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adjust_alpha as an experimental argument to
optimize alpha automatically.update_model to update terms of existing models to
classify documents with unseen words more accurately.std::vector to
arma::mat.perplexity() to compute perplexity scores of fitted
LDA models.alpha and beta to be a vector for
asymmetric Dirichlet priors.uniform to simplify the computation of seed word
weights.levels argument to better handle hierarchical
dictionaries.textmodel_seqlda() is called.auto_iter to textmodel_seededlda() and
textmodel_lda() to stop Gibbs sampling automatically before
max_iter is reached.batch_size to textmodel_seededlda()
and textmodel_lda() to enable the distributed LDA algorithm
for parallel computing.textmodel_seededlda() and
textmodel_lda() for sequential classification.textmodel_seqlda() as as short cut for
textmodel_lda(gamma = 0.5).regularize argument to
divergence() for the regularized topic divergence
measure.data_corpus_moviereviews to the package to reduce
dependency.min_prob and select to
topics() for greater flexibilityweighted, min_size,
select to divergence() for regularized topic
divergence scores.textmodel_seededlda() to set positive integer
values to residual.textmodel_seededlda() that ignores n-grams
when concatenator is not “_“.topics() to return document names.divergence() to optimize the number of topics or
the seed words (#26).model argument to textmodel_lda()
to replace predict().textmodel_seededlda object to save
dictionary and related settings (#18)predict() to identify topics of unseen documents
(#9)dfm_trim() in textmodel_seededlda() via
... (#8)topics() to return factor with NA for empty
documentsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.