v0.2.0

.Rbuildignore

@@ -1,5 +1,6 @@
 ^CODE_OF_CONDUCT\.md$
 ^LICENSE\.md$
+man-roxygen
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^working$

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: netrics
 Title: Many Ways to Measure and Classify Membership for Networks, Nodes, and Ties
-Version: 0.1.0
-Date: 2026-03-15
+Version: 0.2.0
+Date: 2026-03-21
 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,

NAMESPACE

@@ -5,20 +5,19 @@ export(cluster_concor)
 export(cluster_cosine)
 export(cluster_hierarchical)
 export(k_elbow)
+export(k_gap)
 export(k_silhouette)
 export(k_strict)
 export(net_by_adhesion)
 export(net_by_assortativity)
 export(net_by_balance)
 export(net_by_betweenness)
-export(net_by_change)
 export(net_by_closeness)
 export(net_by_cohesion)
 export(net_by_components)
 export(net_by_congruency)
 export(net_by_connectedness)
 export(net_by_core)
-export(net_by_correlation)
 export(net_by_degree)
 export(net_by_density)
 export(net_by_diameter)
@@ -48,21 +47,26 @@ export(net_by_richness)
 export(net_by_scalefree)
 export(net_by_smallworld)
 export(net_by_spatial)
-export(net_by_stability)
 export(net_by_strength)
 export(net_by_toughness)
 export(net_by_transitivity)
 export(net_by_transmissibility)
 export(net_by_upperbound)
 export(net_by_waves)
 export(net_x_brokerage)
+export(net_x_change)
+export(net_x_correlation)
 export(net_x_dyad)
 export(net_x_hazard)
 export(net_x_hierarchy)
 export(net_x_mixed)
+export(net_x_stability)
 export(net_x_tetrad)
 export(net_x_triad)
-export(node_by_adoption_time)
+export(node_by_adopt_exposure)
+export(node_by_adopt_recovery)
+export(node_by_adopt_threshold)
+export(node_by_adopt_time)
 export(node_by_alpha)
 export(node_by_authority)
 export(node_by_betweenness)
@@ -81,7 +85,6 @@ export(node_by_efficiency)
 export(node_by_effsize)
 export(node_by_eigenvector)
 export(node_by_equivalency)
-export(node_by_exposure)
 export(node_by_flow)
 export(node_by_harmonic)
 export(node_by_heterophily)
@@ -102,12 +105,10 @@ export(node_by_power)
 export(node_by_randomwalk)
 export(node_by_reach)
 export(node_by_reciprocity)
-export(node_by_recovery)
 export(node_by_redundancy)
 export(node_by_richness)
 export(node_by_stress)
 export(node_by_subgraph)
-export(node_by_thresholds)
 export(node_by_transitivity)
 export(node_by_vitality)
 export(node_in_adopter)

NEWS.md

@@ -1,3 +1,45 @@
+# netrics 0.2.0
+
+## Package
+
+- Added roxygen2 parameter templates (`param_attr`, `param_data`,
+  `param_dir`, `param_memb`, `param_motf`, `param_norm`, `param_select`) and
+  net/node/tie-level templates (`net_measure`, `net_motif`, `node_mark`,
+  `node_measure`, `node_member`, `node_motif`, `tie_mark`, `tie_measure`) for
+  consistent function documentation.
+- Fixed startup messages.
+
+## Measuring
+
+- Renamed `node_adoption_time()` to `node_by_adopt_time()`
+- Renamed `node_thresholds()` to `node_by_adopt_threshold()`
+- Renamed `node_exposure()` to `node_by_adopt_exposure()`  
+- Renamed `node_recovery()` to `node_by_adopt_recovery()`  
+- Separated centralisation scripts into different files per measure type
+  (betweenness, closeness, degree, eigenvector) for easier maintenance,
+  and into different documentation per level (node, tie) for better organization.
+- Updated and separated brokerage, diversity/assortativity, cohesion, closure, 
+  cliques, components, features, and hierarchy documentation by level.
+
+## Members
+
+- Separated `node_in_community()` documentation from the hierarchical
+  and non-hierarchical community-detection algorithms.
+- Core documentation split into separate mark, measure, and member pages.
+- Improved various functions that rely on a membership argument to accept
+  both a membership vector and a string identifier of a network attribute.
+
+## Motifs
+
+- Renamed `net_by_change()` to `net_x_change()` and related functions to 
+  reflect their motif (subgraph-counting) nature.
+
+## Methods
+
+- Added gap method for cluster *k*-selection in `method_k()`.
+- Renamed `model_k()` to `method_k()` and related cluster-selection utilities
+  renamed for clarity.
+
 # netrics 0.1.0
 
