bootstrap_contrast      Bootstrap similarity and ratio computations
bootstrap_nns           Bootstrap nearest neighbors
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_grouped_similarity
                        Get averaged similarity scores between target
                        word(s) and one or two vectors of candidate
                        words.
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
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
