v0.3.0

DESCRIPTION

@@ -1,7 +1,6 @@
 Package: netrics
 Title: Many Ways to Measure and Classify Membership for Networks, Nodes, and Ties
-Version: 0.2.3
-Date: 2026-04-25
+Version: 0.3.0
 Description: Many tools for calculating network, node, or tie
    marks, measures, motifs and memberships of many different types of networks.
    Marks identify structural positions, measures quantify network properties,
@@ -14,10 +13,9 @@ License: MIT + file LICENSE
 Language: en-GB
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.3.3
 Depends: 
-    R (>= 3.6.0),
-    manynet
+    R (>= 4.1.0),
+    manynet (>= 2.0.0)
 Imports:
     dplyr,
     igraph (>= 2.1.0)
@@ -45,3 +43,4 @@ Config/Needs/website:
 Config/testthat/parallel: true
 Config/testthat/edition: 3
 Config/testthat/start-first: measure_net 
+Config/roxygen2/version: 8.0.0

NAMESPACE

@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-export("%>%")
 export(cluster_concor)
 export(cluster_cosine)
 export(cluster_hierarchical)
@@ -178,7 +177,10 @@ export(tie_is_simmelian)
 export(tie_is_transitive)
 export(tie_is_triangular)
 export(tie_is_triplet)
-importFrom(dplyr,"%>%")
+importFrom(dplyr,filter)
+importFrom(dplyr,group_by)
+importFrom(dplyr,mutate)
+importFrom(dplyr,select)
 importFrom(igraph,V)
 importFrom(igraph,adhesion)
 importFrom(igraph,all_shortest_paths)

NEWS.md

@@ -1,3 +1,25 @@
+# netrics 0.3.0
+
+## Package
+
+- Improved docs/examples to use base R pipe (|>) instead of magrittr's %>%
+- Improved dependency requirements (R >= 4.1.0)
+- Improved startup messages to be more succinct
+
+## Measures
+
+- Fixed `node_by_homophily()` to work when attribute is provided as a vector (e.g., a membership vector)
+- Improved `node_by_homophily()` to avoid calling `as_igraph()` multiple times
+
+## Motifs
+
+- Fixed `net_x_hazard()` to use `diff_model$t` for naming the returned data frame columns, rather than the deprecated `diff_model$time`
+
+## Tutorials
+
+- Updated topology tutorial to use base R pipe (|>) instead of magrittr's %>%
+- Updated centrality tutorial to use base R pipe (|>) instead of magrittr's %>%
+
 # netrics 0.2.3
 
 ## Tutorials

R/class_metrics.R

@@ -76,7 +76,3 @@ make_network_motif <- function(out, .data) {
   attr(out, "call") <- deparse(sys.calls())
   out
 }
-
-#' @importFrom dplyr %>%
-#' @export
-dplyr::`%>%`

