model_word2vec: Word2Vec Model
In news-r/gensimr: Topic Modelling

The word2vec algorithms include skip-gram and CBOW models, using either hierarchical softmax or negative sampling.

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model_word2vec(...)

load_word2vec(file)

`...`	Any other options, from the official documentation.
`file`	Path to a saved model.

docs <- prepare_documents(corpus)

# initialise
word2vec <- model_word2vec(size = 100L, window = 5L, min_count = 1L)
word2vec$build_vocab(docs)
word2vec$train(docs, total_examples = word2vec$corpus_count, epochs = 20L)
word2vec$init_sims(replace = TRUE)

# use
word2vec$wv$most_similar(positive = c("interface"))
word2vec$wv$doesnt_match(c("human", "interface", "trees"))
word2vec$wv$similarity("human", "trees")