cograph 2.3.6

Bug fixes

• Removed the cluster_network() alias for summarize_network(). It collided with Nestimate::cluster_network() — a completely different function (PAM clustering on sequence data, one network per cluster) — and the two silently masked each other depending on package attach order, producing confusing unused arguments (k = ..., cluster_by = ...) errors. Use summarize_network() (or its remaining short form cnet()) for matrix-to-cluster aggregation in cograph.

cograph 2.3.5

Documentation

• Added Sonsoles López-Pernas as co-copyright holder in LICENSE.
• README / docs wording fixes (e.g. “hyper order” → “higher-order”).
• Introduction vignette no longer asserts a fixed centrality-measure count, which kept drifting as the measure set grew.

cograph 2.3.4

Bug fixes

• .smooth_blob() (used by plot_simplicial() and overlay_communities()) now guards grDevices::chull() against non-finite anchor coordinates. Previously a node lacking layout coordinates (NA/Inf) aborted the blob with “finite coordinates are needed”; such anchors are now dropped before the convex-hull step.

cograph 2.3.3

Documentation

• Aligned the motifs() / subgraphs() roxygen documentation with the post-audit behavior shipped in 2.3.2 (census type_summary counts, min_count handling, and corrected plot legend descriptions).

cograph 2.3.2

Bug fixes

• Full audit pass over the motifs subsystem: type_summary now holds real MAN-type counts in census mode, min_count is honored in census mode, and the swapped source/target color description in plot.cograph_motif_result() is corrected.
• Unbroke motifs() and plot_simplicial() on Nestimate-backed workflows (HON / HYPA sequence inputs).
• panel_layout(): tightened dimension validation and made the restoration claim honest — it now restores only the par() settings it actually changed.

cograph 2.3.1

Multi-panel layout control

• New combined argument (default TRUE) on every multi-panel plot function: splot() group-cascade, plot_netobject_group(), plot_netobject_ml(), plot_net_bootstrap_group(), plot_group_permutation(), plot_compare(), splot.net_mlvar(), plot_network_evolution(), plot.cograph_motifs(), plot.cograph_motif_result(), plot.cograph_motif_analysis(), and plot.tna_disparity(). With combined = FALSE these functions draw panels into the active device without calling graphics::par(mfrow=...), so callers can drive their own layout (e.g. graphics::layout() or the new panel_layout() helper). Default TRUE preserves prior behavior — every existing call site renders identically.
• New panel_layout() helper sets up a custom multi-panel device layout for use with combined = FALSE. Accepts either a uniform-grid c(nrow, ncol) or a graphics::layout() matrix for non-uniform layouts (e.g. one wide panel + two narrow ones). Returns a par() snapshot for restoration via on.exit().

Test suite hygiene

• test-coverage-splot-{41,42}.R: bumped n_nodes from 4 to 10 in seven per-edge attribute tests so the seed=42 sampler does not produce duplicate (1,2) pairs that trip cograph’s undirected-duplicate-edge detector.
• test-coverage-class-network-41.R: aligned the set_layout_coords() mismatched-row-count test with the strict input validation already enforced by R/class-network.R.
• test-overlay-communities.R: prefixed two communities() calls with cograph:: to avoid tna masking when both packages are loaded in the suite (per CLAUDE.md “namespace masking” gotcha).

cograph 2.3.0

Documentation

• Audited every R/*.R function file for roxygen/Rd accuracy. Corrected stale defaults (cr_color #D4820A -> #D4829A in plot-forest.R; show_value default FALSE -> TRUE in splot-nodes.R), corrected dataset dimensions in data-hai.R (302 -> 429 x 287), corrected a reference to the nonexistent igraph::is_bipartite() (now bipartite_mapping()), expanded centrality() @param measure lists for mode, cutoff, invert_weights, and membership to match the implementation, dropped baked-in measure counts that rot on each addition, and removed nonexistent themes from sn_theme documentation. No runtime behavior changes from the documentation pass itself.

