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DESCRIPTION
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Package: inlabru
Type: Package
Title: Bayesian Latent Gaussian Modelling using INLA and Extensions
Version: 2.12.0.9002
Authors@R: c(
person("Finn", "Lindgren", email = "[email protected]",
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 = "[email protected]",
role = c("aut", "cph"),
comment = "Fabian Bachl wrote the main code"),
person("David L.", "Borchers", email = "[email protected]",
role = c("ctb", "dtc", "cph"),
comment = "David Borchers wrote code for Gorilla data import and sampling, multiplot tool"),
person("Daniel", "Simpson", email = "[email protected]",
role = c("ctb", "cph"),
comment = "Daniel Simpson wrote the basic LGCP sampling method"),
person("Lindesay", "Scott-Howard", email = "[email protected]",
role = c("ctb", "dtc", "cph"),
comment = "Lindesay Scott-Howard provided MRSea data import code"),
person("Seaton", "Andy", email = "[email protected]", role = c("ctb"),
comment = "Andy Seaton provided testing, bugfixes, and vignettes"),
person("Suen", "Man Ho", email = "[email protected]", role = c("ctb", "cph"),
comment = "Man Ho Suen contributed features for aggregated responses and vignette updates"),
person("Roudier", "Pierre", email = "", role = c("ctb", "cph"),
comment = "Pierre Roudier contributed general quantile summaries"),
person("Meehan", "Tim", email = "[email protected]", role = c("ctb", "cph"),
comment = "Tim Meehan contributed the SVC vignette and robins data"),
person("Reddy Peddinenikalva", "Niharika", role = c("ctb", "cph"),
comment = "Niharika Peddinenikalva contributed the LGCP residuals vignette"),
person("Perepolkin", "Dmytro", email = "[email protected]",
role = c("ctb", "cph"),
comment = "Dmytro Perepolkin contributed the ZIP/ZAP vignette")
)
URL: http://www.inlabru.org, https://inlabru-org.github.io/inlabru/, https://github.com/inlabru-org/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: 7.3.2
Encoding: UTF-8
Depends:
fmesher (>= 0.2.0),
methods,
R (>= 3.6),
stats
Imports:
dplyr,
lifecycle,
magrittr,
MatrixModels,
Matrix,
plyr,
rlang,
sf,
tibble,
utils,
withr
Suggests:
covr,
ggplot2,
graphics,
INLA (>= 23.01.31),
knitr,
maps,
mgcv,
patchwork,
raster,
RColorBrewer,
rgl,
rmarkdown,
scales,
shiny,
sn,
sp (>= 2.1),
spatstat.geom,
spatstat.data,
sphereplot,
splancs,
terra,
tidyterra,
testthat,
tidyr,
DiagrammeR
Enhances:
stars
Roxygen: list(markdown = TRUE)
Config/testthat/parallel: true
Config/testthat/edition: 3
Collate:
'0_inlabru_envir.R'
'bru.gof.R'
'bru.inference.R'
'bru_sp.R'
'data.Poisson1_1D.R'
'data.Poisson2_1D.R'
'data.Poisson3_1D.R'
'data.gorillas.R'
'data.mexdolphin.R'
'data.mrsea.R'
'data.robins_subset.R'
'data.shrimp.R'
'data.toygroups.R'
'data.toypoints.R'
'deltaIC.R'
'deprecated.R'
'effect.R'
'environment.R'
'fmesher.R'
'ggplot.R'
'inla.R'
'inlabru-package.R'
'local_testthat.R'
'mappers.R'
'mapper_collect.R'
'mapper_repeat.R'
'mapper_sum.R'
'model.R'
'nlinla.R'
'plotsample.R'
'rgl.R'
'sampling.R'
'spatstat.R'
'spde.R'
'stack.R'
'track_plotting.R'
'transformation.R'
'used.R'
'utils.R'
VignetteBuilder: knitr
BuildVignettes: true
LazyData: true
LazyDataCompression: xz