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lda (version 1.4.2)

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.

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Install

install.packages('lda')

Monthly Downloads

4,365

Version

1.4.2

License

LGPL

Maintainer

Last Published

November 22nd, 2015

Functions in lda (1.4.2)

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