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
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Vignettes of topicmodels

Name
topicmodels.Rnw
topicmodels.bib
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Details

Type Package
Additional_repositories https://datacube.wu.ac.at
SystemRequirements GNU Scientific Library version >= 1.8, C++11
License GPL-2
Encoding UTF-8
LazyLoad yes
NeedsCompilation yes
Packaged 2019-12-03 06:42:35 UTC; hornik
Repository CRAN
Date/Publication 2019-12-03 07:03:58 UTC

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