#' gp3sequences: Transparent Analysis of Ordered Categorical Sequences
#'
#' `gp3sequences` provides transparent, reproducible, and auditable tools
#' for working with ordered categorical sequences represented in ordinary
#' long-format data frames.
#'
#' 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.
#'
#' @section 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.
#'
#' @keywords internal
"_PACKAGE"
