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.

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Description file content

Package
lda
Type
Package
Title
Collapsed Gibbs Sampling Methods for Topic Models
Version
1.4.2
Date
2015-11-22
Author
Jonathan Chang
Maintainer
Jonathan Chang
Description
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.
License
LGPL
LazyLoad
yes
Suggests
Matrix, reshape2, ggplot2 (>= 1.0.0), penalized, nnet
NeedsCompilation
yes
Packaged
2015-11-22 08:13:39 UTC; jonathanchang
Depends
R (>= 2.10)
Repository
CRAN
Date/Publication
2015-11-22 11:48:11

install.packages('lda')

1.4.2

02 years ago

Jonathan Chang

LGPL

Depends on

R (>= 2.10)

Suggests

Matrix, reshape2, ggplot2 (>= 1.0.0), penalized, nnet

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