lda: Collapsed Gibbs sampling methods for topic models

This package 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 writtten 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: 1.3.2
Depends: R (≥ 2.10)
Suggests: Matrix, reshape2, ggplot2 (≥ 0.9.1), penalized
Published: 2012-10-14
Author: Jonathan Chang
Maintainer: Jonathan Chang <jonchang at fb.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_1.3.2.tar.gz
Windows binaries: r-devel: lda_1.3.2.zip, r-release: lda_1.3.2.zip, r-oldrel: lda_1.3.2.zip
OS X Snow Leopard binaries: r-release: lda_1.3.2.tgz, r-oldrel: lda_1.3.2.tgz
OS X Mavericks binaries: r-release: lda_1.3.2.tgz
Old sources: lda archive

Reverse dependencies:

Reverse imports: stm
Reverse suggests: qdap, topicmodels