dependency-tracker

Auditing your economics research pipeline involves analyzing script I/O across R, Stata, Python, and LaTeX to build a dependency graph. This identifies stale outputs, missing dependencies, and data-flow gaps to ensure all results are current and reproducible.

Checking data-flow dependencies before manuscript submission requires mapping the end-to-end data flow from raw inputs to final outputs. This detects broken links and stale outputs, verifying that all figures, tables, and references are properly linked and up-to-date.

A dependency graph for research reproducibility maps relationships between raw inputs, scripts, and manuscript outputs. You need one when starting inherited projects, after code changes, or before submission to ensure all outputs are up-to-date.

Yes, this pipeline audit supports mixed R, Stata, Python, and LaTeX code. It analyzes script I/O across all four languages to map complete data flow and identify missing dependencies or stale outputs in your research project.

Detecting stale outputs after code changes involves running an audit that compares script execution timestamps against generated results. This identifies any outputs not regenerated after code modification, ensuring full reproducibility.

Generating a DAG report to identify missing dependencies requires analyzing script I/O to construct a directed acyclic graph of your data flow. This report highlights broken links and data-flow gaps while providing actionable remediation steps.