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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.

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.