Replication Package

Replication Package

Produce the deposit an economist hands a journal at acceptance: a directory tree (data/, code/, output/, README) plus a DCAS compliance checklist, built to the AEA Data and Code Availability Standard, openICPSR deposit expectations, and the Social Science Reproduction Platform reproduction protocol. This skill moves the repo from auditing reproducibility to producing the deposit — /audit-reproducibility proves the numbers; this skill packages everything a third party needs to regenerate them from scratch.

Core principle: the package is reproducible by a stranger with the data and the README — no tacit knowledge, no "ask the author" steps. Every table and figure maps to the exact script and line that produces it.

When to use

• At acceptance. The journal's data editor (AEA, REStud, JPE, EJ, ...) requests a DCAS-compliant deposit before the paper is typeset.
• Before an openICPSR / Zenodo / Dataverse upload. Build the tree and README once, locally, before the web upload.
• Pre-submission dry run. Catch the "I never wrote down where Table 3 comes from" gap while it is cheap to fix.
• Confidential-data papers. Produce the access-restricted-data note and a runnable-on-restricted-data package even when the data itself cannot be deposited.

Inputs

• $0 — path to the manuscript (.tex, .qmd, .md, .pdf). Required (the source of the Table/Figure inventory).
• $1 — outputs directory. Defaults to output/, where every language's pipeline writes. Recognised alternative: _targets/objects/. If output/ does not exist but a pre-v2.6 scripts/<lang>/_outputs/ does, use that and say so.

Workflow

Phase 0: Pre-flight — detect language(s) and outputs

1. Detect the analysis language(s) by scanning for scripts/R/*.R (+ renv.lock / DESCRIPTION), scripts/stata/*.do, scripts/python/*.py (+ requirements.txt / environment.yml / pyproject.toml). A project may be polyglot — record all detected languages.
2. Locate the outputs directory ($1) and the one-command entry point (00_run_all.R, 99_run_all.do, run.py, Makefile). If none exists, flag it — DCAS requires a single master script.
3. If quality_reports/passports/<paper-slug>.yaml exists, load it; its claims: entries are the authoritative Table/Figure → source_file:source_line map for Phase 1.

Phase 1: Generate the standard replication README

Write replication_package/README.md (the AEA template, fields below). Leave a [FILL] marker on any field you cannot infer — never fabricate a data source or license.

• Overview / paper citation — title, authors, abstract one-liner.
• Data Availability Statement — for each dataset: public / restricted / proprietary, and whether it is redistributed in the package. This is the single most-rejected DCAS field; be explicit.
• Dataset manifest — a table, one row per file: filename | description | source (URL/citation) | access (public / DUA / purchase) | license | provided in package? (Y/N).
• Computational requirements — OS, software + versions (R / Stata / Python), key packages, approximate runtime, RAM, any HPC/cluster need.
• Step-by-step run instructions — the single master-script invocation, then the expected outputs.
• Table/Figure → script:line map — one row per exhibit: Exhibit | Program | Line | Output file. Read from the passport if present; otherwise grep the manuscript for \input{} / \includegraphics{} and trace each to the producing script. This map is what a reproducer follows; it is the heart of the package.

Phase 2: Capture the computational environment

Generate the dependency lockfile(s) and an environment snapshot for each detected language. Prefer /capture-environment if available; otherwise produce them directly:

• R — renv::snapshot() → renv.lock; sessionInfo() → output/sessionInfo.txt.
• Python — pip freeze → requirements.txt (or export the conda environment.yml); record python --version.
• Stata — creturn list / about / the which list → output/sessionInfo_stata.txt, the environment record the Stata convention requires; confirm every .do pins version NN (per stata-code-conventions.md).
• Container (recommended by DCAS for non-trivial setups) — scaffold a Dockerfile pinning the base image + language version.

Phase 3: Confirm claims reproduce before packaging

Run /audit-reproducibility $0 $1 (passport-aware if the YAML exists).

• Any FAIL (out of tolerance, no named alternative) → block: do not assemble a package around numbers that do not reproduce. Surface the failing claims and stop.
• EXPLAINED (out of tolerance with a recorded named alternative) → allowed; carry the note into the README's known-discrepancies section.
• All PASS / PASS + EXPLAINED → proceed to Phase 4.