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
• Package README

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)