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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.
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