MRSea and MRSeaPower

MRSea

Marine renewables strategic environmental assessment

Software for fitting one and two dimensional spatially adaptive smoothing splines.  This software was developed for the marine renewables framework but is suitable for most smoothing applications. Information about the package and links to the package vignettes are at the bottom of the main MRSea github page.

Email comments to: [email protected]

For the latest package release (v1.3.1), please download from here or use the following code in R:

> devtools::install_github(repo = "lindesaysh/MRSea", ref="stable")

For the development version of MRSea, please use the following code in R to install:

> devtools::install_github(repo = "lindesaysh/MRSea", ref = "master")

References/Documentation

• Package:
  • Scott-Hayward, L.A.S., Walker, C.G. and M.L. Mackenzie (2021). Vignette for the MRSea Package v1.3: Statistical Modelling of bird and cetacean distributions in offshore renewables development areas. Centre for Research into Ecological and Environmental Modelling, University of St Andrews.
  • Mackenzie, M.L, Scott-Hayward, L.A.S., Oedekoven,  C.S., Skov, H., Humphreys, E., and Rexstad E. (2013). Statistical Modelling of  Seabird and Cetacean data: Guidance Document. University of St. Andrews contract for Marine Scotland; SB9 (CR/2012/05).
• CReSS smoothing:
  • Scott-Hayward, L., Mackenzie, M. L., Donovan, C.  R., Walker, C. G., and Ashe, E. (2013). Complex Region Spatial Smoother  (CReSS). Journal of Computational and Graphical Statistics.
  • Scott-Hayward, L., Mackenzie, M. L., Ashe, E. and R. Williams (2015). Modelling Killer whale feeding behaviour using a spatially adaptive complex region spatial smoother (CReSS) and generalised estimating equations (GEEs). Journal of Agricultural, Biological and Environmental Statistics,, 20, 305-322.
• SALSA1D:
  • Walker, C., Mackenzie, M., Donovan, C., and  O’Sullivan, M. (2011). SALSA – a Spatially Adaptive Local Smoothing  Algorithm. Journal of Statistical  Computation and Simulation 81, 179-191.
• SALSA 2D:
  • Scott-Hayward, L.A.S., M.L. Mackenzie, C.G. Walker, G. Shatumbu and W. Kilian, P. du Preez (2022). Automated surface feature selection using SALSA2D: An illustration using Elephant Mortality data in Etosha National Park.  https://arxiv.org/abs/2202.07977 Submitted to Journal of Applied Statistics.

MRSeaPower

Power analysis methods for assessing the Power to Detect Change.

Software for analysing the power to detect change. This work was undertaken by CREEM on behalf of the Scottish Government.

R package

For the latest package release, please download from here.

For the development version of MRSeaPower, please use the following code in R to install:

> devtools::install_git("https://github.com/lindesaysh/MRSeaPower.git")

References/Documentation

Mackenzie, M.L., Scott-Hayward, L.A.S., Paxton, C.G. and M.L. Burt (2017). Quantifying the Power to Detect Change: methodological development and implementation using the R package MRSeaPower.

Scott-Hayward, L.A.S. and M.L. Mackenzie (2017). MRSeaPower: Power analysis method for gamMRSea models. R package version 1.0.