Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly
becoming feasible in many environmental applications due to the recent advances
in both statistical methodology and computation power. Implementation of these methods
using the Markov chain Monte Carlo (MCMC) computational techniques, however,
requires development of problem-specific and user-written computer code, possibly in a
low-level language. This programming requirement is hindering the widespread use of the
Bayesian model-based methods among practitioners and, hence there is an urgent need to
develop high-level software that can analyze large data sets rich in both space and time.
This paper develops the package spTimer for hierarchical Bayesian modeling of stylized
environmental space-time monitoring data as a contributed software package in the R
language that is fast becoming a very popular statistical computing platform. The package
is able to fit, spatially and temporally predict large amounts of space-time data using
three recently developed Bayesian models. The user is given control over many options
regarding covariance function selection, distance calculation, prior selection and tuning
of the implemented MCMC algorithms, although suitable defaults are provided. The
package has many other attractive features such as on the fly transformations and an
ability to spatially predict temporally aggregated summaries on the original scale, which
saves the problem of storage when using MCMC methods for large datasets. A simulation
example, with more than a million observations, and a real life data example are used to
validate the underlying code and to illustrate the software capabilities.

Keywords: Bayesian spatio-temporal modeling, Markov chain Monte Carlo, Gibbs sampling,
autoregressive, predictive processes.