Collapsed Gibbs Sampling Methods for Topic Models
A subset of the Cora dataset of scientific documents.
Functions to manipulate text corpora in LDA format.
tools:::Rd_package_title("lda")
Functions to Fit LDA-type models
Generate LDA Documents from Raw Text
Convert a set of links keyed on source to a single list of edges.
A collection of newsgroup messages with classes.
Collapsed Gibbs Sampling for the Networks Uncovered By Bayesian Infere...
A collection of political blogs with ratings.
Compute predictive distributions for fitted LDA-type models.
Use the RTM to predict whether a link exists between two documents.
Read LDA-formatted Document and Vocabulary Files
Collapsed Gibbs Sampling for the Relational Topic Model (RTM).
Predict the response variable of documents using an sLDA model.
Get the Top Words and Documents in Each Topic
Compute Summary Statistics of a Corpus
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.