docs: describe logical params more consistently

R/adjacency.R

@@ -149,7 +149,7 @@ graph.adjacency <- function(
 #'   edge attribute named by the `weighted` argument. If it is `TRUE`
 #'   then a weighted graph is created and the name of the edge attribute will be
 #'   `weight`. See also details below.
-#' @param diag Logical scalar, whether to include the diagonal of the matrix in
+#' @param diag Logical, whether to include the diagonal of the matrix in
 #'   the calculation. If this is `FALSE` then the diagonal is zerod out
 #'   first.
 #' @param add.colnames Character scalar, whether to add the column names as

R/assortativity.R

@@ -130,14 +130,14 @@ assortativity.degree <- function(graph, directed = TRUE) {
 #'   This
 #'   argument is ignored (with a warning) if it is not `NULL` and undirected
 #'   assortativity coefficient is being calculated.
-#' @param directed Logical scalar, whether to consider edge directions for
+#' @param directed Logical, whether to consider edge directions for
 #'   directed graphs.
 #'   This argument is ignored for undirected graphs.
 #'   Supply
 #'   `TRUE` here to do the natural thing, i.e. use directed version of the
 #'   measure for directed graphs and the undirected version for undirected
 #'   graphs.
-#' @param normalized Boolean, whether to compute the normalized assortativity.
+#' @param normalized Logical, whether to compute the normalized assortativity.
 #' The non-normalized nominal assortativity is identical to modularity.
 #' The non-normalized value-based assortativity is simply the covariance of the
 #' values at the two ends of edges.

R/bipartite.R

@@ -129,7 +129,7 @@ bipartite.mapping <- function(graph) {
 #'   one projection is requested in argument `which`.
 #' @param which A character scalar to specify which projection(s) to calculate.
 #'   The default is to calculate both.
-#' @param remove.type Logical scalar, whether to remove the `type` vertex
+#' @param remove.type Logical, whether to remove the `type` vertex
 #'   attribute from the projections. This makes sense because these graphs are
 #'   not bipartite any more. However if you want to combine them with each other
 #'   (or other bipartite graphs), then it is worth keeping this attribute. By

