# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "Gtheory4LLM" in publications use:' type: software license: GPL-3.0-only title: 'Gtheory4LLM: Generalizability Theory for LLM Subjective Tasks' version: 0.5.0.9000 identifiers: - type: doi value: 10.32614/CRAN.package.Gtheory4LLM abstract: Studies the reliability and generalizability of subjective judgments produced by large language models (LLMs), including annotation, rating, and structured LLM-as-a-judge tasks. Specifies evaluator, prompt, generation, and repeated-run facets through crossed or explicitly nested random sources with configurable item interactions. Fits univariate models, joint Gaussian models, and joint discrete models for binary, ordinal, and unordered categorical outcomes, with source-specific covariance. Gaussian models use exact balanced likelihood; discrete models use a dense first-order Laplace approximation with Gaussian latent random effects. Supported balanced decision studies compare evaluator, prompt, and replication allocations using observed Gaussian or explicitly requested latent binary and ordinal reliability, with random or fixed facets after Brennan (2001). Gaussian fits report asymptotic Wald standard errors for their variance components and delta-method intervals for the coefficients; discrete fits report point estimates only. Provides fixed-layout Gaussian batch projections, bounded cost-aware allocation comparisons, and Gaussian simulation-based pilot precision planning. Scalar nominal reliability and joint Gaussian-discrete fitting are not implemented. Includes three publicly archived LLM annotation datasets covering hate-speech, mental-health, and drug-review tasks. Discrete fitting is limited to small models; the preflight report describes supported designs and computational limits. Generalizability coefficients follow the variance-decomposition framework of Brennan (2001) . authors: - family-names: Liu given-names: Jin email: Veronica.Liu0206@gmail.com preferred-citation: type: manual title: 'Gtheory4LLM: Generalizability Theory for LLM Subjective Tasks' authors: - family-names: Liu given-names: Jin email: Veronica.Liu0206@gmail.com year: '2026' notes: R package version 0.5.0.9000 url: https://github.com/Veronica0206/Gtheory4LLM repository: https://veronica0206.r-universe.dev repository-code: https://github.com/Veronica0206/Gtheory4LLM commit: f4acb425191bf912dc139d859f46bb0cc4e03c51 url: https://github.com/Veronica0206/Gtheory4LLM date-released: '2026-10-18' contact: - family-names: Liu given-names: Jin email: Veronica.Liu0206@gmail.com