Bug fixes

• plot_simplicial() now warns when anomaly is set on an input that has no anomaly concept (HON, association rules, link prediction, character pathways, method = "hon" / "rules"). Previously the argument was silently dropped, so calls like plot_simplicial(hon, anomaly = "over") and plot_simplicial(hon, anomaly = "under") produced byte-identical plots. anomaly is honored only for net_hypa inputs and method = "hypa" auto-builds.

Centrality

• centrality() gains an umbrella argument tna_network (logical or NULL). When TRUE (or auto-detected from a tna/group_tna/ctna/ ftna/atna input), all measures shared with tna::centralities() match byte-for-byte: loops = FALSE, invert_weights = TRUE, diffusion_method = "power_series", transitivity_type = "onnela". Side-by-side audit confirms zero divergence on OutStrength, InStrength, ClosenessIn/Out/All, Betweenness, Diffusion, Clustering (max|diff| = 0). Any per-argument override the user passes explicitly always wins over the umbrella.
• centrality() (and centrality_diffusion()) gain a diffusion_method = c("kandhway_kuri", "power_series") argument. The default NULL auto-detects: "power_series" for tna inputs (matches tna::centralities(., measures = "Diffusion") byte-for-byte when loops = FALSE), "kandhway_kuri" (the existing 1-hop binary-degree formula, Kandhway & Kuri 2014) for everything else. Previously cograph’s diffusion silently disagreed with tna’s because cograph used an unweighted neighborhood-degree sum while tna uses rowSums(P + P^2 + ... + P^n) on the diagonal-zeroed weighted matrix — the same name covered two different statistics. Set explicitly to override the auto-detect.

Tests

• Added a regression test in tests/testthat/test-validate-nestimate-bootstrap-permutation.R asserting that centrality() on a Nestimate netobject agrees with centrality() on its $weights matrix when the diagonal is non-zero. Locks in the upstream Nestimate fix to .extract_edges_from_matrix() (Nestimate >= 2026-05-02) which now preserves self-loops in $edges. Without that fix, loop-bearing netobjects (e.g. Nestimate::build_mcml() |> Nestimate::as_tna()) silently under-counted node degree by 2.

Plotting — edge-label cex coupling (Phase 2)

• Default edge_label_size is now coupled to the node label cex at a fixed 0.55 fraction (edge_cex = 0.55 * mean(node_label_cex)) so the node-to-edge-label ratio stays a stable ~1.82x across canvases. This replaces the previous EDGE_LABEL_SCALE_CAP-based compensation, which let the ratio drift from 2.5x at reference to 3.6x at poster canvases because edge labels were clamped to a tighter 1.6 ceiling while node labels scaled freely to 2.3. The visible effect: edge weight annotations are now readable at poster sizes instead of shrinking relative to node labels. User-explicit edge_label_size still wins and receives the same (capped) visual-scale compensation as before; only the default path changed.
• Edge-label visual_scale resolution moved from render_edges_splot() into splot.R so the final cex is produced in a single place.

Plotting — device-aware visual scaling

• splot() now applies device-dependent compensation to text, line, and point sizes so visual ratios (label-to-node, legend-to-plot, edge thickness) stay consistent when the output device changes. This fixes the long-standing “labels too big at high DPI” and “legend desynchronised from the plot” issues when saving PNGs at res = 300 or res = 600 with pixel-default width/height, and when resizing the RStudio plot pane. Implementation: a single compute_visual_scale() reads the active device’s canvas size (dev.size("in")) and returns multipliers keyed off a 5.9-inch reference (matching the default RStudio 7×5” pane so backward-compatible behaviour at the default canvas is preserved). Multipliers are clamped to [0.55, 1.9] to keep thumbnails and posters legible. See the new R/visual-scale.R.
• New scaling = "fixed" mode on splot() — and corresponding global option options(cograph.visual_scaling = FALSE) — disables device compensation for reproducibility-sensitive workflows that calibrated against the previous behaviour.
• splot() return value now carries two attributes for downstream tooling: cograph.visual_scale (the multiplier list) and cograph.node_diam_in (the representative node diameter in inches at the rendered device).
• The splot-internal render_legend_splot() plus the new shared .render_legend_base() (R/render-legend-shared.R) replace the ad-hoc legend cex/pt.cex handling with a single compensated path. plot_htna, plot_mtna, plot_mlna, plot_mcml still use their historical scale multiplier arguments; Phase 2 will migrate them to the shared helper.

