Generalizability Theory for LLM Subjective Tasks


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Documentation for package ‘Gtheory4LLM’ version 0.2.0

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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