 ## Release notes

R/mark_nodes.R

@@ -1,76 +1,7 @@
-# Degree properties ####
-
-#' Marking nodes based on degree properties
-#' 
-#' @description 
-#'   These functions return logical vectors the length of the 
-#'   nodes in a network identifying which hold certain properties or positions in the network.
-#'   
-#'   - `node_is_isolate()` marks nodes that are isolates,
-#'   with neither incoming nor outgoing ties.
-#'   - `node_is_pendant()` marks nodes that are pendants,
-#'   with exactly one incoming or outgoing tie.
-#'   - `node_is_universal()` identifies whether nodes are adjacent to all other
-#'   nodes in the network.
-#' @param .data A network object of class `mnet`, `igraph`, `tbl_graph`, `network`, or similar.
-#'   For more information on the standard coercion possible,
-#'   see [manynet::as_tidygraph()].
-#' @family marks
-#' @family degree
-#' @name mark_degree
-NULL
-
-#' @rdname mark_degree
-#' @examples 
-#' node_is_isolate(ison_brandes)
-#' @export
-node_is_isolate <- function(.data){
-  .data <- manynet::expect_nodes(.data)
-  mat <- abs(manynet::as_matrix(.data))
-  if(manynet::is_twomode(.data)){
-    out <- c(rowSums(mat)==0, colSums(mat)==0)
-  } else {
-    out <- rowSums(mat)==0 & colSums(mat)==0
-  }
-  names(out) <- manynet::node_names(.data)
-  make_node_mark(out, .data)
-}
-
-#' @rdname mark_degree
-#' @export
-node_is_pendant <- function(.data){
-  .data <- manynet::expect_nodes(.data)
-  mat <- abs(manynet::as_matrix(.data))
-  if(manynet::is_twomode(.data)){
-    out <- c(rowSums(mat)==1, colSums(mat)==1)
-  } else {
-    out <- rowSums(mat)==1 & colSums(mat)==1
-  }
-  make_node_mark(out, .data)
-}
-
-#' @rdname mark_degree
-#' @section Universal/dominating node: 
-#'   A universal node is adjacent to all other nodes in the network.
-#'   It is also sometimes called the dominating vertex because it represents
-#'   a one-element dominating set.
-#'   A network with a universal node is called a cone, and its universal node
-#'   is called the apex of the cone.
-#'   A classic example of a cone is a star graph,
-#'   but friendship, wheel, and threshold graphs are also cones.
-#' @examples
-#' node_is_universal(create_star(11))
-#' @export
-node_is_universal <- function(.data){
-  .data <- manynet::expect_nodes(.data)
-  net <- manynet::to_undirected(manynet::to_unweighted(.data))
-  make_node_mark(node_by_deg(net)==(manynet::net_nodes(net)-1), .data)
-}
-
 # Structural properties ####
 
 #' Marking nodes based on structural properties
-#' 
+#' @name mark_nodes
 #' @description 
 #'   These functions return logical vectors the length of the 
 #'   nodes in a network identifying which hold certain properties or positions in the network.
@@ -83,11 +14,8 @@ node_is_universal <- function(.data){
 #'   triangles that are only connected by that node.
 #'   - `node_is_mentor()` marks a proportion of high indegree nodes as 'mentors' (see details).
 #'   - `node_is_neighbor()` marks nodes that are neighbours of a given node.
-#' @param .data A network object of class `mnet`, `igraph`, `tbl_graph`, `network`, or similar.
-#'   For more information on the standard coercion possible,
-#'   see [manynet::as_tidygraph()].
-#' @family marks
-#' @name mark_nodes
+#' @template param_data
+#' @template node_mark
 NULL
 
 #' @rdname mark_nodes
@@ -219,6 +147,72 @@ node_is_neighbor <- function(.data, node){
   make_node_mark(out, .data)
 }
 
+# Degree properties ####
+
+#' Marking nodes based on degree properties
+#' @name mark_degree
+#' @description
+#'   These functions return logical vectors the length of the 
+#'   nodes in a network identifying which hold certain properties or positions in the network.
+#'   
+#'   - `node_is_isolate()` marks nodes that are isolates,
+#'   with neither incoming nor outgoing ties.
+#'   - `node_is_pendant()` marks nodes that are pendants,
+#'   with exactly one incoming or outgoing tie.
+#'   - `node_is_universal()` identifies whether nodes are adjacent to all other
+#'   nodes in the network.
+#' @template param_data
+#' @family degree
+#' @template node_mark
+NULL
+
+#' @rdname mark_degree
+#' @examples 
+#' node_is_isolate(ison_brandes)
+#' @export
+node_is_isolate <- function(.data){
+  .data <- manynet::expect_nodes(.data)
+  mat <- abs(manynet::as_matrix(.data))
+  if(manynet::is_twomode(.data)){
+    out <- c(rowSums(mat)==0, colSums(mat)==0)
+  } else {
+    out <- rowSums(mat)==0 & colSums(mat)==0
+  }
+  names(out) <- manynet::node_names(.data)
+  make_node_mark(out, .data)
+}
+
+#' @rdname mark_degree
+#' @export
+node_is_pendant <- function(.data){
+  .data <- manynet::expect_nodes(.data)
+  mat <- abs(manynet::as_matrix(.data))
+  if(manynet::is_twomode(.data)){
+    out <- c(rowSums(mat)==1, colSums(mat)==1)
+  } else {
+    out <- rowSums(mat)==1 & colSums(mat)==1
+  }
+  make_node_mark(out, .data)
+}
+
+#' @rdname mark_degree
+#' @section Universal/dominating node: 
+#'   A universal node is adjacent to all other nodes in the network.
+#'   It is also sometimes called the dominating vertex because it represents
+#'   a one-element dominating set.
+#'   A network with a universal node is called a cone, and its universal node
+#'   is called the apex of the cone.
+#'   A classic example of a cone is a star graph,
+#'   but friendship, wheel, and threshold graphs are also cones.
+#' @examples
+#' node_is_universal(create_star(11))
+#' @export
+node_is_universal <- function(.data){
+  .data <- manynet::expect_nodes(.data)
+  net <- manynet::to_undirected(manynet::to_unweighted(.data))
+  make_node_mark(node_by_deg(net)==(manynet::net_nodes(net)-1), .data)
+}
+
 # Diffusion properties ####
 
