Changes in version 0.1.0.9000                      

  - New accept_model(): model+harness acceptance gate. A candidate LLM
    runs the spec-to-IR regression corpus and is cross-compared against
    the rules baseline at full canonical semantics; both thresholds are
    required arguments (acceptance criteria must be prespecified), any
    pipeline error rejects the candidate, and the decision is logged as
    a digest-only model_acceptance audit event.
  - New routing_plan(): derives the execution routing directly from spec
    lineage — topological waves, per-variable dependencies, and the two
    fail-closed lanes (cycle-blocked and human-routed variables stay
    waveless). The ordering is computed, not declared, replacing a
    hand-registered process DAG.
  - New classify_variables_dual(): runs the rules classifier and the LLM
    pipeline independently on the same spec, compares the two
    translations per variable at full canonical semantics
    (ir_fingerprint(level = "full")), adopts the rules IR on agreement,
    and escalates any divergence (disagree / rules_abstain /
    llm_abstain) to needs_human — no side is ever silently preferred.
    The decision is recorded as a digest-only dual_consensus audit
    event; dual_disagreements() extracts the human review queue.

                        Changes in version 0.1.0                        

Initial release.

  - Translate P21-style ADaM specifications into layer IR over a closed
    vocabulary, then deterministically compile the IR into
    admiral/metatools programs with # CHECK: validation comments and
    provenance sidecars (classify_variables(), render_program(),
    write_artifact()).
  - Zero-dependency rules classifier (backend = "rules") as the default
    baseline; optional LLM translation (backend = "llm", via 'ellmer')
    emits IR only — the LLM never writes R code and every model response
    is validated against the closed vocabulary before use.
  - Sandbox gate for executing generated programs, hash-chained audit
    log, dataset-JSON reader, define.xml (2.0) metadata reader, and
    dependency/impact reporting across ADaM datasets.
  - Study-level drivers: execute_one(), execute_study() with derivation
    ordering, gates, and manifest generation.