Fix tests for quanteda v4.2.0.
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 documents