lda-package: Collapsed Gibbs Sampling Methods for Topic Models

In lda: Collapsed Gibbs Sampling Methods for Topic Models

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

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Author(s)

Jonathan Chang

Maintainer: Santiago Olivella <[e-mail]>

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)

lda documentation built on June 22, 2024, 6:47 p.m.