crossverify

Check a statistical analysis the way a careful reviewer would: confirm the numbers are internally consistent, reproduce identically on a second run, and agree with an independent implementation in another tool. crossverify runs your analysis through a documented six-phase protocol and writes the evidence — a verification log, a Python-vs-R comparison table, and a methodology statement you can adapt for a manuscript.

It establishes that a result is implementation-independent, not that it is correct: agreement across Python and R is strong evidence a number is not an artifact of one library's defaults, but you write both sides, so a shared specification error agrees perfectly. See The Protocol for the scope and limits.

Where to go next

• The Protocol — what each of the six phases does, in brief and in technical detail. The conceptual centerpiece; start here.
• API Reference — the public modules, generated from the source docstrings.

Quickstart

crossverify uses uv and runs entirely on your machine (no network, no telemetry):

uv sync
uv run crossverify --project examples/project.yaml

crossverify 0.1.1 — OLS regression: mpg ~ wt + hp (mtcars)
  Phase 5 triangulation    11 pass
  Cross-tool: 11/11 statistics matched within tolerance.

Result: PASS (30 passed, 0 failed, 4 informational)

The cross-tool phase additionally needs R with the jsonlite package; use --skip-r to run the Python-only phases. Full install and usage details are in the README.