| 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 |