topicmodels: Topic Models

Provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Version: 0.2-7
Depends: R (≥ 2.15.0)
Imports: stats4, methods, modeltools, slam, tm (≥ 0.6)
Suggests: lasso2, lattice, lda, OAIHarvester, SnowballC, XML, corpus.JSS.papers
Published: 2017-11-03
Author: Bettina Grün [aut, cre], Kurt Hornik [aut]
Maintainer: Bettina Grün <Bettina.Gruen at>
License: GPL-2
NeedsCompilation: yes
SystemRequirements: GNU Scientific Library version >= 1.8, C++11
Citation: topicmodels citation info
Materials: NEWS
In views: NaturalLanguageProcessing
CRAN checks: topicmodels results


Reference manual: topicmodels.pdf
Vignettes: topicmodels: An R Package for Fitting Topic Models
Package source: topicmodels_0.2-7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: topicmodels_0.2-7.tgz
OS X Mavericks binaries: r-oldrel: topicmodels_0.2-7.tgz
Old sources: topicmodels archive

Reverse dependencies:

Reverse depends: BullsEyeR
Reverse imports: ldatuning, preText, revtools, textmineR, textmining
Reverse suggests: corpustools, LDAvis, PivotalR, quanteda, tidytext, udpipe, widyr


Please use the canonical form to link to this page.