  Recent advances in big data and analytics research have provided a wealth of
large data sets that are too big to be analyzed in their entirety, due to
restrictions on computer memory or storage size. New Bayesian methods have been
developed for large data sets that are only large due to large sample sizes;
these methods partition big data sets into subsets, and perform independent
Bayesian Markov chain Monte Carlo analyses on the subsets. The methods then
combine the independent subset posterior samples to estimate a posterior
density given the full data set. These approaches were shown to be effective
for Bayesian models including logistic regression models, Gaussian mixture
models and hierarchical models. Here, we introduce the R package
parallelMCMCcombine which carries out four of these techniques for combining
independent subset posterior samples. We illustrate each of the methods using a
Bayesian logistic regression model for simulation data and a Bayesian Gamma
model for real data; we also demonstrate features and capabilities of the R
package. The package assumes the user has carried out the Bayesian analysis and
has produced the independent subposterior samples outside of the package. The
methods are primarily suited to models with unknown parameters of fixed
dimension that exist in continuous parameter spaces. We envision this tool will
allow researchers to explore the various methods for their specific
applications, and will assist future progress in this rapidly developing field.
