Gtheory4LLM-package     Generalizability Theory for LLM Subjective
                        Tasks
gt_components           Extract Source Covariances or Correlations
gt_control              Configure Gaussian and Discrete Fitting and
                        Acceptance Checks
gt_design               Declare a Generalizability-Theory Random-Source
                        Design
gt_diagnostics          Inspect Numerical Acceptance and Backend
                        Diagnostics
gt_dstudy               Evaluate Balanced Measurement Allocations
gt_example              Load Modeling Tables from Three LLM-Annotated
                        Datasets
gt_family               Specify a Gaussian, Binary, Ordinal, or
                        Unordered Categorical Outcome
gt_fit                  Fit Univariate or Joint Multivariate
                        Generalizability Models
gt_fit_methods          Standard Extractors for Fitted Generalizability
                        Models
gt_preflight            Inspect Data, Source Dimensions, and Backend
                        Limits Before Fitting
gt_reliability          Compute Balanced G and Phi Coefficients
gt_score                Declare Composite-Score Weights
gtheory_datasets        Three LLM Annotation Datasets: Design, Coding,
                        and Sources
gtheory_drug_review     LLM Drug-Review Sentiment and Four Aspect
                        Annotations
gtheory_hate_speech     LLM Hate-Speech Annotations of HateXplain Texts
gtheory_mental_health   LLM Mental-Health Text Annotations in Five
                        Outcome Sets
print.summary.gt_fit    Print a Concise Generalizability Model Summary
