Bayesian Spatio-Temporal Analysis in Stream Networks

Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". . In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.

SSNbayes

SSNbayes fits spatio-temporal stream network data using Bayesian inference in Stan.

Installation

You can install the released version of SSNbayes from CRAN with:

install.packages("SSNbayes")

And the development version from GitHub with:

devtools::install_github("EdgarSantos-Fernandez/SSNbayes")

See more details in the articles Santos-Fernandez, Hoef, et al. (2022) and Santos-Fernandez, Ver Hoef, et al. (2022)

Reproducible examples

These examples show the package in action:

Example of one of the outputs produced

Evolution of the Boise River exceedance probability:

Alt Text

References

Santos-Fernandez, Edgar, Jay M. Ver Hoef, James M. McGree, Daniel J. Isaak, Kerrie Mengersen, and Erin E. Peterson. 2022. “SSNbayes: An r Package for Bayesian Spatio-Temporal Modelling on Stream Networks.” arXiv. https://doi.org/10.48550/ARXIV.2202.07166.

Santos-Fernandez, Edgar, Jay M. Ver Hoef, Erin E. Peterson, James McGree, Daniel J. Isaak, and Kerrie Mengersen. 2022. “Bayesian Spatio-Temporal Models for Stream Networks.” Computational Statistics & Data Analysis 170: 107446. https://doi.org/https://doi.org/10.1016/j.csda.2022.107446.