Changes in v0.6.1

• Mention doc2vec in package description.
• Add perplexity() to asses models’ the goodness-of-fit to data.
• Save quanteda’s internal docvars in the textmodel_doc2vec objects.
• Add group to as.matrix() to average sentence or paragraph vectors from the same documents.

Changes in v0.6.0

• Upgrade textmodel_doc2vec to train the distributed memory (DM) and distributed bag-of-word (DBOW) models.
• Add as.textmodel_doc2vec() to create document vectors as weighted average of word vectors.
• Add layer to as.matrix() to choose between word or document vectors.
• normalize is now defunct in textmodel_word2vec().

Changes in v0.5.1

• Add normalize to textmodel_doc2vec() and pass it to as.matrix().
• Add weights to textmodel_doc2vec() to adjust the salience of words in the document vectors.
• Add include_data to textmodel_word2vec() to save the original tokens object.

Changes in v0.5.0

• Add the model argument to textmodel_word2vec() to update existing models.
• The normalize argument is moved from textmodel_word2vec() to as.matrix(). The original argument is deprecated and set to FALSE by default.
• Remove weights().
• Improve the structure of C++ code.

Changes in v0.4.0

• Add the tolower argument and set to TRUE to lower-case tokens.
• Allow x to be quanteda’s tokens_xptr object to enhance efficiency.

Changes in v0.3.0

• Save docvars in the textmodel_doc2vec objects.
• Set zero for empty documents in the textmodel_doc2vec objects.
• Add probability() to compute probability of words.

Changes in v0.2.0

• Rename word2vec(), doc2vec() and lsa() to textmodel_word2vec(), textmodel_doc2vec() and textmodel_lsa() respectively.
• Simplify the C++ code to make maintenance easier.
• Add normalize to word2vec to disable or enable word vector normalization.
• Add weights() to extract back-propagation weights.
• Make analogy() to convert a formula to named character vector.
• Improve the stability of word2vec() when verbose = TRUE.

Changes in v0.1.0

• Fork https://github.com/bnosac/word2vec and change the package name to wordvector.
• Replace a list of character with quanteda’s tokens object as an input object.
• Recreate word2vec() with new argument names and object structures.
• Create lda() to train word vectors using Latent Semantic Analysis.
• Add similarity() and analogy() functions using proxyC.
• Add data_corpus_news2014 that contain 20,000 news summaries as package data.