The reproducibility badge reflects the status of the gallery repos. See those repos for a live demonstration of
repro_badge()in a real analysis workflow.
Know your R analysis will produce the same results tomorrow as it does today.
You finish an analysis. The code runs. The numbers look right. But are they stable?
Package updates change function behaviour silently. Stochastic code without a fixed seed produces different results on every run. A model fitted on one platform may return subtly different coefficients on another. Results that were correct in January may drift by March — with no error, no warning, and no obvious cause.
reproducr makes these risks visible and trackable, before they reach a journal, a regulator, or a collaborator.
Scans your scripts for known breaking changes across popular CRAN packages, flags stochastic calls missing set.seed(), and identifies locale-sensitive operations that behave differently across systems.
Certifies your outputs by hashing key results — model coefficients, summary statistics, p-values — so any numerical drift is detected automatically on subsequent runs.
Generates audit reports in three styles: a plain summary, a ready-to-paste academic methods paragraph, and a structured QC document with sign-off fields for regulated workflows.
Works with your existing setup. If you use renv, reproducr reads your lockfile automatically. If you don't, it uses your installed library. No configuration required.
# Development version from GitHub
install.packages("remotes")
remotes::install_github("repro-stats/reproducr")Real-world pipelines demonstrating the full reproducr workflow across
different domains, environments, and regulatory contexts. Each repository
is a complete, independently runnable analysis with CI that audits on every
push, certifies outputs, detects drift, and updates the badge automatically.
| Example | Domain | Audience | renv | Report style | Badge | Walkthrough |
|---|---|---|---|---|---|---|
| reproducr-ecology | Ecology / penguins | General R users | No | minimal |  |
DEMO.md |
| reproducr-clinical | Clinical trials / oncology | Biostatisticians, pharma | Yes | pharma |  |
DEMO.md |
| reproducr-rwe | Real world evidence | Epidemiologists, HEOR | Yes | academic |  |
DEMO.md |
| reproducr-cmc | CMC statistics | CMC statisticians, regulatory affairs | Yes | pharma |  |
DEMO.md |
Together they demonstrate that reproducr is environment-agnostic — it works
with or without renv, across domains, and at any level of regulatory rigour.
Each DEMO.md walks through the complete pipeline with real output at every step.
library(reproducr)
# Step 1: Audit your script
report <- audit_script("analysis.R")
print(report)
#>
#> -- reproducr audit report [2026-05-30 14:32] --
#>
#> Files scanned: 1
#> Packages found: 4
#> Calls detected: 23
#> R version: 4.4.2
#> Platform: aarch64-apple-darwin20
#> Versions from: installed library
#>
#> Next step: risks <- risk_score(report)
# Step 2: Score for risk
risks <- risk_score(report)
print(risks)
#>
#> -- reproducr risk score --
#>
#> HIGH: 1
#> MEDIUM: 2
#> LOW: 1
#>
#> [HIGH] dplyr::summarise (line 14 in analysis.R)
#> Check : changelog
#> Details : In dplyr 1.1.0, summarise() changed its default
#> grouping behaviour ...
#> Reference: https://dplyr.tidyverse.org/news/index.html#dplyr-110
# Step 3: Certify your outputs as a baseline
model <- lm(mpg ~ wt, data = mtcars)
certify(
outputs = list(
coefs = coef(model),
r_squared = summary(model)$r.squared,
n_obs = nrow(mtcars)
),
tag = "submission-v1",
script = "analysis.R"
)
#> reproducr: certified 3 output(s) [2026-05-30] under tag 'submission-v1'
# Step 4: After any environment change or package upgrade, check for drift
check_drift(
outputs = list(
coefs = coef(model),
r_squared = summary(model)$r.squared,
n_obs = nrow(mtcars)
),
against = "submission-v1"
)
#>
#> -- reproducr drift check vs 'submission-v1' --
#>
#> Verdict : ALL OUTPUTS MATCH
#> OK : 3
#> Drifted : 0
# Step 5: Generate a report
repro_report(report, risks, format = "html", style = "pharma",
output_file = "qc_report.html")
# Step 6: Badge your README
repro_badge(report, risks, output = "README")| Function | Tier | Purpose |
|---|---|---|
audit_script() |
1 | Parse a script and extract all pkg::fn calls with version info |
risk_score() |
1 | Check calls against the breaking-changes database |
certify() |
2 | Hash and store analytical outputs as a signed baseline |
check_drift() |
2 | Compare current outputs against a stored baseline |
list_certs() |
2 | Inspect all certifications in a project |
repro_report() |
3 | Render audit report (text / Markdown / HTML) |
repro_badge() |
3 | Generate a shields.io reproducibility badge |
check_db_staleness() |
— | Check database entries against current CRAN versions |
Tier 1 — Scan & score Tier 2 — Baseline & drift Tier 3 — Report & export
───────────────────── ───────────────────────── ─────────────────────────
audit_script() certify() repro_report()
│ │ │
▼ ▼ ▼
risk_score() check_drift() repro_badge()
Use Tier 1 alone for a quick scan, or build the full pipeline for regulated or peer-reviewed work.