R/centrality.R

@@ -388,7 +388,7 @@ betweenness.estimate <- estimate_betweenness
 #'   betweenness. If the graph has a `weight` edge attribute, then this is
 #'   used by default. Weights are used to calculate weighted shortest paths,
 #'   so they are interpreted as distances.
-#' @param normalized Logical scalar, whether to normalize the betweenness
+#' @param normalized Logical, whether to normalize the betweenness
 #'   scores. If `TRUE`, then the results are normalized by the number of ordered
 #'   or unordered vertex pairs in directed and undirected graphs, respectively.
 #'   In an undirected graph,
@@ -540,7 +540,7 @@ edge.betweenness.estimate <- estimate_edge_betweenness
 #'   *to* a vertex, \dQuote{out} measures paths *from* a vertex,
 #'   *all* uses undirected paths. This argument is ignored for undirected
 #'   graphs.
-#' @param normalized Logical scalar, whether to calculate the normalized
+#' @param normalized Logical, whether to calculate the normalized
 #'   closeness, i.e. the inverse average distance to all reachable vertices.
 #'   The non-normalized closeness is the inverse of the sum of distances to
 #'   all reachable vertices.
@@ -854,7 +854,7 @@ arpack_defaults <- function() {
 #'   \dQuote{input matrix}. (The input matrix is never given explicitly.) The
 #'   second argument is `extra`.
 #' @param extra Extra argument to supply to `func`.
-#' @param sym Logical scalar, whether the input matrix is symmetric. Always
+#' @param sym Logical, whether the input matrix is symmetric. Always
 #'   supply `TRUE` here if it is, since it can speed up the computation.
 #' @param options Options to ARPACK, a named list to overwrite some of the
 #'   default option values. See details below.
@@ -1041,7 +1041,7 @@ arpack.unpack.complex <- function(vectors, values, nev) {
 #' effectively means that the measure can only be calculated for small graphs.
 #'
 #' @param graph The input graph. It will be treated as undirected.
-#' @param diag Boolean scalar, whether to include the diagonal of the adjacency
+#' @param diag Logical, whether to include the diagonal of the adjacency
 #'   matrix in the analysis. Giving `FALSE` here effectively eliminates the
 #'   loops edges from the graph before the calculation.
 #' @return A numeric vector, the subgraph centrality scores of the vertices.
@@ -1239,7 +1239,7 @@ eigen_defaults <- function() {
 #' computation, see [arpack()] for more about ARPACK in igraph.
 #'
 #' @param graph Graph to be analyzed.
-#' @param directed Logical scalar, whether to consider direction of the edges
+#' @param directed Logical, whether to consider direction of the edges
 #'   in directed graphs. It is ignored for undirected graphs.
 #' @param scale `r lifecycle::badge("deprecated")` Normalization will always take
 #' place.
@@ -1441,7 +1441,7 @@ diversity <- function(graph, weights = NULL, vids = V(graph)) {
 #' scores are the same as authority scores.
 #'
 #' @param graph The input graph.
-#' @param scale Logical scalar, whether to scale the result to have a maximum
+#' @param scale Logical, whether to scale the result to have a maximum
 #'   score of one. If no scaling is used then the result vector has unit length
 #'   in the Euclidean norm.
 #' @param weights Optional positive weight vector for calculating weighted
@@ -1539,7 +1539,7 @@ authority_score <- function(
 #' @title Kleinberg's hub centrality scores.
 #' @rdname hub_score
 #' @param graph The input graph.
-#' @param scale Logical scalar, whether to scale the result to have a maximum
+#' @param scale Logical, whether to scale the result to have a maximum
 #'   score of one. If no scaling is used then the result vector has unit length
 #'   in the Euclidean norm.
 #' @param weights Optional positive weight vector for calculating weighted
@@ -1707,7 +1707,7 @@ page_rank <- function(
 #'   the edge directions only, \dQuote{in} traverses the edges in reverse, while
 #'   \dQuote{all} ignores edge directions. This argument is ignored for undirected
 #'   graphs.
-#' @param normalized Logical scalar, whether to calculate the normalized
+#' @param normalized Logical, whether to calculate the normalized
 #'   harmonic centrality. If true, the result is the mean inverse path length to
 #'   other vertices, i.e. it is normalized by the number of vertices minus one.
 #'   If false, the result is the sum of inverse path lengths to other vertices.
@@ -1874,7 +1874,7 @@ bonpow.sparse <- function(
 #' @param graph the input graph.
 #' @param nodes vertex sequence indicating which vertices are to be included in
 #'   the calculation.  By default, all vertices are included.
-#' @param loops boolean indicating whether or not the diagonal should be
+#' @param loops Logical indicating whether or not the diagonal should be
 #'   treated as valid data.  Set this true if and only if the data can contain
 #'   loops.  `loops` is `FALSE` by default.
 #' @param exponent exponent (decay rate) for the Bonacich power centrality
@@ -1883,7 +1883,7 @@ bonpow.sparse <- function(
 #'   1.
 #' @param tol tolerance for near-singularities during matrix inversion (see
 #'   [solve()])
-#' @param sparse Logical scalar, whether to use sparse matrices for the
+#' @param sparse Logical, whether to use sparse matrices for the
 #'   calculation. The \sQuote{Matrix} package is required for sparse matrix
 #'   support
 #' @inheritParams as_adjacency_matrix
@@ -2069,7 +2069,7 @@ alpha.centrality.sparse <- function(
 #' @inheritParams as_adjacency_matrix
 #' @param tol Tolerance for near-singularities during matrix inversion, see
 #'   [solve()].
-#' @param sparse Logical scalar, whether to use sparse matrices for the
+#' @param sparse Logical, whether to use sparse matrices for the
 #'   calculation. The \sQuote{Matrix} package is required for sparse matrix
 #'   support
 #' @return A numeric vector contaning the centrality scores for the selected

