lda v1.4.2


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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.

Functions in lda

Name Description
links.as.edgelist Convert a set of links keyed on source to a single list of edges.
predictive.link.probability Use the RTM to predict whether a link exists between two documents.
newsgroups A collection of newsgroup messages with classes.
lexicalize Generate LDA Documents from Raw Text
rtm.collapsed.gibbs.sampler Collapsed Gibbs Sampling for the Relational Topic Model (RTM).
top.topic.words Get the Top Words and Documents in Each Topic
cora A subset of the Cora dataset of scientific documents.
nubbi.collapsed.gibbs.sampler Collapsed Gibbs Sampling for the Networks Uncovered By Bayesian Inference (NUBBI) Model.
poliblog A collection of political blogs with ratings.
word.counts Compute Summary Statistics of a Corpus
filter.words Functions to manipulate text corpora in LDA format.
slda.predict Predict the response variable of documents using an sLDA model.
read.documents Read LDA-formatted Document and Vocabulary Files
lda.collapsed.gibbs.sampler Functions to Fit LDA-type models
predictive.distribution Compute predictive distributions for fitted LDA-type models.
sampson Sampson monk data
lda-package Collapsed Gibbs Samplers and Related Utility Functions for LDA-type Models
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Type Package
Date 2015-11-22
License LGPL
LazyLoad yes
NeedsCompilation yes
Packaged 2015-11-22 08:13:39 UTC; jonathanchang
Repository CRAN
Date/Publication 2015-11-22 11:48:11

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