stats-standards

name

stats-standards

description

Help authors and reviewers apply rOpenSci's Statistical Software peer review standards (stats-devguide.ropensci.org) to a statistical R package. Use this whenever a package implements a statistical method, model, or algorithm (Bayesian, regression, machine learning, EDA/summary stats, time series, spatial, dimensionality reduction/clustering, probability distributions) and someone wants to comply with or review against the rOpenSci statistical standards, document standards with the srr package (@srrstats tags), run srr or autotest, or understand the graded badges. This is an EXTENSION of the general rOpenSci process — always apply package-standards first; this skill adds the statistical standards on top.

rOpenSci Statistical Software Standards

The statistical software review process is a direct extension of the general rOpenSci process, not a replacement. Everything in package-standards (naming, DESCRIPTION, docs, tests, CI, security) and peer-review-author / package-review still applies. Start there. This skill adds the statistics-specific layer: explicit, machine-checkable standards documented in code with the srr package.

Using the reference efficiently. The categories table and workflow below answer the common "how does this work / what do I do" question on their own. Open references/standards-categories.md only when you need the exact wording of a specific standard or the full list for a category you're actually documenting — not by default.

Primary sources: https://stats-devguide.ropensci.org/, standards: https://stats-devguide.ropensci.org/standards.html, srr: https://docs.ropensci.org/srr/.

What "standards" means here

Unlike the general guide's "guidelines", statistical review centers on explicit standards that can be tracked automatically:

• General Standards (G-prefixed, e.g. G1.0, G2.0a, G5.4) apply to every statistical package — documentation, input validation, algorithm robustness, output consistency, testing.
• Category-specific standards apply per analytical domain. Every package must comply with the General Standards plus at least one category.

Codes are hierarchical alphanumerics with decimal sub-standards (e.g. G2.0a, RE1.1). The full text of all standards is in references/standards-categories.md.

Categories

Code	Category
BS	Bayesian & Monte Carlo
EA	Exploratory Data Analysis & Summary Statistics
ML	Machine Learning
RE	Regression & Supervised Learning
SP	Spatial
TS	Time Series
UL/US	Dimensionality Reduction, Clustering & Unsupervised Learning
PD	Probability Distributions

Pick every category your package touches; you must satisfy each category's standards (or mark them not-applicable with a reason).

Workflow for authors

1. Make the package generally compliant first with package-standards.

2. Identify your categories from the table above.

3. Scaffold standards documentation with srr:

   srr::srr_stats_roxygen(category = c("regression"))   # your categories
   

   This writes R/srr-stats-standards.R pre-filled with every applicable standard as an @srrstatsTODO tag.

4. Annotate the code. For each standard, near the code that satisfies it, change the tag from @srrstatsTODO to either:

   • @srrstats "G2.0a" describe how this is met — the standard is satisfied; say where/how.
   • @srrstatsNA "RE1.2" reason it does not apply — the standard genuinely doesn't apply; give the reason.

   The srr roclet runs on devtools::document() / roxygen2::roxygenise() and reports the state of standards on screen. Leaving @srrstatsTODO tags means the documentation is incomplete.

5. Generate the compliance report and pre-submission check:

   srr::srr_report()             # HTML report of standards coverage
   srr::srr_stats_pre_submit()   # completeness gate before submitting
   

6. Run autotest to catch undocumented/incorrect edge-case behavior — it mutates inputs to surface mishandled cases:

   autotest::autotest_package()
   

7. Submit via the normal ropensci/software-review process (see peer-review-author). The editor verifies standards with @ropensci-review-bot check srr, and pkgcheck includes a "srr documentation complete" check.

Workflow for reviewers

• Do everything in package-review, plus assess alignment against each applicable standard rather than just the general checklist.
• Read the srr_report() output: is each standard either satisfied (@srrstats) or justifiably not-applicable (@srrstatsNA)? Are the claims accurate?
• Check that the chosen categories actually cover what the package does.
• Confirm autotest issues are addressed or explained.

Graded badges

Statistical packages receive graded badges publicizing how thoroughly they meet standards (broader standards coverage and autotest / full-test compliance earn higher grades). See https://stats-devguide.ropensci.org/ for the current tier definitions.

Tooling summary

• srr — standards roclets: srr_stats_roxygen(), @srrstats / @srrstatsNA tags, srr_report(), srr_stats_pre_submit().
• autotest — automated mutation/edge-case testing for statistical packages.
• pkgcheck / @ropensci-review-bot check srr — verify standards docs are complete.

Related skills

• Always apply first: package-standards (general standards).
• Submission process: peer-review-author.
• Reviewing: package-review.