---
title: "Using loopnet: Directed Network Simulation and Feedback Analysis"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Using loopnet}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```r
# 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.