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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install.packages("lda.svi")

0.1.0 by Nicholas Erskine, 2 months ago


Report a bug at https://github.com/nerskin/lda.svi/issues


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


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