lda: Collapsed Gibbs Sampling Methods for Topic Models (original) (raw)
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 |
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Depends: | R (≥ 4.3.0) |
Imports: | methods (≥ 4.3.0) |
Suggests: | Matrix, reshape2, ggplot2 (≥ 3.4.4), penalized, nnet |
Published: | 2024-04-27 |
DOI: | 10.32614/CRAN.package.lda |
Author: | Jonathan Chang |
Maintainer: | Santiago Olivella |
License: | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.1)] |
NeedsCompilation: | yes |
Materials: | README |
In views: | NaturalLanguageProcessing |
CRAN checks: | lda results |
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