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inlabru : an R package for Bayesian spatial modelling from ecological survey data
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dc.contributor.author | Bachl, Fabian E. | |
dc.contributor.author | Lindgren, Finn | |
dc.contributor.author | Borchers, David L. | |
dc.contributor.author | Illian, Janine B. | |
dc.date.accessioned | 2020-03-21T00:33:33Z | |
dc.date.available | 2020-03-21T00:33:33Z | |
dc.date.issued | 2019-06 | |
dc.identifier | 258089729 | |
dc.identifier | 64b7e7c2-5e99-4b36-9f3c-81a58626560f | |
dc.identifier | 85063212449 | |
dc.identifier | 000470017200002 | |
dc.identifier.citation | Bachl , 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 . https://doi.org/10.1111/2041-210X.13168 | en |
dc.identifier.issn | 2041-210X | |
dc.identifier.other | RIS: urn:04D471224E7FB54EDF121C9759FE8B7E | |
dc.identifier.other | ORCID: /0000-0002-3944-0754/work/72842454 | |
dc.identifier.uri | https://hdl.handle.net/10023/19688 | |
dc.description | This research was funded by EPSRC grants EP/K041061/1, EP/K041053/1, and EP/K041053/2. | en |
dc.description.abstract | 1. 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.format.extent | 7 | |
dc.format.extent | 3853310 | |
dc.language.iso | eng | |
dc.relation.ispartof | Methods in Ecology and Evolution | en |
dc.subject | Bayesian inference | en |
dc.subject | Georeferenced data | en |
dc.subject | Point process | en |
dc.subject | Spatial count | en |
dc.subject | Spatial modelling | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | QH301 Biology | en |
dc.subject | DAS | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | QH301 | en |
dc.title | inlabru : an R package for Bayesian spatial modelling from ecological survey data | en |
dc.type | Journal article | en |
dc.contributor.sponsor | EPSRC | en |
dc.contributor.institution | University of St Andrews. Statistics | en |
dc.contributor.institution | University of St Andrews. Marine Alliance for Science & Technology Scotland | en |
dc.contributor.institution | University of St Andrews. Scottish Oceans Institute | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Ecological & Environmental Modelling | en |
dc.identifier.doi | 10.1111/2041-210X.13168 | |
dc.description.status | Peer reviewed | en |
dc.date.embargoedUntil | 2020-03-21 | |
dc.identifier.grantnumber | EP/K041061/1 | en |
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