textrecipes 0.3.0

stopwords package have been moved from Imports to Suggests.
step_ngram() gained an argument min_num_tokens to be able to return multiple n-grams together. (#90)
Adds step_text_normalization() to perform unicode normalization on character vectors. (#86)

textrecipes 0.2.3

step_word_embeddings() got a argument aggregation_default to specify value in cases where no words matches embedding.

step_tokenize() got an engine argument to specify packages other then tokenizers to tokenize.
spacyr have been added as an engine to step_tokenize().
step_lemma() has been added to extract lemma attribute from tokenlists.
step_pos_filter() has been added to allow filtering of tokens bases on their pat of speech tags.
step_ngram() has been added to generate ngrams from tokenlists.
step_stem() not correctly uses the options argument. (Thanks to @grayskripko for finding bug, #64)

step_word2vec() have been changed to step_lda() to reflect what is actually happening.
step_word_embeddings() has been added. Allows for use of pre-trained word embeddings to convert token columns to vectors in a high-dimensional “meaning” space. (@jonthegeek, #20)
text2vec have been changed from Imports to Suggests.
textfeatures have been changed from Imports to Suggests.
step_tfidf() calculations are slightly changed due to flaw in original implementation https://github.com/dselivanov/text2vec/issues/280.

Custom stemming function can now be used in step_stem using the custom_stemmer argument.
step_textfeatures() have been added, allows for multiple numerical features to be pulled from text.
step_sequence_onehot() have been added, allows for one hot encoding of sequences of fixed width.
step_word2vec() have been added, calculates word2vec dimensions.
step_tokenmerge() have been added, combines multiple list columns into one list-columns.
step_texthash() now correctly accepts signed argument.
Documentation have been improved to showcase the importance of filtering tokens before applying step_tf() and step_tfidf().

First CRAN version