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 nodesprint.cograph_network() now shows a structured summary:
node/edge counts, density, reciprocity, weight range, and top-degree
nodes — replacing the minimal R6 default outputmcml 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%||% operator for R 4.1 compatibility (no
longer requires R 4.4+)$between →
$macro, $within → $clustersas_tna() on MCML objects now returns a flat
group_tna list instead of a nested structureplot_mcml() now suppresses zero-weight edges instead of
drawing invisible lines, and strips leading zeros from edge labels
(.32 instead of 0.32)cluster_summary() are now preserved in
the macro diagonal, reflecting intra-cluster retention ratesoverlay_communities() for drawing community blob
overlays on any network plot — accepts method names, membership vectors,
or pre-computed community objectsplot_simplicial() for higher-order pathway
visualization, rendering simplicial complexes as smooth blobs with
flexible separators and a dismantled view optionvalue_nudge parameter to
plot_transitions() for controlling the distance between
flow labels and nodesbundle_legend_size,
bundle_legend_color, bundle_legend_fontface,
bundle_legend_positionlabel_size,
label_color, label_fontface,
label_hjust) to plot_transitions(),
plot_trajectories(), and plot_alluvial()cluster_summary() for aggregating network weights
at the cluster level, producing between-cluster and within-cluster
matrices from raw transition databuild_mcml() for constructing Markov Chain
Multi-Level models from edge lists or sequence data with automatic
cluster detectioncluster_quality() for modularity-based cluster
quality metrics and cluster_significance() for
permutation-based significance testingas_tna() to convert cluster summaries to TNA
objects for bootstrapping, permutation testing, and plotting with
splot()simplify() for pruning weak edges from networks,
with configurable weight threshold and aggregation methoddisparity_filter() for backbone extraction
(Serrano et al. 2009), with methods for matrices, tna, igraph, and
cograph_network objectsrobustness() for network robustness analysis with
targeted (betweenness, degree) and random attack strategies, plus
ggplot_robustness() for faceted ggplot2 outputtemporal_edge_list() for converting sequence data
to timestamped edge listssupra_adjacency(), supra_layer(),
supra_interlayer() for multilayer supra-adjacency matrix
constructionlayer_similarity(),
layer_similarity_matrix(), and
layer_degree_correlation() for comparing layers in
multilayer networksaggregate_weights() and
aggregate_layers() for weight aggregation across
layersverify_with_igraph() for cross-validating cograph
centrality and network metrics against igraphmotifs() / 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 testingplot_mcml() for Markov Chain Multi-Level
visualization showing between-cluster summary edges alongside
within-cluster detail, with pie charts, self-loops, and 22 customization
parametersplot_chord() for native chord diagrams with
automatic weight-based arc sizingplot_time_line() for cluster membership timeline
visualizationplot_htna() orientations: "facing"
(tip-to-tip columns) and "circular" (two semicircles), plus
intra_curvature for drawing intra-group edges as dotted
bezier arcsthreshold parameter to all plot functions for
filtering edges/cells below a minimum absolute weightvalue_fontface, value_fontfamily,
and value_halo parameters to plot_heatmap()
for text styling controlscale_nodes_by:
indegree, outdegree, instrength,
outstrength, incloseness,
outcloseness, inharmonic,
outharmonic, ineccentricity,
outeccentricityscale_nodes_scale parameter to
splot() for dampening (< 1) or exaggerating (> 1)
centrality-based node sizing differencessplot(): when
plotting tna objects, qgraph-style parameters (vsize,
asize, edge.color, lty,
shape) are automatically mapped to cograph equivalentsnode_label_format (e.g.,
"{state} (n={count})") for showing counts on transition
plot nodesbundle_size for aggregating
individual trajectories into weighted summary lines in large
datasetsshow_values /
value_position for displaying transition counts on flow
lineslabel_position consistency across ALL columns
(first, middle, last) in trajectory plotsgamer_data,
group_engagement, srl_dataset_node_groups() /
get_node_groups() for managing cluster assignments on
cograph_network objects$meta with
getter/setter functionsgroup_tna support to splot() for
direct plotting of grouped TNA modelscentrality_* wrapper its own focused help
pagesplot() viewport
calculationsplot()’s signature and
