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: | 1.5.2 |
| Depends: | R (≥ 4.3.0) |
| Imports: | methods (≥ 4.3.0) |
| Suggests: | Matrix, reshape2, ggplot2 (≥ 3.4.4), penalized, nnet |
| Published: | 2024-04-27 |
| Author: | Jonathan Chang |
| Maintainer: | Santiago Olivella <olivella at unc.edu> |
| License: | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.1)] |
| NeedsCompilation: | yes |
| Materials: | README |
| In views: | NaturalLanguageProcessing |
| CRAN checks: | lda results |
| Reference manual: | lda.pdf |
| Package source: | lda_1.5.2.tar.gz |
| Windows binaries: | r-devel: lda_1.4.2.zip, r-release: lda_1.4.2.zip, r-oldrel: lda_1.5.2.zip |
| macOS binaries: | r-release (arm64): lda_1.5.2.tgz, r-oldrel (arm64): lda_1.5.2.tgz, r-release (x86_64): lda_1.5.2.tgz, r-oldrel (x86_64): lda_1.5.2.tgz |
| Old sources: | lda archive |
| Reverse imports: | ldaPrototype, NetMix, stm, tosca |
| Reverse suggests: | LDAvis, qdap, sentopics, textmineR, topicmodels |
| Reverse enhances: | quanteda |
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