  Spatio-temporal models are widely used in many research areas from ecology to
epidemiology. However, most covariance functions describe spatial relationships
based on Euclidean distance only. In this paper, we introduce the R package
SSNbayes for fitting Bayesian spatio-temporal models and making predictions on
branching stream networks. SSNbayes provides a linear regression framework with
multiple options for incorporating spatial and temporal autocorrelation.
Spatial dependence is captured using stream distance and flow connectivity
while temporal autocorrelation is modelled using vector autoregression
approaches. SSNbayes provides the functionality to make predictions across the
whole network, compute exceedance probabilities and other probabilistic
estimates such as the proportion of suitable habitat. We illustrate the
functionality of the package using a stream temperature dataset collected in
Idaho, USA.
