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: 1.4.2
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-prerel: lda_1.4.2.zip, r-release: lda_1.4.2.zip, r-oldrel: lda_1.4.2.zip
macOS binaries: r-prerel (arm64): lda_1.4.2.tgz, r-release (arm64): lda_1.4.2.tgz, r-oldrel (arm64): lda_1.4.2.tgz, r-prerel (x86_64): lda_1.4.2.tgz, r-release (x86_64): lda_1.4.2.tgz
Old sources: lda archive

Reverse dependencies:

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


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