| bootstrap_contrast | Bootstrap similarity and ratio computations |
| bootstrap_nns | Bootstrap nearest neighbors |
| bootstrap_ols | Bootstrap OLS |
| bootstrap_similarity | Boostrap similarity vector |
| build_conText | build a 'conText-class' object |
| build_dem | build a 'dem-class' object |
| build_fem | build a 'fem-class' object |
| compute_contrast | Compute similarity and similarity ratios |
| compute_similarity | Compute similarity vector (sub-function of bootstrap_similarity) |
| compute_transform | Compute transformation matrix A |
| conText | Embedding regression |
| contrast_nns | Contrast nearest neighbors |
| cos_sim | Compute the cosine similarity between one or more ALC embeddings and a set of features. |
| cr_glove_subset | GloVe subset |
| cr_sample_corpus | Congressional Record sample corpus |
| cr_transform | Transformation matrix |
| dem | Build a document-embedding matrix |
| dem_group | Average document-embeddings in a dem by a grouping variable |
| dem_sample | Randomly sample documents from a dem |
| embed_target | Embed target using either: (a) a la carte OR (b) simple (untransformed) averaging of context embeddings |
| feature_sim | Given two feature-embedding-matrices, compute "parallel" cosine similarities between overlapping features. |
| fem | Create an feature-embedding matrix |
| find_cos_sim | Find cosine similarities between target and candidate words |
| find_nns | Return nearest neighbors based on cosine similarity |
| get_context | Get context words (words within a symmetric window around the target word/phrase) sorrounding a user defined target. |
| get_cos_sim | Given a tokenized corpus, compute the cosine similarities of the resulting ALC embeddings and a defined set of features. |
| get_local_vocab | Identify words common to a collection of texts and a set of pretrained embeddings. |
| get_ncs | Given a set of tokenized contexts, find the top N nearest contexts. |
| get_nns | Given a tokenized corpus and a set of candidate neighbors, find the top N nearest neighbors. |
| get_nns_ratio | Given a corpus and a binary grouping variable, computes the ratio of cosine similarities over the union of their respective N nearest neighbors. |
| get_seq_cos_sim | Calculate cosine similarities between target word and candidates words over sequenced variable using ALC embedding approach |
| ncs | Given a set of embeddings and a set of tokenized contexts, find the top N nearest contexts. |
| nns | Given a set of embeddings and a set of candidate neighbors, find the top N nearest neighbors. |
| nns_ratio | Computes the ratio of cosine similarities for two embeddings over the union of their respective top N nearest neighbors. |
| permute_contrast | Permute similarity and ratio computations |
| permute_ols | Permute OLS |
| plot_nns_ratio | Plot output of 'get_nns_ratio()' |
| prototypical_context | Find most "prototypical" contexts. |
| run_ols | Run OLS |
| tokens_context | Get the tokens of contexts sorrounding user defined patterns |