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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