Fit Latent Dirichlet Allocation Models using Stochastic Variational Inference

Fits Latent Dirichlet Allocation topic models to text data using the stochastic variational inference algorithm described in Hoffman et. al. (2013) . This method is more efficient than the original batch variational inference algorithm for LDA, and allows users to fit LDA models with more topics and to larger text corpora than would be feasible using that older method.


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0.1.0 by Nicholas Erskine, 7 months ago

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Authors: Nicholas Erskine [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports Rcpp, reshape2, tm, methods, Rdpack

Suggests topicmodels

Linking to Rcpp, RcppArmadillo, BH

System requirements: C++11

See at CRAN