removed deprecated igraph functions

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: netrankr
 Type: Package
 Title: Analyzing Partial Rankings in Networks
-Version: 1.2.3
+Version: 1.2.3.9000
 Authors@R: c(
     person("David", "Schoch", email = "david@schochastics.net", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2952-4812")),

R/approximate.ranks.R

@@ -49,11 +49,11 @@ approx_rank_expected <- function(P, method = "lpom") {
     if (length(MSE) >= 1) {
         MSE <- t(apply(MSE, 1, sort))
         MSE <- MSE[!duplicated(MSE), ]
-        g <- igraph::graph.empty()
-        g <- igraph::add.vertices(g, nrow(P))
-        g <- igraph::add.edges(g, c(t(MSE)))
-        g <- igraph::as.undirected(g)
-        MSE <- igraph::clusters(g)$membership
+        g <- igraph::make_empty_graph()
+        g <- igraph::add_vertices(g, nrow(P))
+        g <- igraph::add_edges(g, c(t(MSE)))
+        g <- igraph::as_undirected(g)
+        MSE <- igraph::components(g)$membership
         equi <- which(duplicated(MSE))
         P <- P[-equi, -equi]
     } else {
@@ -168,11 +168,11 @@ approx_rank_relative <- function(P, iterative = TRUE, num.iter = 10) {
     if (length(MSE) >= 1) {
         MSE <- t(apply(MSE, 1, sort))
         MSE <- MSE[!duplicated(MSE), ]
-        g <- igraph::graph.empty()
-        g <- igraph::add.vertices(g, nrow(P))
-        g <- igraph::add.edges(g, c(t(MSE)))
-        g <- igraph::as.undirected(g)
-        MSE <- igraph::clusters(g)$membership
+        g <- igraph::make_empty_graph()
+        g <- igraph::add_vertices(g, nrow(P))
+        g <- igraph::add_edges(g, c(t(MSE)))
+        g <- igraph::as_undirected(g)
+        MSE <- igraph::components(g)$membership
         equi <- which(duplicated(MSE))
         P <- P[-equi, -equi]
     } else {

R/comparable.pairs.R

@@ -22,7 +22,7 @@ comparable_pairs <- function(P) {
     if (!is.binary(P)) {
         stop("P is not a binary matrix")
     }
-    igraph::graph.density(igraph::graph_from_adjacency_matrix(P, "max"))
+    igraph::edge_density(igraph::graph_from_adjacency_matrix(P, "max"))
 }
 
 #' @title Incomparable pairs in a partial order
@@ -49,5 +49,5 @@ incomparable_pairs <- function(P) {
     if (!is.binary(P)) {
         stop("P is not a binary matrix")
     }
-    igraph::graph.density(igraph::graph.complementer(igraph::graph_from_adjacency_matrix(P, "max")))
+    igraph::edge_density(igraph::complementer(igraph::graph_from_adjacency_matrix(P, "max")))
 }

R/hyperbolic.index.R

@@ -23,23 +23,23 @@ hyperbolic_index <- function(g, type = "odd") {
         ENW <- rep(0, n)
         for (v in 1:n) {
             Nv <- igraph::neighborhood(g, 1, v)[[1]]
-            g1 <- igraph::induced.subgraph(g, Nv)
-            C <- igraph::get.adjacency(g1, type = "both")
+            g1 <- igraph::induced_subgraph(g, Nv)
+            C <- igraph::as_adjacency_matrix(g1, type = "both")
             eig.decomp <- eigen(C, symmetric = TRUE)
             V <- (eig.decomp$vectors)^2
             lambda <- eig.decomp$values
-            ENW[v] <- sum(V %*% cosh(lambda)) * igraph::graph.density(g1) # cosh(x)
+            ENW[v] <- sum(V %*% cosh(lambda)) * igraph::edge_density(g1) # cosh(x)
         }
     } else if (type == "odd") {
         ENW <- rep(0, n)
         for (v in 1:n) {
             Nv <- igraph::neighborhood(g, 1, v)[[1]]
-            g1 <- igraph::induced.subgraph(g, Nv)
-            C <- igraph::get.adjacency(g1, type = "both")
+            g1 <- igraph::induced_subgraph(g, Nv)
+            C <- igraph::as_adjacency_matrix(g1, type = "both")
             eig.decomp <- eigen(C, symmetric = TRUE)
             V <- (eig.decomp$vectors)^2
             lambda <- eig.decomp$values
-            ENW[v] <- sum(V %*% sinh(lambda)) * igraph::graph.density(g1) # sinh(x)
+            ENW[v] <- sum(V %*% sinh(lambda)) * igraph::edge_density(g1) # sinh(x)
         }
     } else {
         stop("type must be even or odd")