Plotting

• splot.netobject now routes on the Nestimate $method slot rather than just direction. Undirected sequence-based networks from build_cna() and wtna(method = "cooccurrence") get oval TNA-family styling (layout, palette, donuts) with arrows and dotted edge starts automatically dropped because the matrix is symmetric. Glasso / cor / pcor / ising networks still get psych_styling = TRUE (spring layout, Okabe-Ito palette).
• from_tna() auto-detects integer-valued weight matrices (ftna, ctna, raw counts) and sets weight_digits = edge_label_digits = 0 so edge labels render as 2304 rather than 2304.00. Fractional weights still format to two decimals. Explicit user-supplied weight_digits still wins.
• psych_styling = TRUE is now exported as a first-class styling preset (undirected counterpart of tna_styling) — Okabe-Ito palette, spring layout, no arrows — applied by default to splot.netobject on correlation-family input and to the $contemporaneous / $between constituents of net_mlvar.
• Expanded splot() dispatch coverage across the tna and Nestimate class hierarchies, ensuring tna, ftna, ctna, group_tna, tna_bootstrap, group_tna_bootstrap, tna_permutation, group_tna_permutation, netobject, netobject_group, netobject_ml, net_mlvar, wtna_mixed, net_bootstrap, net_permutation, boot_glasso, mcml, net_hon, net_hypa, and simplicial_complex all reach the correct renderer.
• Self-loops are now preserved in every plot function.

Correctness fixes (audit-driven)

• detect_duplicate_edges(), aggregate_duplicate_edges(), simplify.cograph_network(), and the internal check_duplicate_edges() helper now respect directed vs undirected semantics. Previously the canonical (min/max) endpoint key collapsed A -> B and B -> A into one edge even on directed graphs, matching igraph::simplify() ground truth.
• .compute_modularity() replaces a nested for loop with cluster-wise vectorization (sum(A[idx, idx]) - sum(k_out[idx]) * sum(k_in[idx]) / m), per the project “no for loops” rule. Results verified bit-exact against igraph::modularity().
• is_directed() now recognises CographNetwork R6 objects — previously only the cograph_network list format dispatched correctly.
• compute_layout_for_cograph() uses layout$get_type() instead of the removed $name field on CographLayout.
• network_small_world() returns 0 (valid: no triangles means definitively not small-world) instead of NA_real_ when the observed clustering coefficient is zero but path length is finite.
• simplify.cograph_network() threads the directed flag through to edge aggregation so directed multigraphs collapse correctly.

Performance & documentation

• simplify() performance refactor for large networks plus a cleaner title-composition path.
• motifs(), extract_motifs(), and plot.cograph_motif_analysis examples reworked to use n_perm = 10L (or significance = FALSE) and promoted from \dontrun to CRAN-runnable (optional tna branches stay in \donttest). Retires 320 seconds of latent CRAN timing risk — every example now runs in under 4 seconds.

New tests

• test-audit-fixes.R — ground-truth regressions for the directed edge semantics, modularity vectorization, and small-world behaviour changes.
• test-integer-weight-labels.R — locks from_tna() integer-weight auto-detect behaviour and precedence of explicit weight_digits.
• test-equiv-{assortativity, cluster-quality, communities, disparity, edge-centrality, network-summary, robustness, standalone-measures}.R — numerical equivalence against igraph, sna, centiserve, brainGraph, influenceR, tidygraph, and NetworkX. Gated by skip_coverage_tests() + skip_on_cran(), so they do not run on the CRAN pipeline.

cograph 2.1.0

New Features

Batch 6 — new-API graph-level / set-level / pair-level measures

These measures don’t fit the per-node centrality() data frame, so they live as standalone functions:

• estrada_index() — graph-level spectral invariant: , equal to the trace of the matrix exponential of the adjacency. Equivalently, the sum of subgraph_centrality() across all nodes. Matches networkx.estrada_index at machine epsilon (max relative diff ~5e-15 across random test graphs).
• trophic_incoherence() — graph-level food-web stability measure (Johnson et al. 2014). Defined as the population standard deviation of per-edge trophic differences where is the trophic level of node . Zero for perfectly coherent DAGs (e.g., a pure chain). Matches networkx.trophic_incoherence_parameter at machine epsilon. Directed-only; reuses the existing trophic_level calculator.
• group_centrality(x, nodes, measure = c("betweenness", "closeness", "degree")) — Everett-Borgatti (1999) group centrality for a set of nodes. Returns a scalar. Supports mode = "in"/"out" for directed-degree variants. Group closeness and group degree match networkx.group_*_centrality bit-exact. Group betweenness implements the textbook Everett-Borgatti / Puzis 2008 definition (fraction of shortest paths passing through at least one node in the group), which diverges from networkx.group_betweenness_centrality on some graphs due to a known quirk in NetworkX’s Puzis-Yahalom-Elovici iterative algorithm. Verified via an independent Python brute-force: cograph matches the textbook definition; NX produces larger values on graphs with many overlapping shortest paths. Documented in the roxygen “Divergence from NetworkX” section.
• dispersion(x, u = NULL, v = NULL, normalized = TRUE, alpha = 1, b = 0, c = 0) — Backstrom-Kleinberg (2014 Facebook) pair-level measure of tie strength. Counts the number of “well-dispersed” mutual friends of u and v (pairs of common neighbors that are not directly connected and share no common neighbor inside u’s ego network other than u and v). Matches networkx.dispersion bit-exact across all 156 edges on the karate club graph. Returns a scalar, named vector, or data frame depending on which of u, v are specified.

Centrality Batch 5 — Gould-Fernandez brokerage (5 roles)

Added the five Gould-Fernandez (1989) brokerage role counts, a foundational measure in social network analysis (~1500 citations). Each role is a separate per-node measure requiring a membership argument (following the same pattern as participation, within_module_z, gateway), and counts open directed 2-paths a -> v -> c through broker v:

• centrality_brokerage_coordinator() — all three in broker’s group (w_I)
• centrality_brokerage_itinerant() — endpoints same group, broker different (w_O, “consultant”)
• centrality_brokerage_representative() — broker + source same, target different (b_IO)
• centrality_brokerage_gatekeeper() — broker + target same, source different (b_OI)
• centrality_brokerage_liaison() — all three in different groups (b_O)

Bit-exact match against sna::brokerage$raw.nli for all five roles across 20 random directed graphs. Implemented natively (no runtime dependency on sna). Key implementation detail: the Gould-Fernandez counting rule requires open 2-paths only — triads where a direct edge a -> c already exists are excluded. This matches sna’s C implementation exactly and was derived empirically (sna’s .C("brokerage_R", ...) has no R-level source).

Directed-only; warns and returns NA on undirected input.

Centrality Batch 4 — directed prestige family (Wasserman-Faust / sna)

• centrality_prestige_domain() — directed-graph prestige measure: for each node , the number of other nodes that can reach via a directed path. Classical Wasserman-Faust (1994) measure from sna::prestige(cmode = "domain"). Bit-exact match against sna, implemented natively via igraph::distances(mode = "out") + colSums(is.finite(D)) - 1 (no runtime dependency on sna). Directed-only; returns NA with a warning on undirected input.
• centrality_prestige_domain_proximity() — distance-weighted variant: R_v^2 / (D_v * (n - 1)) where R_v is the number of reachers and D_v is the sum of their geodesic distances to v. Bit-exact match against sna::prestige(cmode = "domain.proximity") on strongly connected directed graphs. On graphs with any unreachable pair, sna has a known bug (FALSE * Inf = NaN collapses the denominator, producing all-zero output); cograph’s is.finite()-masked formula produces mathematically correct values on any directed graph. Directed-only.