R/mark_nodes.R

@@ -240,10 +240,10 @@ node_is_latent <- function(.data, time = 0){
   .data <- manynet::expect_nodes(.data)
   if(manynet::is_changing(.data)){
     t <- time
-    latent <- manynet::as_changelist(.data) %>% 
-      dplyr::filter(time <= t & value %in% c("E", "I")) %>%
-      dplyr::group_by(node) %>%
-      dplyr::mutate(n = dplyr::n()) %>%
+    latent <- manynet::as_changelist(.data) |> 
+      dplyr::filter(time <= t & value %in% c("E", "I")) |>
+      dplyr::group_by(node) |>
+      dplyr::mutate(n = dplyr::n()) |>
       dplyr::filter(n == 1 & value == "E")
     if (manynet::is_labelled(.data)) {
       out <- seq_len(manynet::net_nodes(.data)) %in% latent$node
@@ -253,10 +253,10 @@ node_is_latent <- function(.data, time = 0){
     }
     make_node_mark(out, .data)
   } else if(inherits(.data, "diff_model")){
-    latent <- summary(.data) %>%
-      dplyr::filter(t <= time & event %in% c("E", "I")) %>%
-      dplyr::group_by(nodes) %>%
-      dplyr::mutate(n = dplyr::n()) %>%
+    latent <- summary(.data) |>
+      dplyr::filter(t <= time & event %in% c("E", "I")) |>
+      dplyr::group_by(nodes) |>
+      dplyr::mutate(n = dplyr::n()) |>
       dplyr::filter(n == 1 & event == "E")
     net <- attr(.data, "network")
     if (manynet::is_labelled(net)) {
@@ -283,10 +283,10 @@ node_is_infected <- function(.data, time = 0) {
   .data <- manynet::expect_nodes(.data)
   if(manynet::is_changing(.data)){
     t <- time
-    infected <- manynet::as_changelist(.data) %>% 
-      dplyr::filter(time <= t & value %in% c("I", "R")) %>%
-      dplyr::group_by(node) %>%
-      dplyr::mutate(n = dplyr::n()) %>%
+    infected <- manynet::as_changelist(.data) |> 
+      dplyr::filter(time <= t & value %in% c("I", "R")) |>
+      dplyr::group_by(node) |>
+      dplyr::mutate(n = dplyr::n()) |>
       dplyr::filter(n == 1 & value == "I")
     if (manynet::is_labelled(.data)) {
       out <- seq_len(manynet::net_nodes(.data)) %in% infected$node
@@ -296,10 +296,10 @@ node_is_infected <- function(.data, time = 0) {
     }
     make_node_mark(out, .data)
   } else if(inherits(.data, "diff_model")){
-    infected <- summary(.data) %>% 
-      dplyr::filter(t <= time & event %in% c("I", "R")) %>%
-      dplyr::group_by(nodes) %>%
-      dplyr::mutate(n = dplyr::n()) %>%
+    infected <- summary(.data) |> 
+      dplyr::filter(t <= time & event %in% c("I", "R")) |>
+      dplyr::group_by(nodes) |>
+      dplyr::mutate(n = dplyr::n()) |>
       dplyr::filter(n == 1 & event == "I")
     net <- attr(.data, "network")
     if (manynet::is_labelled(net)) {
@@ -325,10 +325,10 @@ node_is_recovered <- function(.data, time = 0){
   .data <- manynet::expect_nodes(.data)
   if(manynet::is_changing(.data)){
     t <- time
-    recovered <- manynet::as_changelist(.data) %>% 
-      dplyr::filter(time <= t & value %in% c("R")) %>%
-      dplyr::group_by(node) %>%
-      dplyr::mutate(n = dplyr::n()) %>%
+    recovered <- manynet::as_changelist(.data) |> 
+      dplyr::filter(time <= t & value %in% c("R")) |>
+      dplyr::group_by(node) |>
+      dplyr::mutate(n = dplyr::n()) |>
       dplyr::filter(n == 1 & value == "R")
     if (manynet::is_labelled(.data)) {
       out <- seq_len(manynet::net_nodes(.data)) %in% recovered$node
@@ -338,10 +338,10 @@ node_is_recovered <- function(.data, time = 0){
     }
     make_node_mark(out, .data)
   } else if(inherits(.data, "diff_model")){
-    recovered <- summary(.data) %>% 
-      dplyr::filter(t <= time & event == "R") %>%
-      dplyr::group_by(nodes) %>%
-      dplyr::mutate(n = dplyr::n()) %>%
+    recovered <- summary(.data) |> 
+      dplyr::filter(t <= time & event == "R") |>
+      dplyr::group_by(nodes) |>
+      dplyr::mutate(n = dplyr::n()) |>
       dplyr::filter(n == 1)
     net <- attr(.data, "network")
     if (manynet::is_labelled(net)) {
@@ -372,6 +372,7 @@ node_is_recovered <- function(.data, time = 0){
 #'   then the function will return nodes exposure to the seed nodes
 #'   in that diffusion.
 #' @param mark vector denoting which nodes are infected
+#' @importFrom dplyr filter select group_by mutate
 #' @examples
 #'   # To mark which nodes are currently exposed
 #'   (expos <- node_is_exposed(manynet::create_tree(14), mark = c(1,3)))
@@ -384,9 +385,9 @@ node_is_exposed <- function(.data, mark, time = 0){
       t <- time
       return(make_node_mark(node_by_adopt_exposure(.data, time = t)>0, .data))
     } else if(inherits(.data, "diff_model")){
-      mark <- summary(.data) %>% 
-        dplyr::filter(t == 0 & event == "I") %>% 
-        dplyr::select(nodes) %>% unlist()
+      mark <- summary(.data) |> 
+        dplyr::filter(t == 0 & event == "I") |> 
+        dplyr::select(nodes) |> unlist()
       .data <- attr(.data, "network")
     }    
   }