R/indirect.relations.R

@@ -160,28 +160,28 @@ indirect_relations <- function(g,
     } else if (type == "weights") {
         if (is.null(igraph::get.edge.attribute(g, "weight"))) {
             warning('no weight attribute present. using "adjacency" instead.\n')
-            rel <- igraph::get.adjacency(g, type = "both", sparse = FALSE, attr = NULL)
+            rel <- igraph::as_adjacency_matrix(g, type = "both", sparse = FALSE, attr = NULL)
             rel <- FUN(rel, ...)
             diag(rel) <- 0
         } else {
-            rel <- igraph::get.adjacency(g, type = "both", sparse = FALSE, attr = "weight")
+            rel <- igraph::as_adjacency_matrix(g, type = "both", sparse = FALSE, attr = "weight")
             rel <- FUN(rel, ...)
             diag(rel) <- 0
         }
     } else if (type == "adjacency") {
-        rel <- igraph::get.adjacency(g, type = "both", sparse = FALSE, attr = NULL)
+        rel <- igraph::as_adjacency_matrix(g, type = "both", sparse = FALSE, attr = NULL)
         rel <- FUN(rel, ...)
         diag(rel) <- 0
     } else if (type == "depend_sp") {
-        adj <- lapply(igraph::get.adjlist(g), function(x) x - 1)
+        adj <- lapply(igraph::as_adj_list(g), function(x) x - 1)
         rel <- dependency(adj)
     } else if (type == "walks") {
-        eigen.decomp <- eigen(igraph::get.adjacency(g, type = "both"))
+        eigen.decomp <- eigen(igraph::as_adjacency_matrix(g, type = "both"))
         lambda <- eigen.decomp$values
         X <- eigen.decomp$vectors
         rel <- X %*% diag(FUN(lambda, ...)) %*% t(X)
     } else if (type == "dist_resist") {
-        L <- igraph::graph.laplacian(g, sparse = FALSE)
+        L <- igraph::laplacian_matrix(g, sparse = FALSE)
         n <- igraph::vcount(g)
         A <- L + matrix(1 / n, n, n)
         C <- solve(A)
@@ -246,7 +246,7 @@ log_forest_fct <- function(g, lfparam) {
     n <- igraph::vcount(g)
     gamma <- log(exp(1) + lfparam^(2 / n))
 
-    L <- igraph::graph.laplacian(g, sparse = FALSE)
+    L <- igraph::laplacian_matrix(g, sparse = FALSE)
     I <- diag(1, n)
     Q <- solve(I + lfparam * L)
 
@@ -266,7 +266,7 @@ depend_netflow_fct <- function(g, netflowmode) {
     for (s in 1:n) {
         for (t in 1:n) {
             if (s != t) {
-                mflow[s, t] <- igraph::graph.maxflow(g, s, t)$value
+                mflow[s, t] <- igraph::max_flow(g, s, t)$value
             }
         }
     }
@@ -278,11 +278,11 @@ depend_netflow_fct <- function(g, netflowmode) {
     }
     flow_smat <- matrix(0, n, n)
     for (i in 1:n) {
-        g_i <- igraph::delete.vertices(g, i)
+        g_i <- igraph::delete_vertices(g, i)
         for (s in 1:n) {
             for (t in 1:n) {
                 if (i != s & s != t & i != t) {
-                    flow <- igraph::graph.maxflow(g_i, s - (s > i), t - (t > i))$value
+                    flow <- igraph::max_flow(g_i, s - (s > i), t - (t > i))$value
                     flow_smat[i, s] <- switch(netflowmode,
                         raw = flow_smat[i, s] + mflow[s, t] - flow,
                         norm = flow_smat[i, s] + mflow[s, t] - flow,
@@ -299,7 +299,7 @@ depend_netflow_fct <- function(g, netflowmode) {
 }
 
