# topicmodels v0.2-4

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## 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 in topicmodels

 Name Description terms_and_topics Extract most likely terms or topics. posterior-methods Determine posterior probabilities build_graph Construct the adjacency matrix for a topic graph perplexity Methods for Function perplexity TopicModelcontrol-class Different classes for controlling the estimation of topic models TopicModel-class Virtual class "TopicModel" AssociatedPress Associated Press data CTM Correlated Topic Model LDA Latent Dirichlet Allocation ldaformat2dtm Transform data from and for use with the lda package distHellinger Compute Hellinger distance logLik-methods Methods for Function logLik No Results!