Centrality Batch 3 — classical measures with reference-package validation

• centrality_katz() — Katz (1953) status index. Bit-exact match against centiserve::katzcent (cograph mirrors centiserve’s exact LAPACK call sequence). Also matches igraph::alpha_centrality(exo = 1) and networkx.katz_centrality_numpy at machine epsilon. New katz_alpha parameter (default 0.1).
• centrality_hubbell() — Hubbell (1965) input-output centrality. Bit-exact match against centiserve::hubbell (cograph mirrors centiserve’s full-inverse LAPACK call path). Note: centiserve’s default (weights = NULL) silently ignores E(g)$weight; to reproduce cograph’s behavior with centiserve on weighted graphs, pass weights = igraph::E(g)$weight explicitly. New hubbell_weight parameter (default 0.5).
• centrality_information() — Stephenson-Zelen (1989) information centrality. Bit-exact match against sna::infocent on connected undirected graphs (cograph mirrors sna’s exact construction and solve() call sequence).
• centrality_pairwisedis() — Pairwise disconnectivity (Potapov et al. 2008). Directed-only; fraction of reachable ordered pairs that become unreachable when each node is removed. Bit-exact match against centiserve::pairwisedis. Warns and returns NA on undirected input, matching the convention used by salsa, leaderrank, and trophic_level.
• centrality_reaching_local() / reaching_global() — Local and global reaching centrality (Mones, Vicsek & Vicsek 2012). Bit-exact match against networkx.local_reaching_centrality across the directed unweighted, undirected unweighted, and weighted branches. Undirected unweighted LRC coincides with igraph::harmonic_centrality(normalized = TRUE) (documented). reaching_global() is a graph-level hierarchy statistic in [0, 1].

cograph 1.8.2

New Features

• plot_simplicial() now accepts tna, netobject, net_hon, and net_hypa objects directly — higher-order pathways are auto-built and visualized with proper state labels, no manual extraction needed. New parameters: method ("hon" / "hypa"), max_pathways, ncol. Dismantled mode uses gridExtra grid layout with scaled nodes
• print.cograph_network() now shows a structured summary: node/edge counts, density, reciprocity, weight range, and top-degree nodes — replacing the minimal R6 default output
• Added mcml S3 class with as_mcml() generic for type-safe handling of Markov Chain Multi-Level models — enables print(), plot(), and method dispatch on MCML objects
• Added local %||% operator for R 4.1 compatibility (no longer requires R 4.4+)

Breaking Changes

• MCML field names renamed for clarity: $between → $macro, $within → $clusters
• as_tna() on MCML objects now returns a flat group_tna list instead of a nested structure

Bug Fixes

• plot_mcml() now suppresses zero-weight edges instead of drawing invisible lines, and strips leading zeros from edge labels (.32 instead of 0.32)
• Self-loops in cluster_summary() are now preserved in the macro diagonal, reflecting intra-cluster retention rates
• Sequence data is properly propagated through the full tna → macro → cluster pipeline, so downstream models can use bootstrap and permutation tests

cograph 1.8.0

New Features

• Added overlay_communities() for drawing community blob overlays on any network plot — accepts method names, membership vectors, or pre-computed community objects
• Added plot_simplicial() for higher-order pathway visualization, rendering simplicial complexes as smooth blobs with flexible separators and a dismantled view option
• Added value_nudge parameter to plot_transitions() for controlling the distance between flow labels and nodes
• Added bundle legend label controls: bundle_legend_size, bundle_legend_color, bundle_legend_fontface, bundle_legend_position
• Added per-function label controls (label_size, label_color, label_fontface, label_hjust) to plot_transitions(), plot_trajectories(), and plot_alluvial()

