Aims
The Journal of Reproducible Statistics publishes statistics and applied mathematics whose results are reproducible by construction. We pair strict methodological and empirical standards with fast, transparent review — the process is built for a first editorial decision within hours — and every contribution ships re-runnable code and data (or a privacy-preserving surrogate).
Scope — what we publish
- Methodological work — new statistical methods, theory, estimation and inference procedures.
- Applications — analyses of real data with substantive insight (not benchmark- or toy-only examples).
- Computational statistics and software — algorithms, implementations, reproducible tooling.
- Replications and null or negative findings — explicitly welcomed and reviewed on equal terms, not as a residual category.
- Simulation studies — with a transparent design and re-runnable code.
A precondition for every submission
Reproducibility is an acceptance criterion, not a bonus. Every contribution carries a reproducibility package: an executable source (Quarto, R Markdown or Jupyter preferred; LaTeX accepted), code, data or a privacy-preserving verifiable surrogate, and a pinned runtime environment. We assess reproducibility by re-execution, not by assertion alone. See the data & code availability policy for what this means in practice, including the route for data that cannot be public.
Out of scope
Work that cannot be made reproducible or verifiable; topics outside statistics and applied mathematics (for now); contributions without their own methodological or empirical substance.
Review criterion — soundness, not significance
We assess a submission for scientific soundness and reproducibility, not for novelty or perceived "interestingness". A rigorous, correctly reported, reproducible study on a less-fashionable question is in scope and publishable. This is not "accept everything": the selectivity bar is rigour plus reproducibility, which is real — we simply do not gatekeep on how exciting a topic is. Replications and null findings are the clearest beneficiaries of this rule, and we welcome them deliberately.
How submissions are assessed — including the disclosed use of AI and the named human editor who signs every decision — is described in the peer-review policy.