| Gtheory4LLM-package | Generalizability Theory for LLM Subjective Tasks |
| coef.gt_fit | Standard Extractors for Fitted Generalizability Models |
| drug_review | LLM Drug-Review Sentiment and Four Aspect Annotations |
| drug_review_4aspect | LLM Drug-Review Sentiment and Four Aspect Annotations |
| Gtheory4LLM | Generalizability Theory for LLM Subjective Tasks |
| Gtheory4LLM-datasets | Three LLM Annotation Datasets: Design, Coding, and Sources |
| 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 |
| gt_components | Extract Source Covariances or Correlations |
| gt_component_vcov | Standard Extractors for Fitted Generalizability Models |
| 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 |
| hate_speech | LLM Hate-Speech Annotations of HateXplain Texts |
| logLik.gt_fit | Standard Extractors for Fitted Generalizability Models |
| mental_health_3group | LLM Mental-Health Text Annotations in Five Outcome Sets |
| mental_health_3L | LLM Mental-Health Text Annotations in Five Outcome Sets |
| mental_health_6flag | LLM Mental-Health Text Annotations in Five Outcome Sets |
| mental_health_7L | LLM Mental-Health Text Annotations in Five Outcome Sets |
| mental_health_nominal | LLM Mental-Health Text Annotations in Five Outcome Sets |
| nobs.gt_fit | Standard Extractors for Fitted Generalizability Models |
| plot.gt_dstudy | Evaluate Balanced Measurement Allocations |
| print.gt_diagnostics | Inspect Numerical Acceptance and Backend Diagnostics |
| print.gt_diagnostic_stage | Inspect Numerical Acceptance and Backend Diagnostics |
| print.gt_diagnostic_stages | Inspect Numerical Acceptance and Backend Diagnostics |
| print.gt_dstudy | Evaluate Balanced Measurement Allocations |
| print.gt_fit | Fit Univariate or Joint Multivariate Generalizability Models |
| print.gt_preflight | Inspect Data, Source Dimensions, and Backend Limits Before Fitting |
| print.gt_reliability | Compute Balanced G and Phi Coefficients |
| print.summary.gt_fit | Print a Concise Generalizability Model Summary |
| summary.gt_fit | Fit Univariate or Joint Multivariate Generalizability Models |
| vcov.gt_fit | Standard Extractors for Fitted Generalizability Models |