Bug Fixes

• Fixed spiky text halo artifacts in transition and heatmap plots by increasing circular offset directions from 8 to 16 (22.5° spacing for smooth outlines)

cograph 1.7.0

New Features

Cluster Analysis

• Added cluster_summary() for aggregating network weights at the cluster level, producing between-cluster and within-cluster matrices from raw transition data
• Added build_mcml() for constructing Markov Chain Multi-Level models from edge lists or sequence data with automatic cluster detection
• Added cluster_quality() for modularity-based cluster quality metrics and cluster_significance() for permutation-based significance testing
• Added as_tna() to convert cluster summaries to TNA objects for bootstrapping, permutation testing, and plotting with splot()

Network Operations

• Added simplify() for pruning weak edges from networks, with configurable weight threshold and aggregation method
• Added disparity_filter() for backbone extraction (Serrano et al. 2009), with methods for matrices, tna, igraph, and cograph_network objects
• Added robustness() for network robustness analysis with targeted (betweenness, degree) and random attack strategies, plus ggplot_robustness() for faceted ggplot2 output
• Added temporal_edge_list() for converting sequence data to timestamped edge lists
• Added supra_adjacency(), supra_layer(), supra_interlayer() for multilayer supra-adjacency matrix construction
• Added layer_similarity(), layer_similarity_matrix(), and layer_degree_correlation() for comparing layers in multilayer networks
• Added aggregate_weights() and aggregate_layers() for weight aggregation across layers
• Added verify_with_igraph() for cross-validating cograph centrality and network metrics against igraph

Motif Analysis

• Added motifs() / subgraphs() as a unified API for triad census (node-exchangeable counts) and instance extraction (named node triples), with auto-detection of actor/session columns, rolling/tumbling window support, and exact configuration model significance testing

Visualization

• Added plot_mcml() for Markov Chain Multi-Level visualization showing between-cluster summary edges alongside within-cluster detail, with pie charts, self-loops, and 22 customization parameters
• Added plot_chord() for native chord diagrams with automatic weight-based arc sizing
• Added plot_time_line() for cluster membership timeline visualization
• Added plot_htna() orientations: "facing" (tip-to-tip columns) and "circular" (two semicircles), plus intra_curvature for drawing intra-group edges as dotted bezier arcs
• Added threshold parameter to all plot functions for filtering edges/cells below a minimum absolute weight
• Added value_fontface, value_fontfamily, and value_halo parameters to plot_heatmap() for text styling control
• Added directional shorthands for scale_nodes_by: indegree, outdegree, instrength, outstrength, incloseness, outcloseness, inharmonic, outharmonic, ineccentricity, outeccentricity
• Added scale_nodes_scale parameter to splot() for dampening (< 1) or exaggerating (> 1) centrality-based node sizing differences
• Added qgraph argument translation in splot(): when plotting tna objects, qgraph-style parameters (vsize, asize, edge.color, lty, shape) are automatically mapped to cograph equivalents

Transition Plot Enhancements

• Added intermediate labels with node_label_format (e.g., "{state} (n={count})") for showing counts on transition plot nodes
• Added line bundling via bundle_size for aggregating individual trajectories into weighted summary lines in large datasets
• Added flow value labels via show_values / value_position for displaying transition counts on flow lines
• Added label_position consistency across ALL columns (first, middle, last) in trajectory plots

Data & Infrastructure

• Added example datasets: gamer_data, group_engagement, srl_data
• Added set_node_groups() / get_node_groups() for managing cluster assignments on cograph_network objects
• Consolidated cograph_network metadata under $meta with getter/setter functions
• Added group_tna support to splot() for direct plotting of grouped TNA models
• Gave each centrality_* wrapper its own focused help page

Bug Fixes

• Fixed load and percolation centrality computation: the BFS assumed unit edge weights, causing infinite loops on weighted graphs; directed graphs now transpose correctly (matching sna convention); disconnected nodes no longer contribute spurious centrality
• Fixed self-loop and edge clipping in splot() viewport calculation
• Fixed argument forwarding in splot dispatch for bootstrap/permutation objects — named parameters (minimum, threshold, layout, title) were consumed by splot()’s signature and silently dropped when dispatching
• Fixed overlapping flow value labels in multi-step alluvial plots
• Fixed alluvial label halo rendering producing spike artifacts (8 → 16 offset directions)
• Fixed viridis palette direction in plot_heatmap() so high values get dark colors
• Fixed build_mcml() density method crash when weight vector had no names
• Fixed display label priority resolution (labels > label > identifier)
• Removed zero-value labels that appeared after rounding in transition plots