R/centralization.R

@@ -282,7 +282,7 @@ NULL
 #'   the most centralized graph with the same number of vertices as the graph
 #'   under study. This is only used if the `normalized` argument is set
 #'   to `TRUE`.
-#' @param normalized Logical scalar. Whether to normalize the graph level
+#' @param normalized Logical. Whether to normalize the graph level
 #'   centrality score by dividing by the supplied theoretical maximum.
 #' @return A real scalar, the centralization of the graph from which
 #'   `scores` were derived.
@@ -329,9 +329,9 @@ centralize <- function(scores, theoretical.max = 0, normalized = TRUE) {
 #' @param graph The input graph.
 #' @param mode This is the same as the `mode` argument of
 #'   `degree()`.
-#' @param loops Logical scalar, whether to consider loops edges when
+#' @param loops Logical, whether to consider loops edges when
 #'   calculating the degree.
-#' @param normalized Logical scalar. Whether to normalize the graph level
+#' @param normalized Logical. Whether to normalize the graph level
 #'   centrality score by dividing by the theoretical maximum.
 #' @return A named list with the following components:
 #'   \describe{
@@ -435,7 +435,7 @@ centr_degree_tmax <- function(
 #' See [centralize()] for a summary of graph centralization.
 #'
 #' @param graph The input graph.
-#' @param directed logical scalar, whether to use directed shortest paths for
+#' @param directed Logical, whether to use directed shortest paths for
 #'   calculating betweenness.
 #' @inheritParams centr_degree
 #' @return A named list with the following components:
@@ -491,7 +491,7 @@ centr_betw <- function(graph, directed = TRUE, normalized = TRUE) {
 #'   `nodes` and `directed` are both given.
 #' @param nodes The number of vertices. This is ignored if the graph is
 #'   given.
-#' @param directed Logical scalar, whether to use directed shortest paths
+#' @param directed Logical, whether to use directed shortest paths
 #'   for calculating betweenness. Ignored if an undirected graph was
 #'   given.
 #' @return Real scalar, the theoretical maximum (unnormalized) graph
@@ -605,7 +605,7 @@ centr_clo_tmax <- function(
 #' See [centralize()] for a summary of graph centralization.
 #'
 #' @param graph The input graph.
-#' @param directed logical scalar, whether to use directed shortest paths for
+#' @param directed Logical, whether to use directed shortest paths for
 #'   calculating eigenvector centrality.
 #' @param scale `r lifecycle::badge("deprecated")` Ignored. Computing
 #' eigenvector centralization requires normalized eigenvector centrality scores.
@@ -682,7 +682,7 @@ centr_eigen <- function(
 #'   `nodes` is given.
 #' @param nodes The number of vertices. This is ignored if the graph is
 #'   given.
-#' @param directed logical scalar, whether to consider edge directions
+#' @param directed Logical, whether to consider edge directions
 #'   during the calculation. Ignored in undirected graphs.
 #' @param scale `r lifecycle::badge("deprecated")` Ignored. Computing
 #' eigenvector centralization requires normalized eigenvector centrality scores.

R/cohesive.blocks.R

@@ -224,7 +224,7 @@ blockGraphs <- function(blocks, graph) {
 #'   For `graphs_from_cohesive_blocks()` and `export_pajek()` the same graph must be
 #'   supplied whose cohesive block structure is given in the `blocks()`
 #'   argument.
-#' @param labels Logical scalar, whether to add the vertex labels to the result
+#' @param labels Logical, whether to add the vertex labels to the result
 #'   object. These labels can be then used when reporting and plotting the
 #'   cohesive blocks.
 #' @param blocks,x,object A `cohesiveBlocks` object, created with the
@@ -240,7 +240,7 @@ blockGraphs <- function(blocks, graph) {
 #'   of the files, without extension. (But it can contain the path to the files.)
 #'
 #'   See also details below.
-#' @param project.file Logical scalar, whether to create a single Pajek project
+#' @param project.file Logical, whether to create a single Pajek project
 #'   file containing all the data, or to create separated files for each item.
 #'   See details below.
 #' @param y The graph whose cohesive blocks are supplied in the `x`

