# Extended Sequence Visualisations

## Synthetic data

    data <- data.frame(
      sequence_id = rep(paste0("s", 1:8), each = 6L),
      sequence_order = rep(1:6, times = 8L),
      state = c(
        rep(c("A", "B", "B", "C", "D", "D"), 4L),
        rep(c("D", "C", "C", "B", "A", "A"), 4L)
      ),
      stringsAsFactors = FALSE
    )
    distance <- compute_sequence_distance(data, method = "levenshtein")
    clustering <- cluster_sequences(distance, k = 2L, method = "hierarchical")
    network <- create_transition_network(data)

## Sequence index

    plot_sequence_index(data)

## State distribution and entropy

    plot_sequence_state_distribution(data)

    plot_sequence_entropy(data)

Entropy is a structural diversity summary at each aligned position. It is not a measure of participant uncertainty or cognition.

## Distance and clustering diagnostics

    plot_sequence_distance_heatmap(distance)

    plot_sequence_cluster_silhouette(clustering, distance)

## Transition network

    plot_transition_network(network)

These base-R plots are intentionally focused on package-native audited objects. They complement, rather than replace, specialist visualisation ecosystems.