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lda: Collapsed Gibbs Sampling Methods for Topic Models

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
DOI: 10.32614/CRAN.package.lda
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

Documentation:

Reference manual: lda.pdf

Downloads:

Package source: lda_1.5.2.tar.gz
Windows binaries: r-devel: lda_1.5.2.zip, r-release: lda_1.5.2.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 dependencies:

Reverse imports: ldaPrototype, NetMix, stm, tosca
Reverse suggests: LDAvis, qdap, sentopics, textmineR, topicmodels
Reverse enhances: quanteda

Linking:

Please use the canonical form https://CRAN.R-project.org/package=lda to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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