lda: Collapsed Gibbs Sampling Methods for Topic Models

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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.

Author
Jonathan Chang
Date of publication
2015-11-22 11:48:11
Maintainer
Jonathan Chang <slycoder@gmail.com>
License
LGPL
Version
1.4.2

View on CRAN

Man pages

cora
A subset of the Cora dataset of scientific documents.
filter.words
Functions to manipulate text corpora in LDA format.
lda.collapsed.gibbs.sampler
Functions to Fit LDA-type models
lda-package
Collapsed Gibbs Samplers and Related Utility Functions for...
lexicalize
Generate LDA Documents from Raw Text
links.as.edgelist
Convert a set of links keyed on source to a single list of...
newsgroups
A collection of newsgroup messages with classes.
nubbi.collapsed.gibbs.sampler
Collapsed Gibbs Sampling for the Networks Uncovered By...
poliblog
A collection of political blogs with ratings.
predictive.distribution
Compute predictive distributions for fitted LDA-type models.
predictive.link.probability
Use the RTM to predict whether a link exists between two...
read.documents
Read LDA-formatted Document and Vocabulary Files
rtm.collapsed.gibbs.sampler
Collapsed Gibbs Sampling for the Relational Topic Model...
sampson
Sampson monk data
slda.predict
Predict the response variable of documents using an sLDA...
top.topic.words
Get the Top Words and Documents in Each Topic
word.counts
Compute Summary Statistics of a Corpus

Files in this package

lda
lda/src
lda/src/cvb0.c
lda/src/gibbs.c
lda/NAMESPACE
lda/demo
lda/demo/lda.R
lda/demo/nubbi.R
lda/demo/mmsb.R
lda/demo/00Index
lda/demo/sldamc.R
lda/demo/slda.R
lda/demo/rtm.R
lda/data
lda/data/cora.cites.rda
lda/data/poliblog.documents.rda
lda/data/sampson.rda
lda/data/cora.titles.rda
lda/data/newsgroup.test.labels.rda
lda/data/poliblog.vocab.rda
lda/data/cora.documents.rda
lda/data/newsgroup.vocab.rda
lda/data/cora.vocab.rda
lda/data/datalist
lda/data/newsgroup.label.map.rda
lda/data/newsgroup.train.documents.rda
lda/data/newsgroup.train.labels.rda
lda/data/poliblog.ratings.rda
lda/data/newsgroup.test.documents.rda
lda/R
lda/R/shift.word.indices.R
lda/R/top.topic.documents.R
lda/R/document.lengths.R
lda/R/lda.cvb0.R
lda/R/rtm.em.R
lda/R/nubbi.collapsed.gibbs.sampler.R
lda/R/top.topic.words.R
lda/R/predictive.link.probability.R
lda/R/predictive.distribution.R
lda/R/slda.em.R
lda/R/lda-internal.R
lda/R/rtm.collapsed.gibbs.sampler.R
lda/R/slda.predict.R
lda/R/lexicalize.R
lda/R/lda.collapsed.gibbs.sampler.R
lda/R/links.as.edgelist.R
lda/R/read.vocab.R
lda/R/slda.predict.docsums.R
lda/R/mmsb.collapsed.gibbs.sampler.R
lda/R/filter.words.R
lda/R/word.counts.R
lda/R/concatenate.documents.R
lda/R/read.documents.R
lda/MD5
lda/DESCRIPTION
lda/man
lda/man/poliblog.Rd
lda/man/lda-package.Rd
lda/man/top.topic.words.Rd
lda/man/filter.words.Rd
lda/man/predictive.distribution.Rd
lda/man/word.counts.Rd
lda/man/lda.collapsed.gibbs.sampler.Rd
lda/man/rtm.collapsed.gibbs.sampler.Rd
lda/man/nubbi.collapsed.gibbs.sampler.Rd
lda/man/read.documents.Rd
lda/man/links.as.edgelist.Rd
lda/man/slda.predict.Rd
lda/man/sampson.Rd
lda/man/lexicalize.Rd
lda/man/cora.Rd
lda/man/predictive.link.probability.Rd
lda/man/newsgroups.Rd