Show simple item record

Files in this item


Item metadata

dc.contributor.authorBachl, Fabian E.
dc.contributor.authorLindgren, Finn
dc.contributor.authorBorchers, David L.
dc.contributor.authorIllian, Janine B.
dc.identifier.citationBachl , F E , Lindgren , F , Borchers , D L & Illian , J B 2019 , ' inlabru : an R package for Bayesian spatial modelling from ecological survey data ' , Methods in Ecology and Evolution , vol. 10 , no. 6 , pp. 760-766 .
dc.identifier.otherRIS: urn:04D471224E7FB54EDF121C9759FE8B7E
dc.identifier.otherORCID: /0000-0002-3944-0754/work/72842454
dc.descriptionThis research was funded by EPSRC grants EP/K041061/1, EP/K041053/1, and EP/K041053/2.en
dc.description.abstract1.  Spatial processes are central to many ecological processes, but fitting models that incorporate spatial correlation to data from ecological surveys is computationally challenging. This is particularly true of point pattern data (in which the primary data are the locations at which target species are found), but also true of gridded data, and of georeferenced samples from continuous spatial fields. 2.  We describe here the R package inlabru that builds on the widely-used R-INLA package to provide easier access to Bayesian inference from spatial point process, spatial count, gridded, and georeferenced data, using integrated nested Laplace approximation (INLA, Rue et al., 2009). 3.  The package povides methods for fitting spatial density surfaces and estimating abundance, as well as for plotting and prediction. It accommodates data that are points, counts, georeferenced samples, or distance sampling data. 4.  This paper describes the main features of the package, illustrated by fitting models to the gorilla nest data contained in the package spatstat (Baddeley & Turner, 2005), a line transect survey data set contained in the package dsm (Miller et al., 2018), and to a georeferenced sample from a simulated continuous spatial field.
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.subjectBayesian inferenceen
dc.subjectGeoreferenced dataen
dc.subjectPoint processen
dc.subjectSpatial counten
dc.subjectSpatial modellingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.titleinlabru : an R package for Bayesian spatial modelling from ecological survey dataen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

This item appears in the following Collection(s)

Show simple item record