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topicmodels (version 0.2-4)

Topic Models

Description

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.

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Version

Install

install.packages('topicmodels')

Monthly Downloads

9,388

Version

0.2-4

License

GPL-2

Maintainer

Bettina Gruen

Last Published

May 23rd, 2016

Functions in topicmodels (0.2-4)

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