Package index
Guided workflow and reporting
Run the staged workflow with explicit researcher decisions, extract report-ready tables, and archive evidence and decisions.
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nomo_run() - Run the guided nomologR workflow
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nomo_revise() - Revise a guided workflow and keep its lineage
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nomo_report() - Render a reproducible nomologR analysis report
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nomo_table() - Extract report-ready evidence tables
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nomo_apa_table() - Manuscript-ready tables in APA style
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nomo_defaults() - Default guidance settings for nomologR
Research basis
The methods the package implements, where each sits between historical and contemporary practice, and the literature behind it.
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nomo_methods() - The methods registry: what nomologR computes, and where it comes from
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nomo_screen() - Audit item-level data before factor modeling
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print(<nomo_screen>) - Print a nomo_screen object
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summary(<nomo_screen>) - Summarize a nomo_screen audit
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print(<summary_nomo_screen>) - Print a summary_nomo_screen object
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plot(<nomo_screen>) - Plot a nomo_screen audit
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nomo_factors() - Evaluate evidence about the number of latent factors
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print(<nomo_factors>) - Print factor-retention evidence
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summary(<nomo_factors>) - Summarize factor-retention evidence
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print(<summary_nomo_factors>) - Print a factor-retention summary
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plot(<nomo_factors>) - Plot factor-retention evidence
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nomo_efa() - Guided exploratory factor analysis
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summary(<nomo_efa>) - Summarize a guided exploratory factor analysis
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plot(<nomo_efa>) - Plot exploratory factor-analysis evidence
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nomo_model() - Build confirmatory factor-analysis syntax
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nomo_split() - Create a reproducible calibration/validation split
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nomo_cfa() - Guided confirmatory factor analysis
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summary(<nomo_cfa>) - Summarize a guided confirmatory factor analysis
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plot(<nomo_cfa>) - Plot confirmatory factor-analysis evidence
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nomo_compare() - Compare confirmatory measurement models
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summary(<nomo_compare>) - Summarize a measurement-model comparison
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plot(<nomo_compare>) - Plot a measurement-model comparison
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nomo_missing() - Missing-data sensitivity: does a result depend on how missing data were handled?
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nomo_reliability() - Model-based reliability evidence
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summary(<nomo_reliability>) - Summarize model-based reliability evidence
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plot(<nomo_reliability>) - Plot model-based reliability evidence
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nomo_hierarchical() - Evaluate general-factor strength in bifactor and higher-order models
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plot(<nomo_hierarchical>) - Plot hierarchical measurement evidence
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nomo_validity() - Convergent and discriminant construct-validity evidence
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summary(<nomo_validity>) - Summarize convergent and discriminant measurement evidence
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plot(<nomo_validity>) - Plot convergent or discriminant validity evidence
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nomo_invariance() - Evaluate measurement invariance across groups
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nomo_partial() - Specify researcher-controlled partial-invariance releases
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plot(<nomo_invariance>) - Plot measurement-invariance evidence
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nomo_scores() - Score a measurement model, with the evidence for the scoring choice
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nomo_hypotheses() - Specify a priori expectations for a nomological network
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positive()negative()negligible() - Specify directional or negligible theoretical expectations
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nomo_network() - Evaluate a theory-specified nomological network
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plot(<nomo_network>) - Plot nomological-network evidence
Teaching datasets
Simulated data with documented population models, so learners can compare package evidence with the known truth.
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nomo_demo_continuous - Simulated two-factor item data with known teaching features
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nomo_demo_ordinal - Simulated five-category ordered item data
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nomo_demo_network - Simulated multi-construct validation study
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nomo_demo_walkthroughnomo_demo_walkthrough_items - Shared walkthrough data from content review to empirical screening