Not because it's fashionable — because R is the language statistics was built in. When you ask us a question, an agent writes real R, executes it against your data in a sandboxed container, and an independent verifier recomputes the answer before it reaches you. Then we hand you the source.
Most statistical methods are published, peer-reviewed, and first implemented in R. Its libraries aren't ports or approximations — they're frequently written by the statisticians who developed the method, and scrutinized by the people who review it.
That matters when the answer has to hold up. A survival model, a mixed-effects fit, a Holm correction across a family of tests — in R these are one well-documented call with decades of literature behind them, and defaults chosen by people who argued about them in journals.
t.test, aov, chisq.test — with the assumption checks and corrections built in, not bolted on.lm, glm, survival — diagnostics, confidence intervals, and residual analysis are first-class, not afterthoughts.p.adjust — Holm, BH, and friends, because testing twenty things and reporting the best one isn't analysis.prcomp, kmeans, factanal — the reference implementations, with the conventions statisticians expect.Every report ships with the exact R that produced it — not pseudocode, not a summary of the approach. The analysis code appendix renders the source in full, with the reasoning written into the comments.
#' COMPUTE — mean cost per usage row by model #' Cost per row is the honest comparison here: totals would just #' reflect how many rows each model happens to have logged. df <- df[!is.na(df$cost_usd), , drop = FALSE] # drop=FALSE: 1-col frames stay frames model_avg <- tapply(df$cost_usd, df$model, mean, na.rm = TRUE)
From a real delivered report. Prose comments become documentation; the code stays executable.
Because it's ordinary R against ordinary data, your analyst can read it, disagree with it, adapt it, or run it themselves. That's a different relationship than "trust the black box."
An AI writing fresh code on every request gives you a different program each time — and a different answer. We do the opposite: the R is written once, checked, and becomes a durable analysis you own.
A separate verifying agent recomputes the headline numbers straight from your raw data — with its own code, never the build's — and sends the work back if anything disagrees.
Only after that independent recomputation agrees does the report reach you. Then it's fixed: same data in, same numbers out, this month and next year. More on reproducibility →
Your R executes in an isolated container with fixed random seeds and no network access. Nothing about your data leaves that box, and every stochastic step — a bootstrap, a clustering init, a train/test split — lands identically on re-run.
This is the part people underestimate: reproducibility isn't just about keeping the code. It's about pinning the environment and the randomness too.
Run an analysis and open the code appendix — the whole script, yours to keep.
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