Measured performance · Reproducible evidence

Mixed-model performance,
case by case.

Explore complete fits, optimizer tradeoffs, and the cost of repeated work. Every result belongs to a recorded model, revision, and environment. Numerical agreement and uncertainty determine which speed claims qualify.

01 / Current evidence

At a glance

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Complete fit · Rust / Julia

Values below 1.0× indicate a lower measured median for lme-rs. Check fit agreement, uncertainty, and timing boundaries before interpreting a speed advantage.

Explore the workloads

Complete fits use matching data and model settings. Prepared fits reuse Rust's design; their comparison with a new Julia model is diagnostic.

Environment

Recorded with the fair-harness reference unless noted.

How to read this

These charts are versioned artifacts, not machine-independent constants.

  • The fair harness loads shared CSVs once, warms up each runtime, and times only the fit call.
  • New runs require converged LMM fits with matching objectives and fixed effects before making speed claims. GLMM equivalence is not yet established.
  • Rust/Julia intervals describe variation within each process. Optimizer intervals resample three execution blocks. Neither establishes performance on other machines; inconclusive and unverified results are not wins.
  • GitHub-hosted runner numbers are useful for tag-to-tag diffs. Re-run the harness on your hardware before citing speed.
  • Whole-script example bars include process startup and JIT; small fixtures favor a prebuilt Rust binary.