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cograph is a modern R package that provides tools for the analysis, visualization, and manipulation of dynamical, social and complex networks. The package supports multiple network formats and offers flexible tools for heterogeneous, multi-layer, and hierarchical network analysis with simple syntax and extensive tool-set.
Key features:
tna package# Install from CRAN (when available)
#install.packages("cograph")
# Or install the development version from GitHub
# install.packages("devtools")
#devtools::install_github("sonsoleslp/cograph")library(cograph)library(tna)
tna_obj <- tna(group_regulation)
splot(tna_obj)
# 10-node directed transition matrix (TNA-style)
set.seed(42)
states <- c("Explore", "Plan", "Monitor", "Evaluate", "Adapt",
"Reflect", "Regulate", "Execute", "Collaborate", "Review")
mat <- matrix(runif(100, 0, 0.3), nrow = 10, dimnames = list(states, states))
diag(mat) <- 0
mat <- mat / rowSums(mat) # row-normalizecograph supports statistical edge visualization with CI
underlays and significance notation.
# Publication-ready with CI underlays and labels
splot(mat,
edge_ci = runif(sum(mat > 0), 0.05, 0.2), layout = "oval",
edge_label_template = "{est}{stars}",
edge_label_p = runif(sum(mat > 0), 0, 0.1),
edge_label_stars = TRUE, edge_label_bg = "transparent", edge_label_color = "maroon",
edge_label_size = 0.5, edge_label_position = 0.6
)
Template placeholders: {est}, {low},
{up}, {range}, {p},
{stars}
set.seed(1)
# Each node gets a vector of pie segment values
pie_vals <- lapply(1:10, function(i) runif(4))
pie_cols <- c("#E41A1C", "#377EB8", "#4DAF4A", "#FF7F00")
splot(mat,
node_shape = "pie",
pie_values = pie_vals,
pie_colors = pie_cols, node_size = 10,
layout = "oval"
)
# Per-node color palettes
pie_cols_multi <- list(
c("#E63946", "#F1FAEE", "#A8DADC"),
c("#264653", "#2A9D8F", "#E9C46A"),
c("#F72585", "#7209B7", "#3A0CA3"),
c("#003049", "#D62828", "#F77F00"),
c("#606C38", "#283618", "#DDA15E"),
c("#0077B6", "#00B4D8", "#90E0EF"),
c("#9B2226", "#AE2012", "#BB3E03"),
c("#023047", "#219EBC", "#8ECAE6"),
c("#5F0F40", "#9A031E", "#FB8B24"),
c("#2D00F7", "#6A00F4", "#8900F2")
)
splot(mat,
node_shape = "pie",
pie_values = lapply(1:10, function(i) runif(3)),
pie_colors = pie_cols_multi,
node_size = 10,
layout = "oval"
)
fills <- runif(10, 0.3, 0.95)
# Per-node donut color palettes
donut_cols_multi <- list(
c("#003049", "#D62828", "#F77F00", "#FCBF49"),
c("#606C38", "#283618", "#DDA15E", "#BC6C25"),
c("#0077B6", "#00B4D8", "#90E0EF", "#CAF0F8"),
c("#9B2226", "#AE2012", "#BB3E03", "#CA6702"),
c("#5F0F40", "#9A031E", "#FB8B24", "#E36414"),
c("#023047", "#219EBC", "#8ECAE6", "#FFB703"),
c("#264653", "#2A9D8F", "#E9C46A", "#F4A261"),
c("#F72585", "#B5179E", "#7209B7", "#560BAD"),
c("#10002B", "#240046", "#3C096C", "#5A189A"),
c("#D8F3DC", "#B7E4C7", "#95D5B2", "#74C69D")
)
splot(mat,
donut_values = lapply(1:10, function(i) runif(4)),
donut_colors = donut_cols_multi,
donut_inner_ratio = 0.55,
node_size = 8
)
# Donut + Pie combo: outer donut ring with inner pie segments
splot(mat,
node_shape = "donut",
donut_fill = fills,
donut_shape = c("circle", "hexagon", "square", "diamond", "triangle",
"pentagon", "circle", "hexagon", "square", "diamond"),
donut_color = palette_viridis(10)
)
plot_htna() creates multi-group network layouts where
node groups are arranged in geometric patterns (bipartite, triangle,
rectangle, polygon, or circular).
layout(t(1:2)); par(mar=c(0,0,0,0))
# Create network with 3 groups
set.seed(42)
nodes <- paste0("N", 1:15)
m <- matrix(runif(225, 0, 0.3), 15, 15)
diag(m) <- 0
colnames(m) <- rownames(m) <- nodes
node_types <- list(
Teacher = paste0("N", 1:5),
Student = paste0("N", 6:10),
System = paste0("N", 11:15)
)
# Polygon layout (triangle for 3 groups)
plot_htna(m, node_types, layout = "polygon", minimum = 0.15)
# Circular layout (groups as arcs)
plot_htna(m, node_types, layout = "circular", minimum = 0.15)
plot_mtna() visualizes multiple network clusters with
summary edges between clusters and individual edges within clusters.
Each cluster is displayed as a shape (circle, square, diamond, triangle)
containing its nodes.
par(mar=c(0,0,0,0))
# Create network with 6 clusters
set.seed(42)
nodes <- paste0("N", 1:30)
m <- matrix(runif(900, 0, 0.3), 30, 30)
diag(m) <- 0
colnames(m) <- rownames(m) <- nodes
clusters <- list(
Alpha = paste0("N", 1:5),
Beta = paste0("N", 6:10),
Gamma = paste0("N", 11:15),
Delta = paste0("N", 16:20),
Epsilon = paste0("N", 21:25),
Zeta = paste0("N", 26:30)
)
# Summary edges between clusters + individual edges within
plot_mtna(m, clusters)
Key parameters: * spacing: Distance between cluster
centers * shape_size: Size of cluster shells *
node_spacing: Node placement within shapes (0-1) *
shapes: Vector of shapes per cluster (“circle”, “square”,
“diamond”, “triangle”) * summary_edges: Show aggregated
between-cluster edges (default TRUE) * within_edges: Show
individual within-cluster edges (default TRUE)
Alias:
mtna()is available as a shorthand forplot_mtna().
plot_mlna() visualizes multilevel/multiplex networks
where multiple layers are stacked in a 3D perspective view. Each layer
contains nodes connected by solid edges (within-layer), while dashed
lines connect nodes between adjacent layers (inter-layer edges).
par(mar=c(0,0,0,0))
# Create multilevel network
set.seed(42)
nodes <- paste0("N", 1:21)
m <- matrix(runif(441, 0, 0.3), 21, 21)
diag(m) <- 0
colnames(m) <- rownames(m) <- nodes
# Define 3 layers
layers <- list(
Macro = paste0("N", 1:7),
Meso = paste0("N", 8:14),
Micro = paste0("N", 15:21)
)
# Basic usage with spring layout
plot_mlna(m, layers, layout = "spring", minimum = 0.18, legend = FALSE)
MIT License. See LICENSE for details.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.