lda: Collapsed Gibbs Sampling Methods for Topic Models

Implements latent Dirichlet allocation (LDA) and related models. This includes (but is not limited to) sLDA, corrLDA, and the mixed-membership stochastic blockmodel. Inference for all of these models is implemented via a fast collapsed Gibbs sampler written in C. Utility functions for reading/writing data typically used in topic models, as well as tools for examining posterior distributions are also included.

Install the latest version of this package by entering the following in R:
AuthorJonathan Chang
Date of publication2015-11-22 11:48:11
MaintainerJonathan Chang <slycoder@gmail.com>

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concatenate.documents Man page
cora Man page
cora.cites Man page
cora.documents Man page
cora.titles Man page
cora.vocab Man page
document.lengths Man page
filter.words Man page
lda Man page
lda.collapsed.gibbs.sampler Man page
lda.cvb0 Man page
lda-package Man page
lexicalize Man page
links.as.edgelist Man page
mmsb.collapsed.gibbs.sampler Man page
newsgroup Man page
newsgroup.label.map Man page
newsgroup.test.documents Man page
newsgroup.test.labels Man page
newsgroup.train.documents Man page
newsgroup.train.labels Man page
newsgroup.vocab Man page
nubbi.collapsed.gibbs.sampler Man page
poliblog Man page
poliblog.documents Man page
poliblog.ratings Man page
poliblog.vocab Man page
predictive.distribution Man page
predictive.link.probability Man page
read.documents Man page
read.vocab Man page
rtm.collapsed.gibbs.sampler Man page
rtm.em Man page
sampson Man page
shift.word.indices Man page
slda.em Man page
slda.predict Man page
slda.predict.docsums Man page
top.topic.documents Man page
top.topic.words Man page
word.counts Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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