docs: add some missing diacritics

R/degseq.R

@@ -68,9 +68,9 @@ is.degree.sequence <- function(out.deg, in.deg = NULL) { # nocov start
 #' @param in.deg `NULL` or an integer vector. For undirected graphs, it
 #'   should be `NULL`. For directed graphs it specifies the in-degrees.
 #' @return A logical scalar.
-#' @author Tamas Nepusz \email{ntamas@@gmail.com} and Szabolcs Horvat \email{[email protected]}
-#' @references Z Kiraly, Recognizing graphic degree sequences and generating
-#' all realizations. TR-2011-11, Egervary Research Group, H-1117, Budapest,
+#' @author Tamás Nepusz \email{ntamas@@gmail.com} and Szabolcs Horvát \email{[email protected]}
+#' @references Z Király, Recognizing graphic degree sequences and generating
+#' all realizations. TR-2011-11, Egerváry Research Group, H-1117, Budapest,
 #' Hungary. ISSN 1587-4451 (2012).
 #'
 #' B. Cloteaux, Is This for Real? Fast Graphicality Testing, *Comput. Sci. Eng.* 17, 91 (2015).
@@ -111,11 +111,11 @@ is_degseq <- function(out.deg, in.deg = NULL) {
 #'   are not. \sQuote{all} means that both loop edges and multiple edges are
 #'   allowed.
 #' @return A logical scalar.
-#' @author Tamas Nepusz \email{ntamas@@gmail.com}
+#' @author Tamás Nepusz \email{ntamas@@gmail.com}
 #' @references Hakimi SL: On the realizability of a set of integers as degrees
 #' of the vertices of a simple graph. *J SIAM Appl Math* 10:496-506, 1962.
 #'
-#' PL Erdos, I Miklos and Z Toroczkai: A simple Havel-Hakimi type algorithm to
+#' PL Erdős, I Miklós and Z Toroczkai: A simple Havel-Hakimi type algorithm to
 #' realize graphical degree sequences of directed graphs.  *The Electronic
 #' Journal of Combinatorics* 17(1):R66, 2010.
 #' @keywords graphs

R/games.R

@@ -459,7 +459,7 @@ aging.prefatt.game <- function(n, pa.exp, aging.exp, m = NULL, aging.bin = 300,
 #'   `start.graph`.
 #' @return A graph object.
 #' @author Gabor Csardi \email{csardi.gabor@@gmail.com}
-#' @references Barabasi, A.-L. and Albert R. 1999. Emergence of scaling in
+#' @references Barabási, A.-L. and Albert R. 1999. Emergence of scaling in
 #' random networks *Science*, 286 509--512.
 #'
 #' de Solla Price, D. J. 1965. Networks of Scientific Papers *Science*,
@@ -1604,7 +1604,7 @@ cit_cit_types <- function(...) constructor_spec(sample_cit_cit_types, ...)
 
 #' Bipartite random graphs
 #'
-#' Generate bipartite graphs using the Erdos-Renyi model
+#' Generate bipartite graphs using the Erdős-Rényi model
 #'
 #' Similarly to unipartite (one-mode) networks, we can define the \eqn{G(n,p)}, and
 #' \eqn{G(n,m)} graph classes for bipartite graphs, via their generating process.
@@ -1871,7 +1871,7 @@ dot_product <- function(...) constructor_spec(sample_dot_product, ...)
 
 #' A graph with subgraphs that are each a random graph.
 #'
-#' Create a number of Erdos-Renyi random graphs with identical parameters, and
+#' Create a number of Erdős-Rényi random graphs with identical parameters, and
 #' connect them with the specified number of edges.
 #'
 #' @section Examples:
@@ -2131,8 +2131,8 @@ sample_forestfire <- forest_fire_game_impl
 #'   graph (the adjacency matrix being used as a vector).
 #' @param p A numeric scalar, the probability of an edge between two
 #'   vertices, it must in the open (0,1) interval. The default is the empirical
-#'   edge density of the graph. If you are resampling an Erdos-Renyi graph and
-#'   you know the original edge probability of the Erdos-Renyi model, you should
+#'   edge density of the graph. If you are resampling an Erdős-Rényi graph and
+#'   you know the original edge probability of the Erdős-Rényi model, you should
 #'   supply that explicitly.
 #' @param permutation A numeric vector, a permutation vector that is
 #'   applied on the vertices of the first graph, to get the second graph.  If
@@ -2143,7 +2143,7 @@ sample_forestfire <- forest_fire_game_impl
 #'   matrix entries is a pair of correlated Bernoulli random variables.
 #'
 #' @references Lyzinski, V., Fishkind, D. E., Priebe, C. E. (2013).  Seeded
-#' graph matching for correlated Erdos-Renyi graphs.
+#' graph matching for correlated Erdős-Rényi graphs.
 #' <https://arxiv.org/abs/1304.7844>
 #' @family games
 #' @export
@@ -2178,7 +2178,7 @@ sample_correlated_gnp <- correlated_game_impl
 #'   correlated with `corr`.
 #'
 #' @references Lyzinski, V., Fishkind, D. E., Priebe, C. E. (2013).  Seeded
-#' graph matching for correlated Erdos-Renyi graphs.
+#' graph matching for correlated Erdős-Rényi graphs.
 #' <https://arxiv.org/abs/1304.7844>
 #' @keywords graphs
 #' @family games

R/igraph-package.R

@@ -99,7 +99,7 @@ NULL
 #'   probably the best choices.
 #'
 #'   The igraph package includes some classic random graphs like the
-#'   Erdos-Renyi GNP and GNM graphs ([sample_gnp()], [sample_gnm()]) and
+#'   Erdős-Rényi GNP and GNM graphs ([sample_gnp()], [sample_gnm()]) and
 #'   some recent  popular models, like preferential attachment
 #'   ([sample_pa()]) and the small-world model
 #'   ([sample_smallworld()]).