R: Semantic Factor Analysis of Language Model Embeddings

# Semantic Factor Analysis of Language Model Embeddings

## Documentation for package ‘semanticfa’ version 0.1.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

semanticfa-package | semanticfa: Semantic Factor Analysis of Language Model Embeddings
as_psych | Coerce to psych fa Object
as_psych.sfa | Coerce to psych fa Object
big5 | IPIP Big Five 50-Item Inventory with Sentence-BERT Embeddings
semanticfa | semanticfa: Semantic Factor Analysis of Language Model Embeddings
sfa | Semantic Factor Analysis
sfa_anchor | Construct-Label and Centroid Anchoring
sfa_clear_cache | Clear Embedding Cache
sfa_congruence | Compare Semantic and Empirical Factor Structures
sfa_corplot | Heatmap of an Item-by-Item Similarity Matrix
sfa_dimselect | Embedding-Dimension Selection by EGA Depth Optimization
sfa_embed | Embed Item Text with a Language Model
sfa_install_python | Provision the Python Environment for Embedding
sfa_itemplot | 2-D Item Map (t-SNE, UMAP, PCA, or MDS)
sfa_item_fit | Vet a Candidate Scale Item Before Data Collection
sfa_jinglejangle | Detect Jingle and Jangle Fallacies Across Scales
sfa_load_npz | Load Pre-generated Embeddings from a NumPy .npz File
sfa_nfactors | Unified Factor Retention Diagnostics
sfa_nli_matrix | Signed Item Similarity from Natural Language Inference
sfa_parallel | Embedding-Adapted Parallel Analysis
sfa_project | Semantic Projection onto Bipolar Axes
sfa_redundancy | Detect Redundant (Near-Duplicate) Items
sfa_similarity | Compute Embedding Similarity Matrix
sfa_simplify | Response-Free Scale Simplification
sfa_tsneplot | 2-D Item Map (t-SNE, UMAP, PCA, or MDS)