cograph

cograph is a modern R package for the analysis, visualization, and manipulation of complex networks. It provides publication-ready plotting with customizable layouts, node shapes, edge styles, and themes through an intuitive, pipe-friendly API. First-class support for Transition Network Analysis (TNA), multilayer networks, and community detection.

Installation

# Install from CRAN
install.packages("cograph")

# Development version from GitHub
devtools::install_github("sonsoleslp/cograph")

Features

Network Plotting

Function	Description
`splot()`	Base R network plot (core engine)
`soplot()`	Grid/ggplot2 network rendering
`tplot()`	qgraph drop-in replacement for TNA
`plot_htna()`	Hierarchical multi-group TNA layouts
`plot_mtna()`	Multi-cluster TNA with shape containers
`plot_mcml()`	Markov Chain Multi-Level visualization
`plot_mlna()`	Multilayer 3D perspective networks
`plot_mixed_network()`	Combined symmetric/asymmetric edges

Flow and Comparison Plots

Function	Description
`plot_transitions()`	Alluvial/Sankey flow diagrams
`plot_alluvial()`	Alluvial wrapper with flow coloring
`plot_trajectories()`	Individual tracking with line bundling
`plot_chord()`	Chord diagrams with ticks
`plot_heatmap()`	Adjacency heatmaps with clustering
`plot_compare()`	Difference network visualization
`plot_bootstrap()`	Bootstrap CI result plots
`plot_permutation()`	Permutation test result plots

Community and Higher-Order Structure

Function	Description
`overlay_communities()`	Community blob overlays on network plots
`plot_simplicial()`	Higher-order pathway (simplicial complex) visualization
`detect_communities()`	11 igraph algorithms with shorthand wrappers
`communities()`	Unified community detection interface

Network Analysis

Function	Description
`centrality()`	23+ centrality measures with individual wrappers
`motifs()` / `subgraphs()`	Motif/triad census with per-actor windowing
`robustness()`	Network robustness analysis
`disparity_filter()`	Backbone extraction (Serrano et al. 2009)
`cluster_summary()`	Between/within cluster weight aggregation
`build_mcml()`	Markov Chain Multi-Level model construction
`summarize_network()`	Comprehensive network-level statistics
`verify_with_igraph()`	Cross-validation against igraph
`simplify()`	Prune weak edges

Multilayer Networks

Function	Description
`supra_adjacency()`	Supra-adjacency matrix construction
`layer_similarity()`	Layer comparison measures
`aggregate_layers()`	Weight aggregation across layers
`plot_ml_heatmap()`	Multilayer heatmaps with 3D perspective

Examples

TNA Plot

The primary use case: visualize transition networks from the tna package.

library(tna)
library(cograph)

# Build a TNA model from sequence data
fit <- tna(engagement)

# One-liner visualization
splot(fit)

Simple Network

library(cograph)

# Create a transition matrix
states <- c("Explore", "Plan", "Monitor", "Adapt", "Reflect")
mat <- matrix(
  c(0.0, 0.4, 0.2, 0.1, 0.3,
    0.3, 0.0, 0.3, 0.2, 0.2,
    0.2, 0.3, 0.0, 0.3, 0.2,
    0.1, 0.2, 0.4, 0.0, 0.3,
    0.2, 0.2, 0.2, 0.4, 0.0),
  nrow = 5, byrow = TRUE,
  dimnames = list(states, states)
)

splot(mat)

Layouts

par(mfrow = c(2, 2), mar = c(1, 1, 2, 1))
splot(mat, layout = "oval",   title = "oval")
splot(mat, layout = "circle", title = "circle")
splot(mat, layout = "kk",     title = "kk")
splot(mat, layout = "fr",     title = "fr")

Edge Styling

splot(mat,
  curvature = 0.3,
  arrow_size = 0.02,
  edge_width = 3
)

Node Shapes

shapes <- c("circle", "square", "hexagon", "diamond", "triangle")

splot(mat,
  node_shape = shapes,
  node_fill = c("#E63946", "#457B9D", "#2A9D8F", "#E9C46A", "#F4A261"),
  layout = "circle"
)

Donuts

Donut nodes show proportional fill with optional polygon shapes.

fills <- c(0.9, 0.7, 0.5, 0.3, 0.8)

splot(mat,
  donut_fill = fills,
  donut_color = "steelblue",
  donut_shape = c("circle", "hexagon", "square", "diamond", "triangle")
)

Pies

Pie chart nodes with per-node color palettes.

pie_vals <- list(
  c(0.5, 0.3, 0.2),
  c(0.4, 0.4, 0.2),
  c(0.3, 0.3, 0.4),
  c(0.6, 0.2, 0.2),
  c(0.2, 0.5, 0.3)
)

pie_cols <- list(
  c("#E63946", "#457B9D", "#2A9D8F"),
  c("#264653", "#E9C46A", "#F4A261"),
  c("#F72585", "#7209B7", "#3A0CA3"),
  c("#003049", "#D62828", "#F77F00"),
  c("#606C38", "#283618", "#DDA15E")
)

splot(mat,
  node_shape = "pie",
  pie_values = pie_vals,
  pie_colors = pie_cols,
  layout = "circle"
)

Donut + Pie Combo

Combine outer donut ring with inner pie segments.

splot(mat,
  donut_fill = fills,
  donut_color = "steelblue",
  pie_values = pie_vals,
  pie_colors = c("#E41A1C", "#377EB8", "#4DAF4A")
)

Chord Diagram

plot_chord(mat, title = "Transition Chord Diagram")

Heatmap

plot_heatmap(mat, show_values = TRUE, colors = "viridis",
             value_fontface = "bold", title = "Transition Heatmap")

Alluvial Flow

plot_transitions(mat, flow_color_by = "from", flow_alpha = 0.5,
                 from_title = "Source", to_title = "Target")

License

MIT License.

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