Functions and Classes in Sequenzo

This page lists all the tools (functions) and building blocks (classes) available in Sequenzo. Think of it as a complete catalog of everything you can do with the package. Click on any item to see detailed instructions and examples.

Core Building Blocks

• SequenceData - The foundation class for working with your sequence data

Data Preparation Tools

• Data format conversion
  • wide_to_long_format_data() and long_to_wide_format_data() - Convert between wide and long data formats
• handle_missing_values() - Check missing data points
• assign_unique_ids() - Generate stable identifiers for sequences
• clean_time_columns() - Parse and normalize time-related columns
• replace_cluster_id_by_labels() - Replace numeric cluster IDs with labels

Sequence Analysis

Visualization

• plot_sequence_index() - Create visual timeline of sequences
• plot_state_distribution() - Show how states change over time
• plot_modal_state() - Visualize the most common state at each time
• plot_most_frequent_sequences() - Display the most common sequences
• plot_mean_time() - Average time spent in each state
• plot_transition_matrix() - Visualize transitions between states
• plot_relative_frequency() - Relative frequency of states
• plot_single_medoid() - Plot a representative sequence for a cluster

Measuring Dissimilarities Between Sequences

• get_distance_matrix() - Calculate how different sequences are from each other

Clustering Analysis: Grouping Similar Sequences

• KMedoids - Partitioning-based clustering for sequences
• Cluster() - Overview of hierarchical clustering for sequences, and Cluster()
• ClusterQuality() - Evaluate clustering quality
• ClusterResults() - Inspect results and extract representatives

Tools for Large Datasets

• CLARA (KMedoids for large datasets) - Efficient clustering on large datasets