Improvements

• Simplified splot dispatch: extracted .collect_dispatch_args() helper to replace 6 copy-paste dispatch blocks, using match.call() + mget() for reliable argument capture

cograph 1.6.0

New Features

Centrality

• Added centrality() with 23 measures and individual wrappers: degree, strength, betweenness, closeness, eigenvector, pagerank, harmonic, authority, hub, alpha, power, kreach, diffusion, percolation, eccentricity, transitivity, constraint, coreness, load, subgraph, leverage, laplacian, current-flow betweenness, current-flow closeness, voterank
• Added edge_betweenness() for edge-level centrality
• Added automatic weight inversion for path-based measures when working with tna transition matrices (where higher weight = stronger connection, not shorter distance)

Community Detection

• Added detect_communities() with 11 algorithms: louvain, walktrap, fast_greedy, label_propagation, leading_eigenvector, infomap, spinglass, leiden, optimal, edge_betweenness, multilevel — plus com_* shorthand aliases
• Added consensus clustering and cluster_significance() for permutation-based validation

Network Metrics

• Added network_summary() and summarize_network() for computing comprehensive network-level statistics (density, reciprocity, transitivity, diameter, components, degree distribution)

Visualization

• Added plot_transitions() for alluvial/Sankey flow diagrams, with plot_alluvial() and plot_trajectories() wrappers
• Added plot_bootstrap() and plot_permutation() for significance-styled visualization of bootstrap and permutation test results — significant edges rendered solid on top, non-significant edges dashed behind
• Added plot_mixed_network() for overlaying symmetric (undirected, straight) and asymmetric (directed, curved) edges on the same network
• Added plot_heatmap() for adjacency matrix heatmaps with optional hierarchical clustering and plot_ml_heatmap() for multilayer 3D perspective heatmaps
• Added plot_compare() for difference network visualization showing edge-weight changes between two networks
• Added splot() S3 methods for tna_bootstrap and tna_permutation objects

Motif Analysis

• Added motif_census(), triad_census(), and extract_motifs() for triad motif analysis with pattern filtering, significance testing, and network diagram visualization

Network Utilities

• Added filter_edges(), subset_edges(), select_nodes(), select_edges() for flexible network subsetting
• Added set_groups() for storing cluster assignments on cograph_network objects with automatic dispatch to plot_htna() / plot_mtna()

Infrastructure

• All plot functions now accept cograph_network objects as input, in addition to matrices, igraph objects, and tna objects
• Layout computation is now lazy — coordinates are only calculated when first needed
• Improved layout_spring and layout_gephi_fr algorithms: vectorized attraction forces, edge aggregation for dense networks
• Renamed package from Sonnet to cograph

Bug Fixes

• Fixed par(pin) error on exit when plot device state was corrupted
• Fixed motif plot scaling and margins for different device sizes

cograph 1.5.2

Breaking Changes

• Standardized first parameter name to x across all plotting functions:
  • plot_tna(): input → x
  • plot_htna(): input → x (was model)
  • plot_mtna(): input → x (was model)
  • splot() already used x

Bug Fixes

• Fixed tplot() default margins causing tiny plots compared to splot()

Documentation

• Added qgraph to splot migration guide (vignettes/qgraph-to-splot.md)

cograph 1.5.1

Breaking Changes (with backwards compatibility)

The following parameters have been renamed for consistency. The old names still work but emit deprecation warnings:

