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type: software
license: MIT
title: 'evaluatellm: Statistical Inference for Language Model Evaluations'
version: 0.1.0
doi: 10.32614/CRAN.package.evaluatellm
abstract: Treats language model evaluations as statistical experiments and supplies
  the inference they require. Provides central limit theorem and cluster-robust standard
  errors for evaluation scores, paired and unpaired model comparisons, variance decomposition
  when several responses are drawn per question, control-variate variance reduction,
  multiplicity adjustment across benchmark suites, and power and minimum detectable
  effect calculations for planning evaluations, following Miller (2024) <https://doi.org/10.48550/arXiv.2411.00640>.
  For evaluations scored by a model judge, implements agreement statistics against
  a human gold standard and prediction-powered inference (Angelopoulos et al. 2023)
  <https://doi.org/10.1126/science.adi6000> with the power-tuned estimator of Angelopoulos,
  Bates and Jordan (2023) <https://doi.org/10.48550/arXiv.2311.01453>, so a small
  set of human labels debiases a large set of judge scores. Leaderboards are supported
  through bootstrap rank intervals and Bradley-Terry ratings (Bradley and Terry 1952)
  <https://doi.org/10.2307/2334029>. Accepts scores from any evaluation harness.
authors:
- family-names: Coverdale
  given-names: Charles
  email: charlesfcoverdale@gmail.com
repository: https://charlescoverdale.r-universe.dev
repository-code: https://github.com/charlescoverdale/evaluatellm
commit: 3ef61edaee981b31b84843678a96f6f6ddd27702
url: https://charlescoverdale.github.io/evaluatellm/
date-released: '2026-09-19'
contact:
- family-names: Coverdale
  given-names: Charles
  email: charlesfcoverdale@gmail.com