 depend_exp_fct <- function(g) {
-    A <- igraph::get.adjacency(g, "both", sparse = FALSE)
+    A <- igraph::as_adjacency_matrix(g, "both", sparse = FALSE)
     eigen_A <- eigen(A)
     n <- nrow(A)
     expA <- eigen_A$vectors %*% diag(exp(eigen_A$values)) %*% t(eigen_A$vectors)
@@ -326,7 +326,7 @@ depend_rsps_fct <- function(g, rspxparam) {
     n <- igraph::vcount(g)
     I <- diag(1, n)
 
-    A <- igraph::get.adjacency(g, sparse = FALSE)
+    A <- igraph::as_adjacency_matrix(g, sparse = FALSE)
     D <- diag(1 / igraph::degree(g))
     P_ref <- D %*% A
     C <- 1 / A
@@ -347,7 +347,7 @@ depend_rspn_fct <- function(g, rspxparam) {
     n <- igraph::vcount(g)
     I <- diag(1, n)
 
-    A <- igraph::get.adjacency(g, sparse = FALSE)
+    A <- igraph::as_adjacency_matrix(g, sparse = FALSE)
     D <- diag(1 / igraph::degree(g))
     P_ref <- D %*% A
     C <- 1 / A
@@ -356,7 +356,7 @@ depend_rspn_fct <- function(g, rspxparam) {
 
     Z <- solve((I - W), I)
     Zdiv <- 1 / Z
-    adj <- igraph::get.adjlist(g, "all")
+    adj <- igraph::as_adj_list(g, "all")
     A <- lapply(adj, function(x) as.vector(x) - 1)
     bet.mat <- dependRspn(A, Z, Zdiv, W, n)
     return(bet.mat)
@@ -365,13 +365,13 @@ depend_rspn_fct <- function(g, rspxparam) {
 depend_curflow_fct <- function(g) {
     n <- igraph::vcount(g)
     D <- diag(igraph::degree(g))
-    A <- igraph::get.adjacency(g, sparse = FALSE)
+    A <- igraph::as_adjacency_matrix(g, sparse = FALSE)
     L <- D - A
 
     Tmat <- solve(L[1:(n - 1), 1:(n - 1)])
     Tmat <- rbind(cbind(Tmat, 0), 0)
 
-    el <- igraph::get.edgelist(g, names = FALSE)
+    el <- igraph::as_edgelist(g, names = FALSE)
     m <- igraph::ecount(g)
     el <- el - 1L
 
@@ -381,7 +381,7 @@ depend_curflow_fct <- function(g) {
 
 dist_rwalk_fct <- function(g) {
     n <- igraph::vcount(g)
-    A <- igraph::get.adjacency(g, sparse = FALSE)
+    A <- igraph::as_adjacency_matrix(g, sparse = FALSE)
     M <- A / rowSums(A)
     e <- rep(1, n - 1)
     H <- matrix(0, n, n)
@@ -395,7 +395,7 @@ dist_rwalk_fct <- function(g) {
 
 dist_walk_fct <- function(g, dwparam) {
     n <- igraph::vcount(g)
-    A <- igraph::get.adjacency(g, sparse = FALSE)
+    A <- igraph::as_adjacency_matrix(g, sparse = FALSE)
     bigeig <- eigen(A, only.values = TRUE)$values[1]
     t <- (1 / dwparam + bigeig)^(-1)
     # if(dwparam > (1/bigeig)){

R/majorization.gap.R

@@ -23,7 +23,7 @@
 #'
 #' @examples
 #' library(igraph)
-#' g <- graph.star(5, "undirected")
+#' g <- make_star(5, "undirected")
 #' majorization_gap(g) # 0 since star graphs are threshold graphs
 #'
 #' g <- sample_gnp(100, 0.15)