Improvements

• edge_label_fontface now accepts string values (“plain”, “bold”, “italic”, “bold.italic”) in addition to numeric values

cograph 1.4.0

New Features

• Added mlna() for multilevel network visualization with 3D perspective
• Added mtna() for multi-cluster network visualization with shape-based cluster containers
• Added plot_htna() for hierarchical multi-group network layouts with polygon and circular arrangements
• Added tplot() as a qgraph drop-in replacement with automatic parameter translation
• Added arrow_angle parameter for customizable arrowhead geometry

Bug Fixes

• Fixed Rd cross-reference warning in splot documentation
• Fixed pie/donut segment divider lines rendering when border width is 0

cograph 1.3.1

New Features

• Added edge_start_dot_density parameter for TNA-style dotted edge starts indicating direction
• Added direct support for tna objects via from_tna() — no manual matrix extraction needed
• Added direct support for statnet network and qgraph objects as input
• Added auto-conversion of pie_values vector to donut_fill when all values are in [0,1]

Bug Fixes

• Fixed TNA visual defaults being silently overwritten in splot() when other parameters were specified
• Fixed self-loop edge labels overlapping the loop arc
• Fixed donut_shape validation rejecting custom SVG shapes
• Fixed title clipping when title text exceeded plot margins
• Fixed edge rendering crash on certain edge/node configurations
• Removed underscore prefix requirement for custom SVG shape names

cograph 1.2.7

Bug Fixes

• Fixed oversized nodes in from_qgraph() when a layout override was provided
• Fixed oval layout using independent axis scaling, which distorted aspect ratios — now uses uniform scaling via normalize_coords()
• Fixed edge label alignment in from_qgraph() by using a matrix intermediary for per-edge vector reordering
• Fixed nrow(el) crash: qgraph’s Edgelist is a list, not a data.frame
• Fixed oval layout node distortion and donut fill values when converting from qgraph

cograph 1.2.6

New Features

• Added donut_empty parameter for rendering unfilled donut nodes
• Added from_qgraph() for converting qgraph objects to cograph format, reading resolved graphAttributes for accurate parameter extraction

Bug Fixes

• Fixed oval layout_info guard causing errors on certain device configurations
• Fixed curvature extraction passing vector values instead of scalars

cograph 1.2.0

New Features

• Added soplot() for grid/ggplot2-based network plotting — full feature parity with splot() using a different rendering backend
• Added layout_oval() for oval/elliptical node arrangements
• Added layout_scale parameter to expand or contract the network layout, with "auto" mode for node-count-based scaling
• Added Gephi-style Fruchterman-Reingold layout algorithm
• Added edge_start_style parameter for visually indicating edge direction via styled start segments (dashed, dotted)

Bug Fixes

• Fixed soplot() curve direction and edge defaults diverging from splot() behavior
• Fixed rescale_layout distorting oval aspect ratios by switching to uniform scaling
• Fixed edge scaling producing abnormally thick edges on small networks
• Fixed par(pin) restoration error on plot device exit

cograph 1.1.0

New Features

• Added splot() — a base R graphics engine for network visualization using polygon(), lines(), and xspline(), providing better performance than grid-based rendering for large networks
• Added polygon-shaped donut nodes, custom SVG node shapes, and AI-generated shape support
• Added shadow/halo labels and fine-grained text control (fontface, fontfamily, hjust, vjust, angle)
• Added double donut nodes with separate inner/outer border controls
• Added edge CI (confidence interval) underlays and template-based edge labels
• Added comprehensive legend support: groups, edge color scales, and node size scales
• Added high-resolution output via sn_save() with configurable DPI
• Added edge curve modes, bidirectional arrows, self-loop rotation, and per-edge curve control

Bug Fixes

• Fixed donut rendering producing artifacts and simplified the donut_color API to accept 1 color (fill), 2 colors (fill + background), or n colors (per-node)
• Fixed arrow positioning and curve direction for qgraph-style edges
• Fixed edge label positioning to avoid overlap with edge lines
• Fixed self-loop rendering to use qgraph-style circular arcs
• Fixed arrow placement on non-square viewports
• Fixed reciprocal edge auto-separation causing edge crossings

cograph 1.0.0

• Initial release of cograph network visualization package