lda.svi: 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) <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.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: Rcpp, reshape2, tm (≥ 0.6), methods, Rdpack
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: topicmodels
Published: 2019-07-12
Author: Nicholas Erskine [aut, cre]
Maintainer: Nicholas Erskine <nicholas.erskine95 at gmail.com>
BugReports: https://github.com/nerskin/lda.svi/issues
License: MIT + file LICENSE
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: lda.svi results


Reference manual: lda.svi.pdf
Package source: lda.svi_0.1.0.tar.gz
Windows binaries: r-devel: lda.svi_0.1.0.zip, r-release: lda.svi_0.1.0.zip, r-oldrel: lda.svi_0.1.0.zip
OS X binaries: r-release: lda.svi_0.1.0.tgz, r-oldrel: lda.svi_0.1.0.tgz


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