lda: Collapsed Gibbs Sampling Methods for Topic Models
Version 1.4.2

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.

Browse man pages Browse package API and functions Browse package files

AuthorJonathan Chang
Date of publication2015-11-22 11:48:11
MaintainerJonathan Chang <slycoder@gmail.com>
LicenseLGPL
Version1.4.2
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("lda")

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

Functions

concatenate.documents Man page Source code
cora Man page
cora.cites Man page
cora.documents Man page
cora.titles Man page
cora.vocab Man page
document.lengths Man page Source code
documents.as.Matrix Source code
filter.words Man page Source code
lda Man page
lda-package Man page
lda.collapsed.gibbs.sampler Man page Source code
lda.cvb0 Man page Source code
lexicalize Man page Source code
links.as.edgelist Man page Source code
mmsb.collapsed.gibbs.sampler Man page Source code
model.filenames Source code
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 Source code
pairwise.link.lda.collapsed.gibbs.sampler Source code
poliblog Man page
poliblog.documents Man page
poliblog.ratings Man page
poliblog.vocab Man page
predictive.distribution Man page Source code
predictive.link.probability Man page Source code
read.beta Source code
read.documents Man page Source code
read.vocab Man page Source code
rtm.collapsed.gibbs.sampler Man page Source code
rtm.em Man page Source code
sampson Man page
shift.word.indices Man page Source code
slda.collapsed.gibbs.sampler Source code
slda.em Man page Source code
slda.predict Man page Source code
slda.predict.docsums Man page Source code
top.topic.documents Man page Source code
top.topic.words Man page Source code
word.counts Man page Source code

Files

src
src/cvb0.c
src/gibbs.c
NAMESPACE
demo
demo/lda.R
demo/nubbi.R
demo/mmsb.R
demo/00Index
demo/sldamc.R
demo/slda.R
demo/rtm.R
data
data/cora.cites.rda
data/poliblog.documents.rda
data/sampson.rda
data/cora.titles.rda
data/newsgroup.test.labels.rda
data/poliblog.vocab.rda
data/cora.documents.rda
data/newsgroup.vocab.rda
data/cora.vocab.rda
data/datalist
data/newsgroup.label.map.rda
data/newsgroup.train.documents.rda
data/newsgroup.train.labels.rda
data/poliblog.ratings.rda
data/newsgroup.test.documents.rda
R
R/shift.word.indices.R
R/top.topic.documents.R
R/document.lengths.R
R/lda.cvb0.R
R/rtm.em.R
R/nubbi.collapsed.gibbs.sampler.R
R/top.topic.words.R
R/predictive.link.probability.R
R/predictive.distribution.R
R/slda.em.R
R/lda-internal.R
R/rtm.collapsed.gibbs.sampler.R
R/slda.predict.R
R/lexicalize.R
R/lda.collapsed.gibbs.sampler.R
R/links.as.edgelist.R
R/read.vocab.R
R/slda.predict.docsums.R
R/mmsb.collapsed.gibbs.sampler.R
R/filter.words.R
R/word.counts.R
R/concatenate.documents.R
R/read.documents.R
MD5
DESCRIPTION
man
man/poliblog.Rd
man/lda-package.Rd
man/top.topic.words.Rd
man/filter.words.Rd
man/predictive.distribution.Rd
man/word.counts.Rd
man/lda.collapsed.gibbs.sampler.Rd
man/rtm.collapsed.gibbs.sampler.Rd
man/nubbi.collapsed.gibbs.sampler.Rd
man/read.documents.Rd
man/links.as.edgelist.Rd
man/slda.predict.Rd
man/sampson.Rd
man/lexicalize.Rd
man/cora.Rd
man/predictive.link.probability.Rd
man/newsgroups.Rd
lda documentation built on May 29, 2017, 2:11 p.m.