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, with a bounded observed-row route for incomplete univariate panels. Discrete models use dense or sparse 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). 'OpenMx' Gaussian fits report asymptotic Wald standard errors for variance components and delta-method coefficient intervals when curvature checks permit them; the 'lme4' route returns point estimates only. Discrete fits offer optional full-information latent coefficient intervals, subject to numerical and boundary checks. An object bootstrap refits resampled objects under each fit's acceptance rule and gives percentile and bias-corrected and accelerated intervals for components, coefficients and decision studies. Provides fixed-layout call effects, categorical score and category-vector reliability projections, repeated-panel decision agreement, bounded cost-aware comparisons, and Gaussian or latent binary/ordinal pilot precision planning, and categorical point-estimator pilot operating characteristics. Joint Gaussian-discrete fitting is not implemented. Includes three publicly archived LLM annotation datasets covering hate-speech, mental-health, and drug-review tasks. Discrete fitting retains conservative size limits; the preflight report describes supported designs and computational limits. Generalizability coefficients follow the variance-decomposition framework of Brennan (2001) <doi:10.1007/978-1-4757-3456-0>.
| Version: | 1.0.0 |
| Depends: | R (≥ 4.5.0) |
| Imports: | Matrix (≥ 1.6.0), OpenMx (≥ 2.22.11), graphics, methods, parallel, stats, tools, utils |
| Suggests: | lme4, ordinal, knitr, rmarkdown |
| Published: | 2026-11-01 |
| DOI: | 10.32614/CRAN.package.Gtheory4LLM |
| Author: | Jin Liu [aut, cre, cph] |
| Maintainer: | Jin Liu <Veronica.Liu0206 at gmail.com> |
| BugReports: | https://github.com/Veronica0206/Gtheory4LLM/issues |
| License: | GPL-3 |
| URL: | https://github.com/Veronica0206/Gtheory4LLM |
| NeedsCompilation: | yes |
| Citation: | Gtheory4LLM citation info |
| Materials: | README, NEWS |
| CRAN checks: | Gtheory4LLM results |
| Reference manual: | Gtheory4LLM.html , Gtheory4LLM.pdf |
| Vignettes: |
How many evaluators, prompts, and runs? (source, R code) Plan annotation effort, costs, and pilot precision (source, R code) |
| Package source: | Gtheory4LLM_1.0.0.tar.gz |
| Windows binaries: | r-devel: Gtheory4LLM_0.5.0.zip, r-release: Gtheory4LLM_0.5.0.zip, r-oldrel: Gtheory4LLM_0.5.0.zip |
| macOS binaries: | r-release (arm64): Gtheory4LLM_0.5.0.tgz, r-oldrel (arm64): Gtheory4LLM_0.5.0.tgz, r-release (x86_64): Gtheory4LLM_0.5.0.tgz, r-oldrel (x86_64): Gtheory4LLM_0.5.0.tgz |
| Old sources: | Gtheory4LLM archive |
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