R/community.R

@@ -584,7 +584,7 @@ code.length <- function(communities) {
 #'   communities are colored green and other edges are red.
 #' @param hang Numeric scalar indicating how the height of leaves should be
 #'   computed from the heights of their parents; see [plot.hclust()].
-#' @param use.modularity Logical scalar, whether to use the modularity values
+#' @param use.modularity Logical, whether to use the modularity values
 #'   to define the height of the branches.
 #' @param \dots Additional arguments. `plot.communities` passes these to
 #'   [plot.igraph()]. The other functions silently ignore
@@ -617,7 +617,7 @@ code.length <- function(communities) {
 #'
 #'   `crossing()` returns a logical vector.
 #'
-#'   `is_hierarchical()` returns a logical scalar.
+#'   `is_hierarchical()` returns a Logical.
 #'
 #'   `merges()` returns a two-column numeric matrix.
 #'
@@ -1391,7 +1391,7 @@ community.to.membership2 <- function(merges, vcount, steps) {
 #'   limit for the number of communities. It is not a problem to supply a
 #'   (reasonably) big number here, in which case some spin states will be
 #'   unpopulated.
-#' @param parupdate Logical constant, whether to update the spins of the
+#' @param parupdate Logical, whether to update the spins of the
 #'   vertices in parallel (synchronously) or not. This argument is ignored if the
 #'   second form of the function is used (i.e. the \sQuote{`vertex`} argument
 #'   is present). It is also not implemented in the \dQuote{neg} implementation.
@@ -1835,12 +1835,12 @@ cluster_fluid_communities <- function(graph, no.of.communities) {
 #'   weights increase the probability that an edge is selected by the random
 #'   walker. In other words, larger edge weights correspond to stronger connections.
 #' @param steps The length of the random walks to perform.
-#' @param merges Logical scalar, whether to include the merge matrix in the
+#' @param merges Logical, whether to include the merge matrix in the
 #'   result.
-#' @param modularity Logical scalar, whether to include the vector of the
+#' @param modularity Logical, whether to include the vector of the
 #'   modularity scores in the result. If the `membership` argument is true,
 #'   then it will always be calculated.
-#' @param membership Logical scalar, whether to calculate the membership vector
+#' @param membership Logical, whether to calculate the membership vector
 #'   for the split corresponding to the highest modularity value.
 #' @return `cluster_walktrap()` returns a [communities()]
 #'   object, please see the [communities()] manual page for details.
@@ -1946,11 +1946,11 @@ cluster_walktrap <- function(
 #'   attribute, but you don't want to use it for community detection. Edge weights
 #'   are used to calculate weighted edge betweenness. This means that edges are
 #'   interpreted as distances, not as connection strengths.
-#' @param directed Logical constant, whether to calculate directed edge
+#' @param directed Logical, whether to calculate directed edge
 #'   betweenness for directed graphs. It is ignored for undirected graphs.
-#' @param edge.betweenness Logical constant, whether to return the edge
+#' @param edge.betweenness Logical, whether to return the edge
 #'   betweenness of the edges at the time of their removal.
-#' @param merges Logical constant, whether to return the merge matrix
+#' @param merges Logical, whether to return the merge matrix
 #'   representing the hierarchical community structure of the network.  This
 #'   argument is called `merges`, even if the community structure algorithm
 #'   itself is divisive and not agglomerative: it builds the tree from top to
@@ -1962,12 +1962,12 @@ cluster_walktrap <- function(
 #'   communities numbered from one to \eqn{N}. The first merge, the first line of
 #'   the matrix creates community \eqn{N+1}, the second merge creates community
 #'   \eqn{N+2}, etc.
-#' @param bridges Logical constant, whether to return a list the edge removals
+#' @param bridges Logical, whether to return a list the edge removals
 #'   which actually splitted a component of the graph.
-#' @param modularity Logical constant, whether to calculate the maximum
+#' @param modularity Logical, whether to calculate the maximum
 #'   modularity score, considering all possibly community structures along the
 #'   edge-betweenness based edge removals.
-#' @param membership Logical constant, whether to calculate the membership
+#' @param membership Logical, whether to calculate the membership
 #'   vector corresponding to the highest possible modularity score.
 #' @return `cluster_edge_betweenness()` returns a
 #'   [communities()] object, please see the [communities()]
@@ -2054,10 +2054,10 @@ cluster_edge_betweenness <- function(
 #'
 #' @param graph The input graph. It must be undirected and must not have
 #'   multi-edges.
-#' @param merges Logical scalar, whether to return the merge matrix.
-#' @param modularity Logical scalar, whether to return a vector containing the
+#' @param merges Logical, whether to return the merge matrix.
+#' @param modularity Logical, whether to return a vector containing the
 #'   modularity after each merge.
-#' @param membership Logical scalar, whether to calculate the membership vector
+#' @param membership Logical, whether to calculate the membership vector
 #'   corresponding to the maximum modularity score, considering all possible
 #'   community structures along the merges.
 #' @param weights The weights of the edges. It must be a positive numeric vector,
@@ -2660,7 +2660,7 @@ cluster_optimal <- function(graph, weights = NULL) {
 #'   jumps to that vertex.
 #' @param nb.trials The number of attempts to partition the network (can be any
 #'   integer value equal or larger than 1).
-#' @param modularity Logical scalar, whether to calculate the modularity score
+#' @param modularity Logical, whether to calculate the modularity score
 #'   of the detected community structure.
 #' @return `cluster_infomap()` returns a [communities()] object,
 #'   please see the [communities()] manual page for details.
@@ -2807,7 +2807,7 @@ plot_dendrogram <- function(x, mode = igraph_opt("dend.plot.type"), ...) {
 #' @param mode Which dendrogram plotting function to use. See details below.
 #' @param \dots Additional arguments to supply to the dendrogram plotting
 #'   function.
-#' @param use.modularity Logical scalar, whether to use the modularity values
+#' @param use.modularity Logical, whether to use the modularity values
 #'   to define the height of the branches.
 #' @param palette The color palette to use for colored plots.
 #' @return Returns whatever the return value was from the plotting function,