Bayesian Latent Gaussian Modelling using INLA and Extensions
Build an inla data stack from linearisation information
Mapper for cos/sin functions
Compute simplified component mappings
Geom for predictions
Plot a map using extent of a spatial object
Subsetting of comp_simple_list objects, retaining class
Generate samples from fitted bru models
Extract timing information from fitted bru object
Add component input/latent mappers
1D LGCP bin count simulation and comparison with data
Methods for mapper lists
Additional bru options
Compute inlabru model linearisation information
Plot inlabru convergence diagnostics
Get access to the internal environment
Utility functions for bru likelihood objects
Fill in missing values in Spatial grids
Extract mapper information from INLA model component objects
Methods for bru_info objects
Backwards compatibility to handle mexpand for INLA <= 24.06.02
Join stacks intended to be run with different likelihoods
Integration points for polygons inside an inla.mesh
Generic methods for bru_mapper objects
Methods for bru_log bookmarks
Add a log message
Create a bru_log object
Position methods for bru_log objects
Clear log contents
Access methods for bru_log objects
Mapper for aggregation
Mapper for concatenated variables
Constant mapper
Mapper for factor variables
Mapper for fm_mesh_1d
Mapper for fm_mesh_2d
Mapper for indexed variables
Mapper for a linear effect
Mapper for log-sum-exp aggregation
Mapper for marginal distribution transformation
Mapper for matrix multiplication
Mapper for basis conversion
Mapper for tensor product domains
Mapper for linking several mappers in sequence
Mapper for element-wise scaling
Mapper for element-wise shifting
mapper object summaries
Mapper for linear Taylor approximations
Constructors for bru_mapper objects
Create an inlabru model object from model components
Create or update an options objects
Load INLA safely for examples and tests
Check for potential sp version compatibility issues
Standardise inla hyperparameter names
Summarise and annotate data
Plot inlabru iteration timings
Transformation tools
Update used_component information objects
Extract basic variable names from expression
List components used in a model
Convenient model fitting using (iterated) INLA
Convert components to R code
Construct component linearisations
Compute all component linearisations
Evaluate component values in predictor expressions
Methods for inlabru component lists
Latent model component construction
Summarise DIC and WAIC from lgcp objects.
Variance and correlations measures for prediction components
Evaluate expressions in the data context
Evaluate spatial covariates
Evaluate a component effect
Compute all index values
Compute all component inputs
Evaluate or sample from a posterior result given a model and locations
Evaluate component effects or expressions
Expand labels
Extract a summary property from all results of an inla result
Geom for data.frame
Geom for fm_mesh_1d objects
Geom for inla.mesh objects
Geom for matrix
Geom for RasterLayer objects
ggplot2 geomes for inlabru related objects
Geom helper for sf objects
Geom for SpatialGridDataFrame objects
Geom for SpatialLines objects
Geom for SpatialPixels objects
Geom for SpatialPixelsDataFrame objects
Geom for SpatialPoints objects
Geom for SpatialPolygons objects
Geom wrapper for SpatRaster objects
Visualize a globe using RGL
Render objects using RGL
ggplot geom for spatial data
Iterated INLA
Obtain indices
Obtain inla index subset information
Deprecated functions in inlabru
inlabru
Obtain component inputs
Log Gaussian Cox process (LGCP) inference using INLA
Observation model construction for usage with bru()
Row-wise Kronecker products
Unit test helpers
Matern correlation or covariance function approximate credible bands.
Multiple ggplots on a page.
Parse inclusion of component labels in a predictor expression
Make hierarchical mesh basis functions
Plot prediction using ggplot2
Sample from an inhomogeneous Poisson process
Plot method for posterior marginals estimated by bru
Create a plot sample.
Convert a plot sample of points into one of counts.
Prediction from fitted bru model
Objects exported from other packages
Print inlabru options
Seal pups
Convert data frame to SpatialLinesDataFrame
Convert SpatialPoints and boundary polygon to spatstat ppp object
Posteriors of SPDE hyper parameters and Matern correlation or covarian...
Convert a data.frame of boundary points into a SpatialPolgonsDataFrame
Summary for an inlabru fit
Summarise components
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>.
Useful links