[shmercer] pairwiseLLM 1.6.0

sterett.mercer@ubc.ca (Sterett H. Mercer) — Mon, 19 Oct 2026 19:15:35 GMT

Provides a unified framework for generating, submitting, and analyzing pairwise comparisons of writing quality using large language models (LLMs). The package supports live and/or batch evaluation workflows across multiple providers ('OpenAI', 'Anthropic', 'Google Gemini', 'Together AI', 'TypeSafe', and locally-hosted 'Ollama' models), includes bias-tested prompt templates and a flexible template registry, and offers tools for constructing forward and reversed comparison sets to analyze consistency and positional bias. The package additionally supports adaptive pairing workflows that iteratively select comparisons based on model uncertainty to improve ranking efficiency. Results can be modeled using frequentist or Bayesian Bradley–Terry–Luce models (Bradley & Terry, 1952 ; see also Caron & Doucet, 2012 ) or Elo rating methods (see Clark et al., 2018 ) to derive writing quality scores. The Bayesian writing quality scores can be converted to distribution-matched performance levels or calibrated to ordered rubric scores using linear or monotone ordinal models. For information on pairwise comparisons and comparative judgement, see Thurstone (1927) and Heldsinger & Humphry (2010) .

[shmercer] writeAlizer 1.7.4

sterett.mercer@ubc.ca (Sterett H. Mercer) — Mon, 19 Oct 2026 19:06:23 GMT

Imports variables from 'ReaderBench' (Dascalu et al., 2018), 'Coh-Metrix' (McNamara et al., 2014), and/or 'GAMET' (Crossley et al., 2019) output files; downloads predictive scoring models documented in Keller-Margulis et al. (2026), Mercer & Cannon (2022), and Mercer et al. (2021); and generates predicted writing quality and curriculum-based measurement (McMaster & Espin, 2007) scores.

shmercer.r-universe.dev

[shmercer] pairwiseLLM 1.6.0

[shmercer] writeAlizer 1.7.4