2026 – present
stats-claw
In-process statistical computing for Rust — distributions, hypothesis tests, and resampling — with zero runtime dependencies, validated against scipy.
Data science on the hot path
0
Runtime dependencies
14
Distribution families
1e-12
Tolerance vs. scipy
Detail
- The Rust ecosystem has strong data pipelines and inference, but no classical hypothesis-test suite with scipy-grade p-values. That gap is the crate's reason to exist.
- std-only: no BLAS, no LAPACK, no transitive supply chain. It compiles in seconds and drops into any Rust binary, including constrained targets.
- Fourteen distribution families with pdf/pmf, cdf, quantile, moments, log-space tails, and seeded sampling. Exact and asymptotic p-values across t, ANOVA, χ², Fisher, Mann–Whitney, Wilcoxon, Kruskal–Wallis, KS, and Shapiro.
- Every numeric is checked against committed golden fixtures generated from the reference Python libraries, to tolerances documented per area.
Stack
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