silently dropped when dispatchingplot_heatmap() so
high values get dark colorsbuild_mcml() density method crash when weight
vector had no names.collect_dispatch_args() helper to replace 6 copy-paste
dispatch blocks, using match.call() + mget()
for reliable argument capturecentrality() 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, voterankedge_betweenness() for edge-level centralitydetect_communities() with 11 algorithms: louvain,
walktrap, fast_greedy, label_propagation, leading_eigenvector, infomap,
spinglass, leiden, optimal, edge_betweenness, multilevel — plus
com_* shorthand aliasescluster_significance()
for permutation-based validationnetwork_summary() and
summarize_network() for computing comprehensive
network-level statistics (density, reciprocity, transitivity, diameter,
components, degree distribution)plot_transitions() for alluvial/Sankey flow
diagrams, with plot_alluvial() and
plot_trajectories() wrappersplot_bootstrap() and
plot_permutation() for significance-styled visualization of
bootstrap and permutation test results — significant edges rendered
solid on top, non-significant edges dashed behindplot_mixed_network() for overlaying symmetric
(undirected, straight) and asymmetric (directed, curved) edges on the
same networkplot_heatmap() for adjacency matrix heatmaps with
optional hierarchical clustering and plot_ml_heatmap() for
multilayer 3D perspective heatmapsplot_compare() for difference network
visualization showing edge-weight changes between two networkssplot() S3 methods for tna_bootstrap
and tna_permutation objectsmotif_census(), triad_census(), and
extract_motifs() for triad motif analysis with pattern
filtering, significance testing, and network diagram visualizationfilter_edges(), subset_edges(),
select_nodes(), select_edges() for flexible
network subsettingset_groups() for storing cluster assignments on
cograph_network objects with automatic dispatch to
plot_htna() / plot_mtna()cograph_network objects
as input, in addition to matrices, igraph objects, and tna objectslayout_spring and layout_gephi_fr
algorithms: vectorized attraction forces, edge aggregation for dense
networkspar(pin) error on exit when plot device state was
corruptedx across all
plotting functions:
plot_tna(): input → xplot_htna(): input → x (was
model)plot_mtna(): input → x (was
model)splot() already used xtplot() default margins causing tiny plots
compared to splot()vignettes/qgraph-to-splot.md)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 |
edge_label_fontface now accepts string values (“plain”,
“bold”, “italic”, “bold.italic”) in addition to numeric valuesmlna() for multilevel network visualization with
3D perspectivemtna() for multi-cluster network visualization
with shape-based cluster containersplot_htna() for hierarchical multi-group network
layouts with polygon and circular arrangementstplot() as a qgraph drop-in replacement with
automatic parameter translationarrow_angle parameter for customizable arrowhead
geometryedge_start_dot_density parameter for TNA-style
dotted edge starts indicating directionfrom_tna() —
no manual matrix extraction needednetwork and
qgraph objects as inputpie_values vector to
donut_fill when all values are in [0,1]splot() when other parameters were specifieddonut_shape validation rejecting custom SVG
shapesfrom_qgraph() when a layout
override was providednormalize_coords()from_qgraph() by using a
matrix intermediary for per-edge vector reorderingnrow(el) crash: qgraph’s Edgelist is a list, not
a data.framedonut_empty parameter for rendering unfilled
donut nodesfrom_qgraph() for converting qgraph objects to
cograph format, reading resolved graphAttributes for
accurate parameter extractionlayout_info guard causing errors on certain
device configurationssoplot() for grid/ggplot2-based network plotting
— full feature parity with splot() using a different
rendering backendlayout_oval() for oval/elliptical node
arrangementslayout_scale parameter to expand or contract the
network layout, with "auto" mode for node-count-based
scalingedge_start_style parameter for visually
indicating edge direction via styled start segments (dashed,
dotted)soplot() curve direction and edge defaults
diverging from splot() behaviorrescale_layout distorting oval aspect ratios by
switching to uniform scalingpar(pin) restoration error on plot device
exitsplot() — a base R graphics engine for network
visualization using polygon(), lines(), and
xspline(), providing better performance than grid-based
rendering for large networkssn_save() with
configurable DPIdonut_color API to accept 1 color (fill), 2 colors (fill +
background), or n colors (per-node)