SSN-package: Spatial Modeling on Stream Networks

Description Details Author(s) References


Creates spatial stream network representations in R and fits spatial models.


Package: SSN
Type: Package
Version: 1.1.12
Date: 2018-01-24
License: GPL-2
LazyLoad: yes

The SSN package provides tools to fit generalized linear models with spatial autocorrelation for stream network data. SSN uses normal likelihood methods (including REML) and quasi-likelihood for Poisson and Binomial families. The spatial formulation was originally described in Ver Hoef, Peterson, and Theobald (2006), with more details given by Ver Hoef and Peterson (2010) and Peterson and Ver Hoef (2010). The spatial data must be formatted in a geographic information system (GIS) prior to importing it into R. A custom ArcGIS toolbox has been provided to format the data: Spatial Tools for the Analysis of River Systems (STARS) toolset (Peterson and Ver Hoef, 2014), and the SSN package is fully described in Ver Hoef, Peterson, Clifford, and Shah (2014).


Jay Ver Hoef and Erin Peterson


Ver Hoef, J. M., Peterson, E. E. and Theobald, D. (2006) Spatial Statistical Models that Use Flow and Stream Distance. Environmental and Ecological Statistics 13, 449–464.

Ver Hoef, J. M. and Peterson, E. E. (2010) A Moving Average Approach for Spatial Statistical Models of Stream Networks (with Discussion). Journal of the American Statistical Association 105, 6–18. DOI: 10.1198/jasa.2009.ap08248. Rejoinder pgs. 22–24.

Peterson, E. E. and Ver Hoef, J. M. (2010) A Mixed-Model Moving-Average Approach to Geostatistical Modeling in Stream Networks. Ecology 91(3), 644–651.

Peterson, E. E. and Ver Hoef, J. M. 2014. STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data . Journal of Statistical Software 56(2): 1–17.

Ver Hoef, J. M., Peterson, E. E., Clifford, D. and Shah, R. (2014) SSN: An R Package for Spatial Statistical Modeling on Stream Networks. Journal of Statistical Software 56(3): 1–45.

SSN documentation built on March 13, 2020, 1:49 a.m.