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Changelog

Source: NEWS.md

cograph 2.1.2 (development)

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:

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

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

Network Utilities

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

CRAN release: 2026-03-02

Breaking Changes

Bug Fixes

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:

Old Name New Name Reason
esize edge_size Add edge_ prefix, expand abbreviation
cut edge_cutoff Add edge_ prefix, clarify meaning
usePCH use_pch Fix camelCase to snake_case
positive_color edge_positive_color Add edge_ prefix (matches theme storage)
negative_color edge_negative_color Add edge_ prefix (matches theme storage)
donut_border_lty donut_line_type Expand lty abbreviation

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