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.
|Date of publication||2015-11-22 11:48:11|
|Maintainer||Jonathan Chang <email@example.com>|
cora: A subset of the Cora dataset of scientific documents.
filter.words: Functions to manipulate text corpora in LDA format.
lda.collapsed.gibbs.sampler: Functions to Fit LDA-type models
lda-package: Collapsed Gibbs Samplers and Related Utility Functions for...
lexicalize: Generate LDA Documents from Raw Text
links.as.edgelist: Convert a set of links keyed on source to a single list of...
newsgroups: A collection of newsgroup messages with classes.
nubbi.collapsed.gibbs.sampler: Collapsed Gibbs Sampling for the Networks Uncovered By...
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...
read.documents: Read LDA-formatted Document and Vocabulary Files
rtm.collapsed.gibbs.sampler: Collapsed Gibbs Sampling for the Relational Topic Model...
sampson: Sampson monk data
slda.predict: Predict the response variable of documents using an sLDA...
top.topic.words: Get the Top Words and Documents in Each Topic
word.counts: Compute Summary Statistics of a Corpus