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# Load the loopnet package (after installation)
library(loopnet)
# Step 1: Create an undirected adjacency matrix (skeleton)
adj <- matrix(0, 9, 9)
adj[1, 2] <- adj[2, 1] <- 1
adj[2, 3] <- adj[3, 2] <- 1
adj[3, 4] <- adj[4, 3] <- 1
adj[4, 5] <- adj[5, 4] <- 1
adj[5, 6] <- adj[6, 5] <- 1
adj[6, 1] <- adj[1, 6] <- 1
# Step 2: Generate all possible directed networks
nets <- generate_directed_networks(adj)
length(nets) # Total configurations
# Step 3: Select one network and detect feedback loops
net1 <- nets[[1]]
loops <- detect_feedback_loops(net1)
str(loops)
# Step 4: Compute overlap and topological features
overlap <- compute_overlap_metrics(loops, n_nodes = nrow(adj))
topo <- summarize_topology(net1, loops)
# Step 5: Simulate dynamics from this network
params <- get_sample_parameters()
S <- simulate_from_network(net1, params, t_max = 50)
plot_symptom_dynamics(S)This vignette walks through the end-to-end use of
loopnet, from generating directed networks to simulating
dynamic behavior and analyzing feedback loop structure.
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.