 #' Marking nodes based on diffusion properties
@@ -232,7 +226,7 @@ node_is_neighbor <- function(.data, node){
 #'   - `node_is_latent()` marks nodes that are latent at a particular time point.
 #'   - `node_is_recovered()` marks nodes that are recovered at a particular time point.
 #' @inheritParams mark_nodes
-#' @family marks
+#' @template node_mark
 #' @family diffusion
 #' @name mark_diff
 NULL
@@ -388,7 +382,7 @@ node_is_exposed <- function(.data, mark, time = 0){
   if (missing(mark)){
     if(manynet::is_changing(.data)){
       t <- time
-      return(make_node_mark(node_by_exposure(.data, time = t)>0, .data))
+      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") %>% 
@@ -406,7 +400,7 @@ node_is_exposed <- function(.data, mark, time = 0){
 # Selection properties ####
 
 #' Marking nodes based on measures
-#' 
+#' @name mark_select_node
 #' @description 
 #'   These functions return logical vectors the length of the 
 #'   nodes in a network identifying which hold certain properties or positions in the network.
@@ -416,33 +410,33 @@ node_is_exposed <- function(.data, mark, time = 0){
 #'   for converting the results from some node measure into a mark-class object.
 #'   They can be particularly useful for highlighting which node or nodes
 #'   are key because they minimise or, more often, maximise some measure.
-#' @inheritParams mark_nodes
-#' @family marks
-#' @name mark_select
+#' @template param_data
+#' @family selection
+#' @template node_mark
 NULL
 
-#' @rdname mark_select
-#' @param size The number of nodes to select (as TRUE).
+#' @rdname mark_select_node
+#' @template param_select
 #' @examples 
 #' node_is_random(ison_brandes, 2)
 #' @export
-node_is_random <- function(.data, size = 1){
+node_is_random <- function(.data, select = 1){
   .data <- manynet::expect_nodes(.data)
   n <- manynet::net_nodes(.data)
   out <- rep(FALSE, n)
-  out[sample.int(n, size)] <- TRUE
+  out[sample.int(n, select)] <- TRUE
   make_node_mark(out, .data)
 }
 
-#' @rdname mark_select
+#' @rdname mark_select_node
 #' @param node_measure An object created by a `node_` measure.
 #' @param ranks The number of ranks of max or min to return.
 #'   For example, `ranks = 3` will return TRUE for nodes with
 #'   scores equal to any of the top (or, for `node_is_min()`, bottom)
 #'   three scores.
 #'   By default, `ranks = 1`.
 #' @examples 
-#' #node_is_max(node_by_degree(ison_brandes))
+#' node_is_max(node_by_degree(ison_brandes))
 #' @export
 node_is_max <- function(node_measure, ranks = 1){
   if(!inherits(node_measure, "node_measure"))
@@ -466,9 +460,9 @@ node_is_max <- function(node_measure, ranks = 1){
   out
 }
 
-#' @rdname mark_select
+#' @rdname mark_select_node
 #' @examples 
-#' #node_is_min(node_by_degree(ison_brandes))
+#' node_is_min(node_by_degree(ison_brandes))
 #' @export
 node_is_min <- function(node_measure, ranks = 1){
   if(!inherits(node_measure, "node_measure"))
@@ -492,9 +486,9 @@ node_is_min <- function(node_measure, ranks = 1){
   out
 }
 
-#' @rdname mark_select
+#' @rdname mark_select_node
 #' @examples 
-#' #node_is_mean(node_degree(ison_brandes))
+#' node_is_mean(node_by_degree(ison_brandes))
 #' @export
 node_is_mean <- function(node_measure, ranks = 1){
   if(!inherits(node_measure, "node_measure"))