  Recent developments in data science and big data research have produced an
abundance of large data sets that are too big to be analyzed in their entirety,
due to limits on either computer memory or storage capacity. Here, we introduce
our R package 'BayesSummaryStatLM' for Bayesian linear regression models with
Markov chain Monte Carlo implementation that overcomes these limitations. Our
Bayesian models use only summary statistics of data as input; these summary
statistics can be calculated from subsets of big data and combined over
subsets. Thus, complete data sets do not need to be read into memory in full,
which removes any physical memory limitations of a user. Our package
incorporates the R package 'ff' and its functions for reading in big data sets
in chunks while simultaneously calculating summary statistics. We describe our
Bayesian linear regression models, including several choices of prior
distributions for unknown model parameters, and illustrate capabilities and
features of our R package using both simulated and real data sets.

Applications;Computation