Collapsed Gibbs Sampling Methods for Topic Models

Implements latent Dirichlet allocation (LDA) and related models. This includes (but is not limited to) sLDA, corrLDA, and the mixed-membership stochastic blockmodel. Inference for all of these models is implemented via a fast collapsed Gibbs sampler written in C. Utility functions for reading/writing data typically used in topic models, as well as tools for examining posterior distributions are also included.


lda

Implements latent Dirichlet allocation (LDA) and related models.

Reference manual

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install.packages("lda")

1.5.2 by Santiago Olivella, 2 years ago


Browse source code at https://github.com/cran/lda


Authors: Jonathan Chang


Documentation:   PDF Manual  


LGPL (>= 2.1) license


Imports methods

Suggests Matrix, reshape2, ggplot2, penalized, nnet


Imported by ergmclust, ldaPrototype, stm, tosca.

Suggested by LDAvis, qdap, sentopics, textmineR, topicmodels.

Enhanced by quanteda.


See at CRAN