Package: inlabru
Type: Package
Title: Bayesian Latent Gaussian Modelling using INLA and Extensions
Version: 2.5.2
Authors@R: c(
  person("Finn", "Lindgren", email = "finn.lindgren@gmail.com",
         role = c("aut", "cre", "cph"),
         comment = c(ORCID = "0000-0002-5833-2011",
                     "Finn Lindgren continued development of the main code")),
  person("Fabian E.", "Bachl", email = "bachlfab@gmail.com",
         role = c("aut", "cph"),
         comment = "Fabian Bachl wrote the main code"),
  person("David L.", "Borchers", email = "dlb@st-andrews.ac.uk",
         role = c("ctb", "dtc", "cph"),
         comment = "David Borchers wrote code for Gorilla data import and sampling, multiplot tool"),
  person("Daniel", "Simpson", email = "dp.simpson@gmail.com",
         role = c("ctb", "cph"),
         comment = "Daniel Simpson wrote the basic LGCP sampling method"),
  person("Lindesay", "Scott-Howard", email = "lass@st-andrews.ac.uk",
         role = c("ctb", "dtc", "cph"),
         comment = "Lindesay Scott-Howard provided MRSea data import code"),
  person("Seaton", "Andy", email = "andy.e.seaton@gmail.com", role = c("ctb"),
         comment = "Andy Seaton provided testing and bugfixes"),
  person("Suen", "Man Ho", email = "M.H.Suen@sms.ed.ac.uk", role = c("ctb", "cph"),
         comment = "Man Ho Suen contributed features for aggregated responses")
  )
URL: http://www.inlabru.org, https://inlabru-org.github.io/inlabru/
BugReports: https://github.com/inlabru-org/inlabru/issues
Description: Facilitates spatial and general latent Gaussian modeling using
  integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>).
  Additionally, extends the GAM-like model class to more general nonlinear predictor
  expressions, and implements a log Gaussian Cox process likelihood for 
  modeling univariate and spatial point processes based on ecological survey data.
  Model components are specified with general inputs and mapping methods to the
  latent variables, and the predictors are specified via general R expressions,
  with separate expressions for each observation likelihood model in
  multi-likelihood models. A prediction method based on fast Monte Carlo sampling
  allows posterior prediction of general expressions of the latent variables.
  Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019)
  <doi:10.1111/2041-210X.13168>.
License: GPL (>= 2)
Additional_repositories: https://inla.r-inla-download.org/R/testing
RoxygenNote: 7.1.2
Encoding: UTF-8
Depends: methods, R (>= 3.5), sp (>= 1.4-5), stats
Imports: MatrixModels, magrittr, Matrix, plyr, rgdal (>= 1.5.8), rgeos,
        rlang, utils, withr
Suggests: covr, dplyr, ggmap, ggplot2, ggpolypath, graphics, INLA (>=
        21.08.31), knitr, maptools, mgcv, patchwork, raster,
        RColorBrewer, rgl, rmarkdown, shiny, sn, spatstat.geom,
        spatstat.core, spatstat.data, spatstat (>= 2.0-0), sphereplot,
        testthat
Config/testthat/parallel: true
Config/testthat/edition: 3
Collate: '0_inlabru_envir.R' 'bru.covariate.R' 'bru.gof.R'
        'bru.inference.R' 'bru.integration.R' 'bru.spatial.R'
        'covariate.R' 'data.Poisson1_1D.R' 'data.Poisson2_1D.R'
        'data.Poisson3_1D.R' 'data.gorillas.R' 'data.mexdolphin.R'
        'data.mrsea.R' 'data.seals.R' 'data.shrimp.R'
        'data.toygroups.R' 'deltaIC.R' 'deprecated.R' 'dsdata.R'
        'dsmdata.R' 'dsmdata.tools.R' 'effect.R' 'environment.R'
        'fmesher_crs.R' 'fmesher_utils.R' 'ggplot.R' 'inla.R'
        'inlabru.R' 'integration.R' 'local_testthat.R' 'mesh.R'
        'model.R' 'nlinla.R' 'plotsample.R' 'prediction.R' 'rgl.R'
        'sampling.R' 'shapefile.R' 'spatstat.R' 'spde.R' 'stack.R'
        'track_plotting.R' 'transformation.R' 'utils.R'
VignetteBuilder: knitr
BuildVignettes: true
NeedsCompilation: no
Packaged: 2022-03-30 15:39:40 UTC; flindgre
Author: Finn Lindgren [aut, cre, cph] (<https://orcid.org/0000-0002-5833-2011>,
    Finn Lindgren continued development of the main code),
  Fabian E. Bachl [aut, cph] (Fabian Bachl wrote the main code),
  David L. Borchers [ctb, dtc, cph] (David Borchers wrote code for
    Gorilla data import and sampling, multiplot tool),
  Daniel Simpson [ctb, cph] (Daniel Simpson wrote the basic LGCP sampling
    method),
  Lindesay Scott-Howard [ctb, dtc, cph] (Lindesay Scott-Howard provided
    MRSea data import code),
  Seaton Andy [ctb] (Andy Seaton provided testing and bugfixes),
  Suen Man Ho [ctb, cph] (Man Ho Suen contributed features for aggregated
    responses)
Maintainer: Finn Lindgren <finn.lindgren@gmail.com>
Repository: CRAN
Date/Publication: 2022-03-30 16:20:05 UTC
Built: R 4.0.5; ; 2022-04-18 01:15:34 UTC; windows
