Package: lda.svi
Title: Fit Latent Dirichlet Allocation Models using Stochastic
        Variational Inference
Version: 0.1.0
Authors@R: person("Nicholas", "Erskine", email = "nicholas.erskine95@gmail.com", role = c("aut", "cre"))
Description: Fits Latent Dirichlet Allocation topic models to text data using the stochastic variational inference algorithm described in Hoffman et. al. (2013) <arXiv:1206.7051v3>. 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.
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
BugReports: https://github.com/nerskin/lda.svi/issues
Encoding: UTF-8
RoxygenNote: 6.1.1
LinkingTo: Rcpp, RcppArmadillo, BH
Imports: Rcpp, reshape2, tm (>= 0.6), methods, Rdpack
Suggests: topicmodels
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2019-07-08 13:26:49 UTC; nicholas
Author: Nicholas Erskine [aut, cre]
Maintainer: Nicholas Erskine <nicholas.erskine95@gmail.com>
Repository: CRAN
Date/Publication: 2019-07-12 16:10:02 UTC
