Markov Chain Monte Carlo Algorithms for the Latent Dirichlet Allocation Model

This R package, ldamcmc, implements several Markov chain Monte Carlo (MCMC) algorithms for the latent Dirichlet allocation (LDA) model. This includes:

• The augmented collapsed Gibbs sampling (ACGS, Griffiths and Steyvers 2004, George and Doss 2015) algorithm
• The full Gibbs sampling (FGS, George and Doss 2015) algorithm
• The serial tempering (George and Doss 2015, Geyer 2011) algorithm
• Hyperparameter selection in the LDA model (George and Doss 2015)
• Posterior predictive checking (PPC, Chen and Doss 2015)

For package documentation run

help("ldamcmc")

in an R console. All major functions and datasets are documented and linked to the package index. Raw data files for each dataset are available in the data-raw folder. To load raw data see demo/load_raw_data.R .

To see all demo R scripts available in this package, run

demo(package="ldamcmc")

in an R console. Some scripts can be executed via running

demo(file-name, package="ldamcmc")

in an R console. The rest of them may require commandline arguments for execution. Please see the documentation provided in each script before execution.

Authors

• Clint P. George (Please contact for questions and comments)
• Hani Doss

Dependencies

This package uses the following R packages, which are already included in this R package.

• Rcpp
• RcppArmadillo based on the Armadillo C++ package
• lattice

Installation Guide

• Download the package source from Git Download Link
• Unzip the dowloaded file and rename the folder ldamcmc-master to ldamcmc
• To install ldamcmc run R CMD INSTALL ldamcmc on the commandline
• To uninstall ldamcmc run R CMD REMOVE ldamcmc on the commandline

References

1. Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research 3 993-1022.
2. Chen, Z. and Doss, H. (2015). Inference for the number of topics in the latent Dirichlet allocation model via Bayesian mixture modelling. Tech. rep., Department of Statistics, University of Florida.
3. George, C.P. and Doss, H. (2018). Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model. Journal of Machine Learning Research. Supplement
4. Geyer, C. J. (2011). Importance sampling, simulated tempering, and umbrella sampling. In Handbook of Markov Chain Monte Carlo (S. P. Brooks, A. E. Gelman, G. L. Jones and X. L. Meng, eds.). Chapman & Hall/CRC, Boca Raton, 295-311.
5. Griffiths, T. L. and Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences 101 5228-5235.

Acknowledgements

This research is partially supported by generous contributions from the International Center for Automated Research (ICAIR) at the University of Florida Levin College of Law.

The authors would like to thank Dr. Joseph N. Wilson, Zhe Chen, and Wei Xia for the valuable discussions and critics that helped throughout the development of this project.