diff --git a/R/degseq.R b/R/degseq.R
index dd13def53d..02992b3b1f 100644
--- a/R/degseq.R
+++ b/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{szhorvat@gmail.com}
-#' @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{szhorvat@gmail.com}
+#' @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
diff --git a/R/games.R b/R/games.R
index 96d23fed0b..49e53b4393 100644
--- a/R/games.R
+++ b/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
diff --git a/R/igraph-package.R b/R/igraph-package.R
index 07e28f7531..a342371866 100644
--- a/R/igraph-package.R
+++ b/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()]).
diff --git a/man/aaa-igraph-package.Rd b/man/aaa-igraph-package.Rd
index 512cec4617..6b25c86dac 100644
--- a/man/aaa-igraph-package.Rd
+++ b/man/aaa-igraph-package.Rd
@@ -70,7 +70,7 @@ To create graphs from field data, \code{\link[=graph_from_edgelist]{graph_from_e
 probably the best choices.
 
 The igraph package includes some classic random graphs like the
-Erdos-Renyi GNP and GNM graphs (\code{\link[=sample_gnp]{sample_gnp()}}, \code{\link[=sample_gnm]{sample_gnm()}}) and
+Erdős-Rényi GNP and GNM graphs (\code{\link[=sample_gnp]{sample_gnp()}}, \code{\link[=sample_gnm]{sample_gnm()}}) and
 some recent  popular models, like preferential attachment
 (\code{\link[=sample_pa]{sample_pa()}}) and the small-world model
 (\code{\link[=sample_smallworld]{sample_smallworld()}}).
diff --git a/man/is_degseq.Rd b/man/is_degseq.Rd
index 0e5d8708b1..da1c352a91 100644
--- a/man/is_degseq.Rd
+++ b/man/is_degseq.Rd
@@ -31,8 +31,8 @@ is_degseq(degree(g))
 is_graphical(degree(g))
 }
 \references{
-Z Kiraly, Recognizing graphic degree sequences and generating
-all realizations. TR-2011-11, Egervary Research Group, H-1117, Budapest,
+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, \emph{Comput. Sci. Eng.} 17, 91 (2015).
@@ -46,7 +46,7 @@ Other graphical degree sequences:
 \code{\link{is_graphical}()}
 }
 \author{
-Tamas Nepusz \email{ntamas@gmail.com} and Szabolcs Horvat \email{szhorvat@gmail.com}
+Tamás Nepusz \email{ntamas@gmail.com} and Szabolcs Horvát \email{szhorvat@gmail.com}
 }
 \concept{graphical degree sequences}
 \keyword{graphs}
diff --git a/man/is_graphical.Rd b/man/is_graphical.Rd
index bdbdc81464..f963422e9c 100644
--- a/man/is_graphical.Rd
+++ b/man/is_graphical.Rd
@@ -45,7 +45,7 @@ is_graphical(degree(g))
 Hakimi SL: On the realizability of a set of integers as degrees
 of the vertices of a simple graph. \emph{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.  \emph{The Electronic
 Journal of Combinatorics} 17(1):R66, 2010.
 }
@@ -54,7 +54,7 @@ Other graphical degree sequences:
 \code{\link{is_degseq}()}
 }
 \author{
-Tamas Nepusz \email{ntamas@gmail.com}
+Tamás Nepusz \email{ntamas@gmail.com}
 }
 \concept{graphical degree sequences}
 \keyword{graphs}
diff --git a/man/sample_bipartite.Rd b/man/sample_bipartite.Rd
index 3af55bc085..2dce0355e9 100644
--- a/man/sample_bipartite.Rd
+++ b/man/sample_bipartite.Rd
@@ -48,7 +48,7 @@ is ignored for undirected graphs.}
 A bipartite igraph graph.
 }
 \description{
-Generate bipartite graphs using the Erdos-Renyi model
+Generate bipartite graphs using the Erdős-Rényi model
 }
 \details{
 Similarly to unipartite (one-mode) networks, we can define the \eqn{G(n,p)}, and
diff --git a/man/sample_correlated_gnp.Rd b/man/sample_correlated_gnp.Rd
index afd73d3bb9..06e64fa5eb 100644
--- a/man/sample_correlated_gnp.Rd
+++ b/man/sample_correlated_gnp.Rd
@@ -21,8 +21,8 @@ graph (the adjacency matrix being used as a vector).}
 
 \item{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.}
 
 \item{permutation}{A numeric vector, a permutation vector that is
@@ -51,7 +51,7 @@ g2
 }
 \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.
 \url{https://arxiv.org/abs/1304.7844}
 }
 \seealso{
diff --git a/man/sample_correlated_gnp_pair.Rd b/man/sample_correlated_gnp_pair.Rd
index 98ef5cc1c3..da95e5c7c3 100644
--- a/man/sample_correlated_gnp_pair.Rd
+++ b/man/sample_correlated_gnp_pair.Rd
@@ -44,7 +44,7 @@ cor(as.vector(gg[[1]][]), as.vector(gg[[2]][]))
 }
 \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.
 \url{https://arxiv.org/abs/1304.7844}
 }
 \seealso{
diff --git a/man/sample_islands.Rd b/man/sample_islands.Rd
index a3e70db73d..902967f8f0 100644
--- a/man/sample_islands.Rd
+++ b/man/sample_islands.Rd
@@ -20,7 +20,7 @@ island.}
 An igraph graph.
 }
 \description{
-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}{
diff --git a/man/sample_pa.Rd b/man/sample_pa.Rd
index d7945e8a0e..e051038bf8 100644
--- a/man/sample_pa.Rd
+++ b/man/sample_pa.Rd
@@ -119,7 +119,7 @@ degree_distribution(g)
 
 }
 \references{
-Barabasi, A.-L. and Albert R. 1999. Emergence of scaling in
+Barabási, A.-L. and Albert R. 1999. Emergence of scaling in
 random networks \emph{Science}, 286 509--512.
 
 de Solla Price, D. J. 1965. Networks of Scientific Papers \emph{Science},