impl
+sample_sbm <- sbm_game_impl
 
 #' @rdname sample_sbm
 #' @param ... Passed to `sample_sbm()`.
@@ -1464,7 +1464,7 @@ sample_hierarchical_sbm <- function(n, m, rho, C, p) {
   commonlen <- unique(c(mlen, rholen, Clen))
 
   if (length(commonlen) == 1 && commonlen == 1) {
-    hsbm_1_game_impl(n, m, rho, C, p)
+    hsbm_game_impl(n, m, rho, C, p)
   } else {
     commonlen <- setdiff(commonlen, 1)
     if (length(commonlen) != 1) {
@@ -1536,7 +1536,7 @@ hierarchical_sbm <- function(...) {
 #' g2
 #' @family games
 #' @export
-sample_dot_product <- sample_dot_product_impl
+sample_dot_product <- dot_product_game_impl
 
 #' @rdname sample_dot_product
 #' @param ... Passed to `sample_dot_product()`.
@@ -1569,7 +1569,7 @@ dot_product <- function(...) constructor_spec(sample_dot_product, ...)
 #' @keywords graphs
 #' @family games
 #' @export
-sample_islands <- sample_islands_impl
+sample_islands <- simple_interconnected_islands_game_impl
 
 
 #' Create a random regular graph
@@ -1608,7 +1608,7 @@ sample_islands <- sample_islands_impl
 #' sapply(k10, plot, vertex.label = NA)
 #' @family games
 #' @export
-sample_k_regular <- sample_k_regular_impl
+sample_k_regular <- k_regular_game_impl
 
 
 #' Random graphs from vertex fitness scores
@@ -1670,7 +1670,7 @@ sample_k_regular <- sample_k_regular_impl
 #' g <- sample_fitness(5 * N, sample((1:50)^-2, N, replace = TRUE))
 #' degree_distribution(g)
 #' plot(degree_distribution(g, cumulative = TRUE), log = "xy")
-sample_fitness <- sample_fitness_impl
+sample_fitness <- static_fitness_game_impl
 
 
 #' Scale-free random graphs, from vertex fitness scores
@@ -1737,7 +1737,7 @@ sample_fitness <- sample_fitness_impl
 #'
 #' g <- sample_fitness_pl(10000, 30000, 2.2, 2.3)
 #' plot(degree_distribution(g, cumulative = TRUE, mode = "out"), log = "xy")
-sample_fitness_pl <- sample_fitness_pl_impl
+sample_fitness_pl <- static_power_law_game_impl
 
 
 #' Forest Fire Network Model
@@ -1795,7 +1795,7 @@ sample_fitness_pl <- sample_fitness_pl_impl
 #' dd2 <- degree_distribution(g, mode = "out")
 #' plot(seq(along.with = dd1) - 1, dd1, log = "xy")
 #' points(seq(along.with = dd2) - 1, dd2, col = 2, pch = 2)
-sample_forestfire <- sample_forestfire_impl
+sample_forestfire <- forest_fire_game_impl
 
 
 #' Generate a new random graph from a given graph by randomly
@@ -1836,7 +1836,7 @@ sample_forestfire <- sample_forestfire_impl
 #' cor(as.vector(g[]), as.vector(g2[]))
 #' g
 #' g2
-sample_correlated_gnp <- sample_correlated_gnp_impl
+sample_correlated_gnp <- correlated_game_impl
 
 
 #' Sample a pair of correlated \eqn{G(n,p)} random graphs
@@ -1875,4 +1875,4 @@ sample_correlated_gnp <- sample_correlated_gnp_impl
 #' )
 #' gg
 #' cor(as.vector(gg[[1]][]), as.vector(gg[[2]][]))
-sample_correlated_gnp_pair <- sample_correlated_gnp_pair_impl
+sample_correlated_gnp_pair <- correlated_pair_game_impl
diff --git a/R/hrg.R b/R/hrg.R
@@ -174,7 +174,7 @@ fit_hrg <- function(graph, hrg = NULL, start = FALSE, steps = 0) {
 #'     vertices. The order is the same as in the `parents` vector.}
 #' @family hierarchical random graph functions
 #' @export
-consensus_tree <- consensus_tree_impl
+consensus_tree <- hrg_consensus_impl
 
 
 #' Create a hierarchical random graph from an igraph graph
@@ -192,7 +192,7 @@ consensus_tree <- consensus_tree_impl
 #'
 #' @family hierarchical random graph functions
 #' @export
-hrg <- hrg_impl
+hrg <- hrg_create_impl
 
 
 #' Create an igraph graph from a hierarchical random graph model
@@ -205,7 +205,7 @@ hrg <- hrg_impl
 #'
 #' @family hierarchical random graph functions
 #' @export
-hrg_tree <- hrg_tree_impl
+hrg_tree <- hrg_dendrogram_impl
 
 
 #' Sample from a hierarchical random graph model
@@ -219,7 +219,7 @@ hrg_tree <- hrg_tree_impl
 #'
 #' @family hierarchical random graph functions
 #' @export
-sample_hrg <- sample_hrg_impl
+sample_hrg <- hrg_game_impl
 
 #' Predict edges based on a hierarchical random graph model
 #'

diff --git a/R/interface.R b/R/interface.R
@@ -496,10 +496,10 @@ get.edge.ids <- function(graph, vp, directed = TRUE, error = FALSE, multi = FALS
 #' g <- make_ring(10)
 #' gorder(g)
 #' vcount(g)
-gorder <- gorder_impl
+gorder <- vcount_impl
 #' @rdname gorder
 #' @export
-vcount <- gorder_impl
+vcount <- vcount_impl
 
 #' Adjacent vertices of multiple vertices in a graph
 #'

diff --git a/R/make.R b/R/make.R
@@ -1217,7 +1217,7 @@ make_tree <- function(n, children = 2, mode = c("out", "in", "undirected")) {
 #' g <- sample_tree(100, method = "lerw")
 #'
 #' @export
-sample_tree <- sample_tree_impl
+sample_tree <- tree_game_impl
 
 #' @rdname make_tree
 #' @param ... Passed to `make_tree()` or `sample_tree()`.
@@ -1249,7 +1249,7 @@ tree <- function(...) constructor_spec(list(make = make_tree, sample = sample_tr
 #' to_prufer(g)
 #' @family trees
 #' @export
-make_from_prufer <- make_from_prufer_impl
+make_from_prufer <- from_prufer_impl
 
 #' @rdname make_from_prufer
 #' @param ... Passed to `make_from_prufer()`
@@ -1698,7 +1698,7 @@ full_citation_graph <- function(...) constructor_spec(make_full_citation_graph,
 #' g2 <- make_graph("Franklin")
 #' isomorphic(g1, g2)
 #' @export
-graph_from_lcf <- graph_from_lcf_impl
+graph_from_lcf <- lcf_vector_impl
 
 ## -----------------------------------------------------------------
 
@@ -1791,4 +1791,4 @@ graph_from_lcf <- graph_from_lcf_impl
 #' }
 #' g5 <- realize_degseq(degs, allowed.edge.types = "multi")
 #' all(degree(g5) == degs)
-realize_degseq <- realize_degseq_impl
+realize_degseq <- realize_degree_sequence_impl