as_psych                Coerce to psych fa Object
big5                    IPIP Big Five 50-Item Inventory with Qwen3
                        Embeddings
plot.sfa_coverage       Plot a Content-Validity Audit
plot.sfa_coverage_battery
                        Plot One Factor of a Content-Validity Audit
                        Battery
semanticfa-package      semanticfa: Semantic Factor Analysis of
                        Language Model Embeddings
sfa                     Semantic Factor Analysis
sfa_anchor              Construct-Label and Centroid Anchoring
sfa_build_bank          Pre-Embed a Set of Texts into a Reusable Bank
sfa_build_region        Build a Construct Region from a Text Corpus
sfa_build_regions       Build Many Construct Regions in One Corpus Pass
sfa_cd                  Comparison-Data Misfit Profile
sfa_clear_cache         Clear Embedding Cache
sfa_combine_banks       Combine Embedding Lookups
sfa_congruence          Compare Semantic and Empirical Factor
                        Structures
sfa_corplot             Heatmap of an Item-by-Item Similarity Matrix
sfa_coverage            Audit the Content Validity of a Scale Against a
                        Construct Region
sfa_cross_matrix        Extract the Numeric Matrix from a Cross-Audit
sfa_deletion_gaps       Newly Uncovered Content After an Item-Set
                        Change
sfa_dimselect           Embedding-Dimension Selection by EGA Depth
                        Optimization
sfa_ekc                 Empirical Kaiser Criterion for Embedding
                        Similarity Matrices
sfa_embed               Embed Item Text with a Language Model
sfa_embedding_bank      Use a Pre-Generated Embedding Bank as an
                        Embedder
sfa_gaps                Gap Table from a Coverage Audit
sfa_install_python      Provision the Python Environment for Embedding
sfa_item_fit            Vet a Candidate Scale Item Before Data
                        Collection
sfa_itemplot            2-D Item Map (t-SNE, UMAP, PCA, or MDS)
sfa_jinglejangle        Detect Jingle and Jangle Fallacies Across
                        Scales
sfa_leximax             Leximax: Rotate Factor Axes Toward the
                        Construct Lexicon
sfa_lexmap              Build the Lexical Map for Leximax Rotation
sfa_load_npz            Load Pre-generated Embeddings from a NumPy .npz
                        File
sfa_load_region         Load a Saved Construct Region
sfa_map                 Velicer's Minimum Average Partial for
                        Similarity Matrices
sfa_name                Name the Factors of an sfa Fit
sfa_nameability         Nameability of an Orientation
sfa_naming_instruction
                        The Default Naming Instruction
sfa_nfactors            Unified Factor Retention Diagnostics
sfa_nli_matrix          Signed Item Similarity from Natural Language
                        Inference
sfa_parallel            Embedding-Adapted Parallel Analysis
sfa_pool                Fetch or Build the Candidate Pool for a Naming
                        Model
sfa_project             Semantic Projection onto Bipolar Axes
sfa_redundancy          Detect Redundant (Near-Duplicate) Items
sfa_reembed_region      Re-Embed a Construct Region Under Another
                        Encoder
sfa_region_bank         Use a Region's Own Stored Embeddings as an
                        Embedder
sfa_semk                Calibrated Semantic Factor Retention (sem-k)
sfa_similarity          Compute Embedding Similarity Matrix
sfa_simplify            Response-Free Scale Simplification
