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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.4.2 by Jonathan Chang, 6 years ago

Browse source code at

Authors: Jonathan Chang

Documentation:   PDF Manual  

Task views: Natural Language Processing

LGPL license

Suggests Matrix, reshape2, ggplot2, penalized, nnet

Imported by NetMix, ergmclust, ldaPrototype, stm, themetagenomics, tosca.

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

See at CRAN