R/mcmc.rank.R

@@ -47,11 +47,11 @@ mcmc_rank_prob <- function(P, rp = nrow(P)^3) {
     if (length(MSE) >= 1) {
         MSE <- t(apply(MSE, 1, sort))
         MSE <- MSE[!duplicated(MSE), ]
-        g <- igraph::graph.empty()
-        g <- igraph::add.vertices(g, nrow(P))
-        g <- igraph::add.edges(g, c(t(MSE)))
-        g <- igraph::as.undirected(g)
-        MSE <- igraph::clusters(g)$membership
+        g <- igraph::make_empty_graph()
+        g <- igraph::add_vertices(g, nrow(P))
+        g <- igraph::add_edges(g, c(t(MSE)))
+        g <- igraph::as_undirected(g)
+        MSE <- igraph::components(g)$membership
         equi <- which(duplicated(MSE))
         P <- P[-equi, -equi]
     } else {
@@ -61,7 +61,7 @@ mcmc_rank_prob <- function(P, rp = nrow(P)^3) {
         stop("all elements are structurally equivalent and have the same rank")
     }
 
-    init.rank <- as.vector(igraph::topological.sort(igraph::graph_from_adjacency_matrix(P, "directed")))
+    init.rank <- as.vector(igraph::topo_sort(igraph::graph_from_adjacency_matrix(P, "directed")))
     if (inherits(P, "Matrix")) {
         res <- mcmc_rank_sparse(P, init.rank - 1, rp)
     } else {

R/neighborhood.inclusion.R

@@ -27,7 +27,7 @@
 #' @examples
 #' library(igraph)
 #' # the neighborhood inclusion preorder of a star graph is complete
-#' g <- graph.star(5, "undirected")
+#' g <- make_star(5, "undirected")
 #' P <- neighborhood_inclusion(g)
 #' comparable_pairs(P)
 #'

R/rank.analysis.R

@@ -73,11 +73,11 @@ exact_rank_prob <- function(P, only.results = TRUE, verbose = FALSE, force = FAL
     if (length(MSE) >= 1) {
         MSE <- t(apply(MSE, 1, sort))
         MSE <- MSE[!duplicated(MSE), ]
-        g <- igraph::graph.empty()
-        g <- igraph::add.vertices(g, nrow(P))
-        g <- igraph::add.edges(g, c(t(MSE)))
-        g <- igraph::as.undirected(g)
-        MSE <- igraph::clusters(g)$membership
+        g <- igraph::make_empty_graph()
+        g <- igraph::add_vertices(g, nrow(P))
+        g <- igraph::add_edges(g, c(t(MSE)))
+        g <- igraph::as_undirected(g)
+        MSE <- igraph::components(g)$membership
         equi <- which(duplicated(MSE))
         P <- P[-equi, -equi]
     } else {
@@ -126,13 +126,13 @@ exact_rank_prob <- function(P, only.results = TRUE, verbose = FALSE, force = FAL
         stop("Input data too big. Use approximations or set `force=TRUE` if you know what you are doing")
     }
     # Prepare Data structures---------------------
-    topo.order <- as.vector(igraph::topological.sort(igraph::graph_from_adjacency_matrix(P, "directed")))
+    topo.order <- as.vector(igraph::topo_sort(igraph::graph_from_adjacency_matrix(P, "directed")))
 
     P <- P[topo.order, topo.order]
-    ImPred <- igraph::get.adjlist(igraph::graph_from_adjacency_matrix(P, "directed"), "in")
+    ImPred <- igraph::as_adj_list(igraph::graph_from_adjacency_matrix(P, "directed"), "in")
     ImPred <- lapply(ImPred, function(x) as.vector(x) - 1)
 
-    ImSucc <- igraph::get.adjlist(igraph::graph_from_adjacency_matrix(P, "directed"), "out")
+    ImSucc <- igraph::as_adj_list(igraph::graph_from_adjacency_matrix(P, "directed"), "out")
     ImSucc <- lapply(ImSucc, function(x) as.vector(x) - 1)
     # TREEOFIDEALS ----------------------------------------------------
     if (verbose == TRUE) {