R/mark_ties.R

@@ -62,7 +62,7 @@ tie_is_bridge <- function(.data){
 #'   By default FALSE, sampling just a single path.
 #' @importFrom igraph all_shortest_paths
 #' @examples 
-#' ison_adolescents %>%
+#' ison_adolescents |>
 #'   mutate_ties(route = tie_is_path(from = "Jane", to = 7)) 
 #' @export
 tie_is_path <- function(.data, from, to, all_paths = FALSE){
@@ -141,7 +141,7 @@ NULL
 #' @rdname mark_triangles
 #' @importFrom igraph triangles
 #' @examples 
-#' ison_monks %>% to_uniplex("like") %>% 
+#' ison_monks |> to_uniplex("like") |> 
 #'   mutate_ties(tri = tie_is_triangular())
 #' @export
 tie_is_triangular <- function(.data){
@@ -161,7 +161,7 @@ tie_is_triangular <- function(.data){
 
 #' @rdname mark_triangles
 #' @examples 
-#' ison_adolescents %>% to_directed() %>% 
+#' ison_adolescents |> to_directed() |> 
 #'   mutate_ties(trans = tie_is_transitive())
 #' @export
 tie_is_transitive <- function(.data){
@@ -177,7 +177,7 @@ tie_is_transitive <- function(.data){
 
 #' @rdname mark_triangles
 #' @examples 
-#' ison_adolescents %>% to_directed() %>% 
+#' ison_adolescents |> to_directed() |> 
 #'   mutate_ties(trip = tie_is_triplet())
 #' @export
 tie_is_triplet <- function(.data){
@@ -198,7 +198,7 @@ tie_is_triplet <- function(.data){
 
 #' @rdname mark_triangles
 #' @examples 
-#' ison_adolescents %>% to_directed() %>% 
+#' ison_adolescents |> to_directed() |> 
 #'   mutate_ties(cyc = tie_is_cyclical())
 #' @export
 tie_is_cyclical <- function(.data){
@@ -214,7 +214,7 @@ tie_is_cyclical <- function(.data){
 
 #' @rdname mark_triangles
 #' @examples 
-#' ison_monks %>% to_uniplex("like") %>% 
+#' ison_monks |> to_uniplex("like") |> 
 #'   mutate_ties(simmel = tie_is_simmelian())
 #' @export
 tie_is_simmelian <- function(.data){
@@ -229,7 +229,7 @@ tie_is_simmelian <- function(.data){
 
 # #' @rdname mark_triangles
 # #' @examples 
-# #' generate_random(8, directed = TRUE) %>% 
+# #' generate_random(8, directed = TRUE) |> 
 # #'   mutate_ties(forbid = tie_is_forbidden())
 # #' @export
 # tie_is_forbidden <- function(.data){
@@ -262,7 +262,7 @@ tie_is_simmelian <- function(.data){
 
 #' @rdname mark_triangles
 #' @examples
-#' fict_marvel %>% to_uniplex("relationship") %>% tie_is_imbalanced()
+#' fict_marvel |> to_uniplex("relationship") |> tie_is_imbalanced()
 #' @export
 tie_is_imbalanced <- function(.data){
   .data <- manynet::expect_ties(.data)  

R/measure_centrality_between.R

@@ -179,7 +179,7 @@ NULL
 #' @importFrom igraph edge_betweenness
 #' @examples
 #' (tb <- tie_by_betweenness(ison_adolescents))
-#' ison_adolescents %>% mutate_ties(weight = tb)
+#' ison_adolescents |> mutate_ties(weight = tb)
 #' @export
 tie_by_betweenness <- function(.data, normalized = TRUE){
   .data <- manynet::expect_ties(.data)

R/measure_centrality_closeness.R

@@ -361,7 +361,7 @@ NULL
 #' @rdname measure_centralities_close 
 #' @examples
 #' (ec <- tie_by_closeness(ison_adolescents))
-#' ison_adolescents %>% mutate_ties(weight = ec)
+#' ison_adolescents |> mutate_ties(weight = ec)
 #' @export
 tie_by_closeness <- function(.data, normalized = TRUE){
   .data <- manynet::expect_ties(.data)