The heart of risk_score() is a curated database of known cases where a package update silently changed function behaviour — not errors, not deprecation warnings, just different results. Coverage spans popular packages across the tidyverse, modelling (lme4, survival, MatchIt, caret), and base R.
The database is kept current via a weekly automated check -- see check_db_staleness().
Community contributions are welcome via reproducr-db, which lists every tracked entry.
Checks every detected pkg::fn call against the built-in database. A call is flagged only if the installed version falls within a known risky version window (from_ver, to_ver].
Risk levels:
- HIGH — output values can change silently with no error
- MEDIUM — argument renamed or deprecated; may error or produce different output
- LOW — minor behavioural note; output unlikely to differ in practice
Flags any call to a stochastic function (rnorm, sample, rbinom, etc.) where no set.seed() is found within 50 lines above the call.
# This will be flagged:
x <- stats::rnorm(100)
# This will not:
set.seed(42)
x <- stats::rnorm(100)Flags functions whose output depends on the system locale (sort(), format(), strftime(), etc.) — relevant when code runs on servers in different countries or with different OS locale settings.
# After your analysis completes, certify the key outputs:
certify(
outputs = list(
model_coefs = coef(my_model),
final_n = nrow(results),
primary_pval = tidy(my_model)$p.value[2]
),
tag = "pre-review",
script = "main_analysis.R"
)
# Three months later, after any environment change:
check_drift(
outputs = list(
model_coefs = coef(my_model),
final_n = nrow(results),
primary_pval = tidy(my_model)$p.value[2]
),
against = "pre-review"
)Certifications accumulate in a .reproducr.rds file in your project root.
Commit this file to version control — it is your audit trail.
A compact Markdown/HTML summary covering environment, verdict, and risk table.
A ready-to-paste methods paragraph for journal submissions:
All analyses were conducted in R (version 4.4.2) on macOS 26.5. The following packages were used: dplyr (v1.1.4), ggplot2 (v3.5.1) ... Reproducibility auditing (reproducr) identified no risks. The full audit report and certification records are available in the supplementary materials.
A structured QC document with execution environment table, full package inventory, risk register, drift assessment, and sign-off fields for analyst and reviewer.
repro_report(report, risks, drift,
format = "html", style = "pharma",
output_file = "qc_report.html")reproducr is designed to run automatically on every push. A typical workflow:
- Audits your scripts for risk
- Checks for drift against the last certified run
- Updates the reproducibility badge in your README
See the reports and badges vignette for the complete GitHub Actions workflow.
The database powering risk_score() is maintained in a dedicated community
repository — reproducr-db.
Contributing a new entry requires:
- A
pkg::fnkey - A version window (
from_version,to_version) - A risk level (
"high","medium", or"low") - A plain-English description of the breaking change
- A URL reference (package
NEWS.md, CRAN page, GitHub release)
Each entry is a small JSON file — see the reproducr-db README for the format and the contributing guide for the version window design principles.
Please note that this project is released with a Contributor Code of Conduct. By participating you agree to abide by its terms.
To contribute to reproducr itself — new features, bug fixes, or
additional risk checks — open an issue or pull request on the
main repository.