README.md

In gp3sequences: Transparent Analysis of Ordered Categorical Sequences

gp3sequences

Links: CRAN · Package website · Source code · Issue tracker · CRAN checks

gp3sequences is an independent R package for transparent, reproducible, and auditable analysis of ordered categorical sequences. It is designed for ordinary long-format data frames and is not restricted to Gazepoint exports, eye-tracking data, particular hardware, or proprietary software.

Installation

Install the current CRAN release:

install.packages("gp3sequences")

Install the current release version from GitHub:

pak::pak("stefanosbalaskas/gp3sequences")

The CRAN release is version 0.1.0, published on 30 July 2026. The source repository currently contains release version 0.3.0.

Current release status

The current release version is 0.3.0, building on the 0.2.0 release. The package currently exposes 81 public functions and the website provides 15 synthetic, reproducible articles.

The 0.3.0 release combines the audited sequence-data contract with consensus and group comparisons, sequence distances, clustering and stability workflows, transition networks, higher-order models, categorical and mixture hidden Markov models, longitudinal and panel workflows, bounded non-contiguous subsequences, time-varying models, multichannel and covariate-dependent HMMs, design-aware inference, extended visualisations, optional specialist-package adapters, and analysis-contract/provenance auditing.

Intended applications

Potential applications include:

• eye-tracking AOI scanpaths;
• website-navigation and customer-journey sequences;
• behavioural-state and task-action sequences;
• interface-interaction sequences;
• educational activity sequences;
• other repeated ordered categorical data.

Neutral long-format input

The core data contract will require configurable columns corresponding to sequence_id, sequence_order, and state. Optional participant, trial, condition, group, timing, duration, weight, and metadata columns may also be mapped explicitly.

A minimal input may look like this:

example_sequences <- data.frame(
  sequence_id = rep(c("s1", "s2"), each = 4L),
  sequence_order = rep(1:4, times = 2L),
  state = c(
    "home", "search", "product", "checkout",
    "home", "category", "product", "home"
  ),
  stringsAsFactors = FALSE
)

example_sequences
#>   sequence_id sequence_order    state
#> 1          s1              1     home
#> 2          s1              2   search
#> 3          s1              3  product
#> 4          s1              4 checkout
#> 5          s2              1     home
#> 6          s2              2 category
#> 7          s2              3  product
#> 8          s2              4     home

Current data-contract API

The core sequence-data contract provides three related functions:

• audit_sequence_data() reports structured data-quality issues;
• validate_sequence_data() returns a compact validity contract;
• prepare_sequence_data() applies explicit preprocessing policies and creates deterministic canonical sequence data.

audit <- audit_sequence_data(
  example_sequences,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state"
)

validation <- validate_sequence_data(
  example_sequences,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state"
)

prepared <- prepare_sequence_data(
  example_sequences,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state",
  repeated_state_policy = "preserve"
)

Analysis contracts and provenance

The 0.3.0 release adds three public functions for machine-readable capability, contract, provenance, and reproducibility auditing:

• sequence_capabilities() inventories native, adapter, reference, handoff, and development capabilities without loading optional backend namespaces;
• audit_sequence_analysis() validates supported analysis objects against explicit structural contracts and recovers provenance where possible;
• compare_sequence_analysis_results() compares contracts, provenance, settings, and optionally result values across two analysis objects.

These functions complement the sequence-data contract and provide the common foundation for the 0.3.0 hardening program.

Encoding and structural summaries

Related functions support deterministic encoding and descriptive sequence summaries without assigning substantive meaning to states:

• encode_sequence_data() creates a transparent state dictionary;
• summarise_sequence_states() reports state frequencies and durations;
• summarise_sequence_transitions() reports adjacent transition counts;
• format_sequence_paths() creates compact ordered path strings.

encoded <- encode_sequence_data(
  prepared$data,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state"
)

state_summary <- summarise_sequence_states(
  prepared$data,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state"
)

transition_summary <- summarise_sequence_transitions(
  prepared$data,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state"
)

paths <- format_sequence_paths(
  prepared$data,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state"
)

Contiguous motif analysis

The motif workflow enumerates contiguous state windows only and retains explicit structural definitions:

• extract_sequence_ngrams() extracts auditable motif occurrences;
• summarise_sequence_motifs() reports counts and sequence prevalence;
• filter_sequence_motifs() applies explicit transparent thresholds;
• format_sequence_motifs() creates stable report-ready tables.

