lda-package {lda} | R Documentation |
Collapsed Gibbs Sampling Methods for Topic Models
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.
Details
The DESCRIPTION file:
Package: | lda |
Type: | Package |
Title: | Collapsed Gibbs Sampling Methods for Topic Models |
Version: | 1.5.2 |
Date: | 2024-04-25 |
Author: | Jonathan Chang |
Maintainer: | Santiago Olivella <olivella@unc.edu> |
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 (>= 2.1) |
LazyLoad: | yes |
Imports: | methods (>= 4.3.0) |
Suggests: | Matrix, reshape2, ggplot2 (>= 3.4.4), penalized, nnet |
Depends: | R (>= 4.3.0) |
Index of help topics:
cora A subset of the Cora dataset of scientific documents. filter.words Functions to manipulate text corpora in LDA format. lda-package Collapsed Gibbs Sampling Methods for Topic Models lda.collapsed.gibbs.sampler Functions to Fit LDA-type models lexicalize Generate LDA Documents from Raw Text links.as.edgelist Convert a set of links keyed on source to a single list of edges. newsgroup A collection of newsgroup messages with classes. nubbi.collapsed.gibbs.sampler Collapsed Gibbs Sampling for the Networks Uncovered By Bayesian Inference (NUBBI) Model. poliblog A collection of political blogs with ratings. predictive.distribution Compute predictive distributions for fitted LDA-type models. predictive.link.probability Use the RTM to predict whether a link exists between two documents. read.documents Read LDA-formatted Document and Vocabulary Files rtm.collapsed.gibbs.sampler Collapsed Gibbs Sampling for the Relational Topic Model (RTM). sampson Sampson monk data slda.predict Predict the response variable of documents using an sLDA model. top.topic.words Get the Top Words and Documents in Each Topic word.counts Compute Summary Statistics of a Corpus
Author(s)
Jonathan Chang
Maintainer: Santiago Olivella <olivella@unc.edu>
Special thanks to the following for their reports and comments: Edo Airoldi, Jordan Boyd-Graber, Christopher E. Cramer, Andrew Dai, James Danowski, Khalid El-Arini, Roger Levy, Solomon Messing, Joerg Reichardt, Dmitriy Selivanov
References
Blei, David M. and Ng, Andrew and Jordan, Michael. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003.
See Also
Functions to fit models:
lda.collapsed.gibbs.sampler
slda.em
mmsb.collapsed.gibbs.sampler
nubbi.collapsed.gibbs.sampler
rtm.collapsed.gibbs.sampler
Functions to read/create corpora:
lexicalize
read.documents
read.vocab
Functions to manipulate corpora:
concatenate.documents
filter.words
shift.word.indices
links.as.edgelist
Functions to compute summary statistics on corpora:
word.counts
document.lengths
Functions which use the output of fitted models:
predictive.distribution
top.topic.words
top.topic.documents
predictive.link.probability
Included data sets:
cora
poliblog
sampson
Examples
## See demos for the following three common use cases:
## Not run: demo(lda)
## Not run: demo(slda)
## Not run: demo(mmsb)
## Not run: demo(rtm)