gp3sequences: An Auditable and Reproducible Framework for Ordered Categorical Sequence Analysis in R

Ordered categorical sequence analysis is distributed across specialist methods and software, making preparation decisions, method settings, diagnostics, and provenance difficult to inspect consistently across an end-to-end workflow. This paper presents gp3sequences, an R framework that adds a domain-specific contract layer to reproducible computing: explicit long-format mappings, declared preparation policies and decision logs, structured analysis contracts, capability-aware specialist hand-offs, and machine-readable provenance. The framework does not replace specialist implementations. Evaluation combines a controlled four-state Markov benchmark, independent numerical-reference checks, adversarial failure cases, multi-seed and design-size sensitivity analyses, a bounded scaling experiment, and a public six-state TraMineR 2.2-14 data example for interoperability. The primary benchmark contains 72 sequences of length 24 and deliberately overlapping transition structure. Review-level validation diagnostics are shown to arise from preserved consecutive repeated states rather than data errors. The prespecified Levenshtein two-cluster solution is weakly separated, and sensitivity analyses confirm that clustering conclusions depend on the selected sequence representation; the result is therefore treated as a diagnostic demonstration rather than evidence of latent classes. Across the evaluated operations, gp3sequences preserves declared analytical state while exposing invalid mappings, unavailable dependencies, and other structural problems rather than silently resolving them. The contribution is accordingly an integration and governance architecture for inspectable ordered-sequence workflows, not a claim of methodological or computational superiority over specialist packages.