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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.

Functions (12)

associatedpress

Associated Press data

build_graph

Construct the adjacency matrix for a topic graph

ctm

Correlated Topic Model

distHellinger

Compute Hellinger distance

get

Extract most likely terms or topics.

lda

Latent Dirichlet Allocation

ldaformat2dtm

Transform data from and for use with the lda package

logLik-methods

Methods for Function logLik

perplexity

Methods for Function perplexity

posterior-methods

Determine posterior probabilities

tm-class

Virtual class "TopicModel"

tmcontrol-class

Different classes for controlling the estimation of topic models

topicmodels

Topic Models

v0.2-12

GPL-2

Authors

Bettina Grün [aut, cre] (<https://orcid.org/0000-0001-7265-4773>), Kurt Hornik [aut] (<https://orcid.org/0000-0003-4198-9911>), David M Blei [ctb, cph] (VEM estimation of LDA and CTM), John D Lafferty [ctb, cph] (VEM estimation of CTM), Xuan-Hieu Phan [ctb, cph] (MCMC estimation of LDA), Makoto Matsumoto [ctb, cph] (Mersenne Twister RNG), Takuji Nishimura [ctb, cph] (Mersenne Twister RNG), Shawn Cokus [ctb] (Mersenne Twister RNG)

Initial release

topicmodels

Functions (12)

topicmodels

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