gp3sequences-package: gp3sequences: Transparent Analysis of Ordered Categorical...
In gp3sequences: Transparent Analysis of Ordered Categorical Sequences
gp3sequences-package R Documentation
gp3sequences: Transparent Analysis of Ordered Categorical Sequences
Description
gp3sequences provides transparent, reproducible, and auditable tools
for working with ordered categorical sequences represented in ordinary
long-format data frames.
Details
The package is designed around explicit column mappings, deterministic
ordering, documented preprocessing policies, machine-readable diagnostics,
reproducible analysis contracts, and conservative structural summaries.
Current workflows cover motifs and subsequences, consensus and group
comparisons, distances and clustering, transition networks and higher-order
models, categorical hidden Markov models, longitudinal and time-varying
analyses, design-aware inference, provenance auditing, visual diagnostics,
and guarded interoperability with specialist sequence-analysis packages.
It is not restricted to eye-tracking data or to any particular device or
export format.
Interpretation boundary
Sequence outputs describe behavioural or structural patterns. 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(s)
Maintainer: Stefanos Balaskas s.balaskas@ac.upatras.gr (ORCID)
Authors:
Stefanos Balaskas s.balaskas@ac.upatras.gr (ORCID)
See Also
Useful links:
gp3sequences documentation built on Aug. 23, 2026, 5:10 p.m.
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| gp3sequences-package | R Documentation |
gp3sequences: Transparent Analysis of Ordered Categorical Sequences
Description
gp3sequences provides transparent, reproducible, and auditable tools
for working with ordered categorical sequences represented in ordinary
long-format data frames.
Details
The package is designed around explicit column mappings, deterministic ordering, documented preprocessing policies, machine-readable diagnostics, reproducible analysis contracts, and conservative structural summaries. Current workflows cover motifs and subsequences, consensus and group comparisons, distances and clustering, transition networks and higher-order models, categorical hidden Markov models, longitudinal and time-varying analyses, design-aware inference, provenance auditing, visual diagnostics, and guarded interoperability with specialist sequence-analysis packages. It is not restricted to eye-tracking data or to any particular device or export format.
Interpretation boundary
Sequence outputs describe behavioural or structural patterns. 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(s)
Maintainer: Stefanos Balaskas s.balaskas@ac.upatras.gr (ORCID)
Authors:
Stefanos Balaskas s.balaskas@ac.upatras.gr (ORCID)
See Also
Useful links:
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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