as_embed                Word vectors data class: 'wordvec' and 'embed'.
cosine_similarity       Cosine similarity/distance between two vectors.
data_transform          Transform plain text of word vectors into
                        'wordvec' (data.table) or 'embed' (matrix),
                        saved in a compressed ".RData" file.
data_wordvec_load       Load word vectors data ('wordvec' or 'embed')
                        from ".RData" file.
data_wordvec_subset     [S3 method] Extract a subset of word vectors
                        data.
demodata                Demo data (pre-trained using word2vec on Google
                        News; 8000 vocab, 300 dims).
dict_expand             Expand a dictionary from the most similar
                        words.
dict_reliability        Reliability analysis and PCA of a dictionary.
get_wordvec             Extract word vector(s).
most_similar            Find the Top-N most similar words.
normalize               Normalize all word vectors to the unit length
                        1.
orth_procrustes         Orthogonal Procrustes rotation for matrix
                        alignment.
pair_similarity         Compute a matrix of cosine similarity/distance
                        of word pairs.
plot_network            Visualize a (partial correlation) network graph
                        of words.
plot_similarity         Visualize cosine similarity of word pairs.
plot_wordvec            Visualize word vectors.
plot_wordvec_tSNE       Visualize word vectors with dimensionality
                        reduced using t-SNE.
sum_wordvec             Calculate the sum vector of multiple words.
tab_similarity          Tabulate cosine similarity/distance of word
                        pairs.
test_RND                Relative Norm Distance (RND) analysis.
test_WEAT               Word Embedding Association Test (WEAT) and
                        Single-Category WEAT.
tokenize                Tokenize raw text for training word embeddings.
train_wordvec           Train static word embeddings using the
                        Word2Vec, GloVe, or FastText algorithm.
