SSN2: The next generation of spatial stream network modeling in R.

Michael Dumelle, Erin E Peterson, Jay M Ver Hoef, Alan Pearse, Daniel J Isaak
Author Information
  1. Michael Dumelle: Pacific Ecological Systems Division, United States Environmental Protection Agency, Corvallis, OR, USA. ORCID
  2. Erin E Peterson: EP Consulting and Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia. ORCID
  3. Jay M Ver Hoef: NMFS Alaska Fisheries Science Center, United States National Oceanic and Atmospheric Administration, Seattle, WA, USA. ORCID
  4. Alan Pearse: NIASRA, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia. ORCID
  5. Daniel J Isaak: Rocky Mountain Research Station, United States Forest Service, Boise, ID, USA.

Abstract

The SSN2 package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the -spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & G��mez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN package but leverages modern -spatial tools like sf (Pebesma, 2018). SSN2 also provides many useful features that were not available in the SSN package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other generic functions.

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Grants

  1. EPA999999/Intramural EPA

Word Cloud

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