v0.1.0

NEWS.md

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+# netrics 0.1.0
+
+## Release notes
+
+`{netrics}` 0.1.0 is the first formal release of the package as a standalone
+analytic engine for the [stocnet](https://github.com/stocnet) ecosystem.
+The analytic functions — marks, measures, motifs, and memberships — have been
+extracted from `{manynet}` and `{migraph}` into this dedicated package, with
+consistent naming conventions and a range of bug fixes.
+
+## New naming conventions
+
+All functions now follow a consistent verb–object–qualifier naming scheme:
+
+- **Marks** (`node_is_*()`, `tie_is_*()`): logical vectors identifying which nodes
+  or ties hold a particular structural property.
+- **Measures** (`*_by_*()`): numeric vectors at the network (`net_by_*()`),
+  node (`node_by_*()`), or tie (`tie_by_*()`) level.
+- **Motifs** (`*_x_*()`): tabular counts of nodes' or networks' participation
+  in structural sub-patterns.
+- **Memberships** (`*_in_*()`): categorical vectors assigning nodes to groups
+  (components, communities, equivalence classes, etc.).
+
+Functions previously named with other prefixes (e.g. `node_centrality_*`,
+`net_cohesion_*`, `node_equivalency_*`) have been renamed to follow the
+`*_by_*()` / `*_x_*()` / `*_in_*()` convention.
+`tie_by_cohesion()` now correctly returns a `tie_measure` class object.
+
+## Functions moved from `{manynet}` / `{migraph}`
+
+The following groups of functions have been moved into `{netrics}`:
+
+### Marks
+- `node_is_core()`, `node_is_cutpoint()`, `node_is_exposed()`,
+  `node_is_fold()`, `node_is_independent()`, `node_is_infected()`,
+  `node_is_isolate()`, `node_is_latent()`, `node_is_max()`,
+  `node_is_mean()`, `node_is_mentor()`, `node_is_min()`,
+  `node_is_neighbor()`, `node_is_pendant()`, `node_is_random()`,
+  `node_is_recovered()`, `node_is_universal()`
+- `tie_is_bridge()`, `tie_is_cyclical()`, `tie_is_feedback()`,
+  `tie_is_imbalanced()`, `tie_is_loop()`, `tie_is_max()`,
+  `tie_is_min()`, `tie_is_multiple()`, `tie_is_path()`,
+  `tie_is_random()`, `tie_is_reciprocated()`, `tie_is_simmelian()`,
+  `tie_is_transitive()`, `tie_is_triangular()`, `tie_is_triplet()`
+
+### Measures
+- Network-level: `net_by_adhesion()`, `net_by_assortativity()`,
+  `net_by_balance()`, `net_by_betweenness()`, `net_by_change()`,
+  `net_by_closeness()`, `net_by_cohesion()`, `net_by_components()`,
+  `net_by_congruency()`, `net_by_connectedness()`, `net_by_core()`,
+  `net_by_correlation()`, `net_by_degree()`, `net_by_density()`,
+  `net_by_diameter()`, `net_by_diversity()`, `net_by_efficiency()`,
+  `net_by_eigenvector()`, `net_by_equivalency()`, `net_by_factions()`,
+  `net_by_harmonic()`, `net_by_heterophily()`, `net_by_hierarchy()`,
+  `net_by_homophily()`, `net_by_immunity()`, `net_by_indegree()`,
+  `net_by_independence()`, `net_by_infection_complete()`,
+  `net_by_infection_peak()`, `net_by_infection_total()`,
+  `net_by_length()`, `net_by_modularity()`, `net_by_outdegree()`,
+  `net_by_reach()`, `net_by_reciprocity()`, `net_by_recovery()`,
+  `net_by_reproduction()`, `net_by_richclub()`, `net_by_richness()`,
+  `net_by_scalefree()`, `net_by_smallworld()`, `net_by_spatial()`,
+  `net_by_stability()`, `net_by_strength()`, `net_by_toughness()`,
+  `net_by_transitivity()`, `net_by_transmissibility()`,
+  `net_by_upperbound()`, `net_by_waves()`
+- Node-level: `node_by_adoption_time()`, `node_by_alpha()`,
+  `node_by_authority()`, `node_by_betweenness()`, `node_by_bridges()`,
