Changes in v1.0.0

• Add auto_iter to textmodel_seededlda() and textmodel_lda() to stop Gibbs sampling automatically before max_iter is reached.
• Add batch_size to textmodel_seededlda() and textmodel_lda() to enable the distributed LDA algorithm for parallel computing.

Changes in v0.9.0

• Add the gamma parameter to textmodel_seededlda() and textmodel_lda() for sequential classification.
• Add textmodel_seqlda() as as short cut for textmodel_lda(gamma = 0.5).
• Improve the calculation of weights for seed words.
• Add the regularize argument to divergence() for the regularized topic divergence measure.

Changes in v0.8.4

• Fix for deprecation in Matrix 1.5-4.

Changes in v0.8.3

• Add data_corpus_moviereviews to the package to reduce dependency.

Changes in v0.8.2

• Add min_prob and select to topics() for greater flexibility
• Change the divergence measure from Kullback-Leibler to Jensen-Shannon.
• Add weighted, min_size, select to divergence() for regularized topic divergence scores.

Changes in v0.8.1

• Change textmodel_seededlda() to set positive integer values to residual.
• Fix a bug in textmodel_seededlda() that ignores n-grams when concatenator is not “_“.
• Change topics() to return document names.
• Add divergence() to optimize the number of topics or the seed words (#26).

Changes in v0.8.0

• Add the model argument to textmodel_lda() to replace predict().

Changes in v0.7.0

• Change the textmodel_seededlda object to save dictionary and related settings (#18)

Changes in v0.6.0

• Add predict() to identify topics of unseen documents (#9)
• Allow selecting seed words based on their frequencies using dfm_trim() in textmodel_seededlda() via ... (#8)

Changes in v0.5.1

• Change topics() to return factor with NA for empty documents
• Fix a bug in initializing LDA that leads to incorrect phi (#4 and #6)

Changes in v0.5

• Implement original LDA estimator using the LDAGibbs++ library