R/measure_diffusion.R

@@ -306,8 +306,8 @@ node_by_adopt_time <- function(.data){
 
   if(inherits(.data, "diff_model")){
     net <- attr(.data, "network") 
-    out <- summary(.data) %>% dplyr::filter(event == "I") %>% 
-      dplyr::distinct(nodes, .keep_all = TRUE) %>% 
+    out <- summary(.data) |> dplyr::filter(event == "I") |> 
+      dplyr::distinct(nodes, .keep_all = TRUE) |> 
       dplyr::select(nodes,t)
     if(!manynet::is_labelled(net))
       out <- dplyr::arrange(out, nodes) else if (is.numeric(out$nodes))
@@ -323,8 +323,8 @@ node_by_adopt_time <- function(.data){
     }
   } else {
     net <- .data
-    out <- manynet::as_changelist(.data) %>% dplyr::filter(value == "I") %>% 
-      dplyr::distinct(node, .keep_all = TRUE) %>% 
+    out <- manynet::as_changelist(.data) |> dplyr::filter(value == "I") |> 
+      dplyr::distinct(node, .keep_all = TRUE) |> 
       dplyr::select(node,time)
     if(!manynet::is_labelled(net))
       out <- dplyr::arrange(out, node) else if (is.numeric(out$node))
@@ -379,9 +379,9 @@ node_by_adopt_threshold <- function(.data, normalized = TRUE, lag = 1){
       }
     }
     if(any(out$event == "E")) 
-      out <- out %>% dplyr::filter(event == "E") else 
-        out <- out %>% dplyr::filter(event == "I")
-    out <- out %>% dplyr::distinct(nodes, .keep_all = TRUE) %>% 
+      out <- out |> dplyr::filter(event == "E") else 
+        out <- out |> dplyr::filter(event == "I")
+    out <- out |> dplyr::distinct(nodes, .keep_all = TRUE) |> 
       dplyr::select(nodes, exposure)
     if(manynet::is_labelled(net))
       out <- stats::setNames(out$exposure, manynet::node_names(net)[out$nodes]) else
@@ -398,9 +398,9 @@ node_by_adopt_threshold <- function(.data, normalized = TRUE, lag = 1){
       }
     }
     if(any(out$value == "E")) 
-      out <- out %>% dplyr::filter(value == "E") else 
-        out <- out %>% dplyr::filter(value == "I")
-    out <- out %>% dplyr::distinct(node, .keep_all = TRUE) %>% 
+      out <- out |> dplyr::filter(value == "E") else 
+        out <- out |> dplyr::filter(value == "I")
+    out <- out |> dplyr::distinct(node, .keep_all = TRUE) |> 
       dplyr::select(node, exposure)
     if(manynet::is_labelled(net))
       out <- stats::setNames(out$exposure, manynet::node_names(net)[out$node]) else

R/measure_heterogeneity.R

@@ -531,10 +531,15 @@ node_by_homophily <- function(.data, attribute,
     manynet::snet_info("Using {.val ie} index instead.")
     assortativity <- "ie"
   }
-  out <- vapply(igraph::ego(manynet::as_igraph(.data)),
-                function(x) net_by_homophily(
-                  igraph::induced_subgraph(manynet::as_igraph(.data), x),
-                  attribute, assortativity = assortativity),
+  idat <- manynet::as_igraph(.data)
+  out <- vapply(igraph::ego(idat),
+                function(x) {
+                  subattr <- if (length(attribute) == 1 && is.character(attribute))
+                    attribute else attribute[as.integer(x)]
+                  net_by_homophily(
+                    igraph::induced_subgraph(idat, x),
+                    subattr, assortativity = assortativity)
+                },
                 FUN.VALUE = numeric(1))
   make_node_measure(out, .data)
 }

R/member_components.R

@@ -28,7 +28,7 @@ NULL
 #' @rdname member_components 
 #' @importFrom igraph components
 #' @examples
-#' ison_monks %>% to_uniplex("esteem") %>%
+#' ison_monks |> to_uniplex("esteem") |>
 #'   mutate_nodes(comp = node_in_component())
 #' @export
 node_in_component <- function(.data){

R/member_core.R

@@ -41,7 +41,7 @@ NULL
 #' \doi{10.48550/arXiv.1102.5511}
 #' @examples 
 #' node_is_core(ison_adolescents)
-#' ison_adolescents %>% 
+#' ison_adolescents |> 
 #'    mutate(corep = node_is_core())
 #' @export
 node_is_core <- function(.data, centrality = c("degree", "eigenvector")){