# topicmodels v0.2-9

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