+  `node_by_brokering_activity()`, `node_by_brokering_exclusivity()`,
+  `node_by_closeness()`, `node_by_constraint()`, `node_by_coreness()`,
+  `node_by_deg()`, `node_by_degree()`, `node_by_distance()`,
+  `node_by_diversity()`, `node_by_eccentricity()`,
+  `node_by_efficiency()`, `node_by_effsize()`, `node_by_eigenvector()`,
+  `node_by_equivalency()`, `node_by_exposure()`, `node_by_flow()`,
+  `node_by_harmonic()`, `node_by_heterophily()`, `node_by_hierarchy()`,
+  `node_by_homophily()`, `node_by_hub()`, `node_by_indegree()`,
+  `node_by_induced()`, `node_by_information()`, `node_by_kcoreness()`,
+  `node_by_leverage()`, `node_by_multidegree()`,
+  `node_by_neighbours_degree()`, `node_by_outdegree()`,
+  `node_by_pagerank()`, `node_by_posneg()`, `node_by_power()`,
+  `node_by_randomwalk()`, `node_by_reach()`, `node_by_reciprocity()`,
+  `node_by_recovery()`, `node_by_redundancy()`, `node_by_richness()`,
+  `node_by_stress()`, `node_by_subgraph()`, `node_by_thresholds()`,
+  `node_by_transitivity()`, `node_by_vitality()`
+- Tie-level: `tie_by_betweenness()`, `tie_by_closeness()`,
+  `tie_by_cohesion()`, `tie_by_degree()`, `tie_by_eigenvector()`
+
+### Motifs
+- `net_x_brokerage()`, `net_x_dyad()`, `net_x_hazard()`,
+  `net_x_mixed()`, `net_x_tetrad()`, `net_x_triad()`
+- `node_x_brokerage()`, `node_x_dyad()`, `node_x_exposure()`,
+  `node_x_path()`, `node_x_tetrad()`, `node_x_tie()`, `node_x_triad()`
+
+### Memberships
+- `node_in_adopter()`, `node_in_automorphic()`,
+  `node_in_betweenness()`, `node_in_brokering()`,
+  `node_in_community()`, `node_in_component()`, `node_in_core()`,
+  `node_in_eigen()`, `node_in_equivalence()`, `node_in_fluid()`,
+  `node_in_greedy()`, `node_in_infomap()`, `node_in_leiden()`,
+  `node_in_louvain()`, `node_in_optimal()`, `node_in_partition()`,
+  `node_in_regular()`, `node_in_roulette()`, `node_in_spinglass()`,
+  `node_in_strong()`, `node_in_structural()`, `node_in_walktrap()`,
+  `node_in_weak()`
+
+## Bug fixes
+
+- `node_is_isolate()` and `node_is_pendant()` now work correctly with signed networks.
+- `tie_is_random()` now correctly returns a `tie_mark` class object (previously returned a node mark).
+- `node_by_authority()` and `node_by_hub()` updated to use current `{igraph}` API.
+- `node_by_brokering_activity()` and `node_by_brokering_exclusivity()` now handle unlabelled networks correctly.
+- `node_by_homophily()` no longer resolves the attribute to a vector prematurely.
+- `node_by_pagerank()` updated to correctly extract the vector output from `{igraph}`.
+- `node_by_power()` reverts to a lower exponent (closer to degree centrality) when there is no degree variation.
+- `node_by_randomwalk()` now works with two-mode networks.
+- `net_by_degree()`, `net_by_harmonic()`, and `net_by_reach()` now consistently include the function call in the returned object.
+- `net_by_richclub()` returns 0 (rather than erroring) when all nodes have equivalent degree.
+- `net_by_smallworld()` and `node_by_bridges()` now use internal `{netrics}` functions rather than `{manynet}` equivalents.
+- `net_by_waves()` returns 1 for cross-sectional networks and correctly returns a network measure class.
+- `net_x_hierarchy()` correctly classified as a motif function.
+- `node_in_community()` now delegates to `{netrics}` membership functions internally.
+- Dyad census fixed to handle two-mode networks.
+- Equivalence *k*-assignment fixed for the degenerate case where every node is placed in the same cluster.
+- `tie_by_cohesion()` now correctly returns a `tie_measure` class object.