Most researchers who need statistics are not programmers, and most no-code tools solve that by hiding the machinery. That trade-off fails at peer review, where the machinery is exactly what gets questioned.
A different model
CATO is an AI data analysis and literature review tool for biomedical researchers, built by NYRUS AI. You work in plain language and a no-code figure editor; CATO writes and executes real Python and R behind the scenes, then exposes the exact code, the tests chosen, the assumptions checked, and the missing-data handling next to every result.
Why this matters
- Reviewers can ask for the code, and you have it.
- Collaborating statisticians can verify rather than redo.
- Methods sections write themselves from what actually ran.
What you can run
Descriptives, group comparisons, regression and mixed models, survival analysis, differential expression, enrichment, power and sample-size calculations, and anything else the 200+ bundled scientific libraries support.
You can try CATO at cato.nyrus.ai, or learn more at nyrus.ai/cato.