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: NA
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:

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