motif_occurrences <- extract_sequence_ngrams(
  prepared$data,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state",
  min_length = 2,
  max_length = 4,
  overlap = "allow"
)

motif_summary <- summarise_sequence_motifs(motif_occurrences)

motif_filter <- filter_sequence_motifs(
  motif_summary,
  min_sequences = 2,
  min_prevalence = 0.10,
  top_n = 20,
  ties = "include"
)

motif_table <- format_sequence_motifs(
  motif_filter,
  prevalence = "percent",
  digits = 1
)

Motif positions and visualisation

Motif occurrences can be summarised and plotted without adding analytical dependencies:

• summarise_sequence_motif_positions() reports structural locations;
• format_sequence_motif_positions() produces explicit report tables;
• plot_sequence_motifs() plots prevalence or occurrence metrics;
• plot_sequence_motif_positions() plots occurrence locations.

position_summary <- summarise_sequence_motif_positions(
  motif_occurrences,
  position = "centre",
  scale = "relative"
)

position_table <- format_sequence_motif_positions(
  position_summary,
  position_units = "percent",
  digits = 1
)

plot_sequence_motifs(
  motif_summary,
  metric = "sequence_prevalence",
  top_n = 10
)

plot_sequence_motif_positions(
  motif_occurrences,
  position = "centre",
  scale = "relative",
  top_n = 5
)

Documentation

The package website now provides 15 synthetic, reproducible articles:

• getting started with the complete structural workflow;
• sequence-data validation and policy-driven preparation;
• choosing among motifs, consensus, distances, clustering, networks, higher-order models, HMMs, and adapters;
• a reproducible integrated synthetic case study;
• contiguous motif analysis and motif positions;
• consensus sequences and descriptive group comparisons;
• sequence distances, clustering, representatives, ensembles, and stability;
• transition networks and higher-order models;
• latent sequence models and optional ecosystem adapters;
• longitudinal and panel sequence workflows;
• non-contiguous subsequence mining;
• time-varying condition models;
• multichannel and covariate hidden Markov models;
• design-aware sequence inference and randomization;
• extended sequence visualisations.

The articles use ordinary data frames and synthetic data. They preserve the package interpretation boundary and do not infer psychological or causal attributes from structural sequence outputs.

Current release extensions

The 0.3.0 release includes the following explicitly governed workflows:

• longitudinal and panel sequence preparation, summaries, change metrics, and plots;
• bounded non-contiguous subsequence mining with explicit gap, span, and combination limits;
• optional time-varying state and transition models using mgcv;
• multichannel and covariate-dependent categorical hidden Markov models;
• design-aware permutation and bootstrap group comparisons;
• sequence-index, state-distribution, entropy, distance, network, and silhouette plots.

These extensions preserve ordinary-data-frame inputs, explicit method parameters, deterministic seeds, and structural interpretation boundaries. Inferential helpers record the independent unit and study design; observational contrasts remain associational, and randomized contrasts require valid assignment and implementation.

Interpretation boundary

Sequence outputs describe behavioural or structural patterns only. They do not independently establish emotion, cognition, comprehension, personality, intention, diagnosis, deception, or other psychological attributes. Substantive interpretation requires an appropriate study design and external evidence.

Author

Stefanos Balaskas

ORCID · Personal website · LinkedIn

Licence

gp3sequences is released under the MIT License.

Advanced sequence-analysis methods

The 0.2.0 release established:

• aligned-position consensus sequences and agreement diagnostics;
• descriptive between-group comparisons;
• edit, LCS, optimal-matching, and transition-profile distances;
• clustering, validation, representatives, ensembles, and stability analysis;
• transition networks, centrality, communities, and higher-order models;
• categorical HMMs, mixture HMMs, and state decoding;
• optional adapters to specialist sequence-analysis packages.

The 0.3.0 release extends that foundation with longitudinal and panel workflows, bounded non-contiguous subsequences, time-varying models, multichannel and covariate HMMs, design-aware inference, extended visualisations, and analysis-contract/provenance auditing.

These outputs are structural and statistical. They do not independently establish emotion, cognition, comprehension, personality, intention, diagnosis, deception, causality, or other psychological attributes.

gp3sequences documentation built on Aug. 23, 2026, 5:10 p.m.