Implements latent Dirichlet allocation (LDA) and related models. This includes (but is not limited to) sLDA, corrLDA, and the mixed-membership stochastic blockmodel. Inference for all of these models is implemented via a fast collapsed Gibbs sampler written in C. Utility functions for reading/writing data typically used in topic models, as well as tools for examining posterior distributions are also included.
| Version: | 1.4.2 |
| Depends: | R (≥ 2.10) |
| Suggests: | Matrix, reshape2, ggplot2 (≥ 1.0.0), penalized, nnet |
| Published: | 2015-11-22 |
| Author: | Jonathan Chang |
| Maintainer: | Jonathan Chang <slycoder at gmail.com> |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| NeedsCompilation: | yes |
| In views: | NaturalLanguageProcessing |
| CRAN checks: | lda results |
| Reference manual: | lda.pdf |
| Package source: | lda_1.4.2.tar.gz |
| Windows binaries: | r-devel: lda_1.4.2.zip, r-release: lda_1.4.2.zip, r-oldrel: lda_1.4.2.zip |
| macOS binaries: | r-release (arm64): lda_1.4.2.tgz, r-oldrel (arm64): lda_1.4.2.tgz, r-release (x86_64): lda_1.4.2.tgz, r-oldrel (x86_64): lda_1.4.2.tgz |
| Old sources: | lda archive |
| Reverse imports: | ergmclust, ldaPrototype, NetMix, stm, tosca |
| Reverse suggests: | LDAvis, psychtm, qdap, quanteda, textmineR, topicmodels |
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