topicmodels v0.2-4

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by Bettina Gruen

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
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Details

Type Package
Additional_repositories http://datacube.wu.ac.at
SystemRequirements GNU Scientific Library version >= 1.8
License GPL-2
Encoding UTF-8
LazyLoad yes
NeedsCompilation yes
Packaged 2016-05-23 06:13:16 UTC; hornik
Repository CRAN
Date/Publication 2016-05-23 08:33:09

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