R/spectral.gap.R

@@ -16,7 +16,7 @@
 #' @export
 #'
 spectral_gap <- function(g, method = "frac") {
-    spec_decomp <- eigen(igraph::get.adjacency(g, "both"))$values[c(1, 2)]
+    spec_decomp <- eigen(igraph::as_adjacency_matrix(g, "both"))$values[c(1, 2)]
     if (method == "frac") {
         return(1 - spec_decomp[2] / spec_decomp[1])
     } else if (method == "abs") {

tests/testthat/test-hyperbolic.index.R

@@ -1,7 +1,7 @@
 test_that("hyperbolic index works", {
-    skip_on_cran()
-    g <- igraph::make_full_graph(2)
-    expect_true(all(hyperbolic_index(g, "odd") == 0))
-    expect_true(all(hyperbolic_index(g, "even") != 0))
-    expect_error(hyperbolic_index(g, "wrong"))
+  skip_on_cran()
+  g <- igraph::make_full_graph(2)
+  expect_true(all(round(hyperbolic_index(g, "odd"), 6) == 0))
+  expect_true(all(hyperbolic_index(g, "even") != 0))
+  expect_error(hyperbolic_index(g, "wrong"))
 })

tests/testthat/test_dominance.R

@@ -23,7 +23,7 @@ test_that("neighborhood-inclusion is correct", {
 })
 
 test_that("positional dominance is correct", {
-    g <- graph.empty(n = 11, directed = FALSE)
+    g <- make_empty_graph(n = 11, directed = FALSE)
     g <- add_edges(g, c(
         1, 11, 2, 4, 3, 5, 3, 11, 4, 8, 5, 9, 5, 11, 6, 7, 6, 8,
         6, 10, 6, 11, 7, 9, 7, 10, 7, 11, 8, 9, 8, 10, 9, 10
@@ -61,7 +61,7 @@ test_that("dominance_graph is correct", {
     library(igraph)
     library(magrittr)
 
-    g <- graph.empty(n = 11, directed = FALSE)
+    g <- make_empty_graph(n = 11, directed = FALSE)
     g <- add_edges(g, c(
         1, 11, 2, 4, 3, 5, 3, 11, 4, 8, 5, 9, 5, 11, 6, 7, 6, 8,
         6, 10, 6, 11, 7, 9, 7, 10, 7, 11, 8, 9, 8, 10, 9, 10

tests/testthat/test_helpers.R

@@ -3,7 +3,7 @@ library(igraph)
 library(magrittr)
 library(Matrix)
 test_that("majorization gap is correct", {
-    g <- graph.empty(n = 11, directed = FALSE)
+    g <- make_empty_graph(n = 11, directed = FALSE)
     g <- add_edges(g, c(
         1, 11, 2, 4, 3, 5, 3, 11, 4, 8, 5, 9, 5, 11, 6, 7, 6, 8,
         6, 10, 6, 11, 7, 9, 7, 10, 7, 11, 8, 9, 8, 10, 9, 10
@@ -16,12 +16,12 @@ test_that("majorization gap is correct", {
     expect_equal(round(majorization_gap(tg), 7), 0)
     expect_equal(majorization_gap(tg, norm = FALSE), 0)
     expect_error(majorization_gap("a"))
-    expect_error(majorization_gap(igraph::graph.full(2, directed = TRUE)))
-    expect_warning(majorization_gap(igraph::graph.full(3) + igraph::graph.full(3)))
+    expect_error(majorization_gap(igraph::make_full_graph(2, directed = TRUE)))
+    expect_warning(majorization_gap(igraph::make_full_graph(3) + igraph::make_full_graph(3)))
 })
 
 test_that("spectral gap is correct", {
-    g <- graph.star(10, "undirected")
+    g <- make_star(10, "undirected")
 
     expect_equal(spectral_gap(g, method = "frac"), 1)
     expect_equal(spectral_gap(g, method = "abs"), 3)
@@ -50,7 +50,7 @@ test_that("transitive_reduction is correct", {
 
 test_that("is_preserved is correct", {
     library(igraph)
-    g <- graph.empty(n = 11, directed = FALSE)
+    g <- make_empty_graph(n = 11, directed = FALSE)
     g <- add_edges(g, c(
         1, 11, 2, 4, 3, 5, 3, 11, 4, 8, 5, 9, 5, 11, 6, 7, 6, 8,
         6, 10, 6, 11, 7, 9, 7, 10, 7, 11, 8, 9, 8, 10, 9, 10