
# cograph <img src="https://sonsoles.me/cograph/reference/figures/logo.png" align="right" width="139" />

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**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

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

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

## How to use it?

- [Network Visualization with cograph: A Complete Plotting
  Guide](https://sonsoles.me/cograph/articles/1_cograph-tutorial-plotting.html)
- [Why cograph?](https://sonsoles.me/cograph/articles/2_why-cograph.html)
- [Plotting TNA Models with
  splot](https://sonsoles.me/cograph/articles/3_plotting-tna-models.html)
- [Visualization of communities and hyper order
  networks](https://sonsoles.me/cograph/articles/cograph-tutorial-communities.html)
- [Network Estimation and Visualization with Nestimate +
  cograph](https://sonsoles.me/cograph/articles/cograph-tutorial-nestimate.html)
- [Advanced
  examples](https://sonsoles.me/cograph/articles/mcml-examples.html)
- [Bootstrap Forest
  Plots](https://sonsoles.me/cograph/articles/bootstrap-forest.html)
- [Migrating from qgraph to
  splot](https://sonsoles.me/cograph/articles/qgraph-to-splot.html)

## 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()` | 87 centrality measures, validated against centiserve/sna/igraph/NetworkX |
| `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.

``` r
library(tna)
library(cograph)

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

# One-liner visualization
splot(fit)
```

<img src="man/figures/README-tna-plot-1.jpeg" alt="" width="100%" />

### Simple Network

``` r
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)
```

<img src="man/figures/README-simple-network-1.jpeg" alt="" width="100%" />

### Layouts

``` r
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")
```

<img src="man/figures/README-layouts-1.jpeg" alt="" width="100%" />

### Edge Styling

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

<img src="man/figures/README-edge-styling-1.jpeg" alt="" width="100%" />

### Node Shapes

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

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

<img src="man/figures/README-node-shapes-1.jpeg" alt="" width="100%" />

### Donuts

Donut nodes show proportional fill with optional polygon shapes.

``` r
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")
)
```

<img src="man/figures/README-donuts-1.jpeg" alt="" width="100%" />

### Pies

Pie chart nodes with per-node color palettes.

``` r
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"
)
```

<img src="man/figures/README-pies-1.jpeg" alt="" width="100%" />

### Donut + Pie Combo

Combine outer donut ring with inner pie segments.

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

<img src="man/figures/README-donut-pie-1.jpeg" alt="" width="100%" />

### Chord Diagram

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

<img src="man/figures/README-chord-1.jpeg" alt="" width="100%" />

### Heatmap

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

<img src="man/figures/README-heatmap-1.jpeg" alt="" width="100%" />

### Alluvial Flow

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

<img src="man/figures/README-alluvial-1.jpeg" alt="" width="100%" />

## License

MIT License.
