Bayesian Latent Gaussian Modelling using INLA and Extensions
Add component input/latent mappers
Conversion methods for bru_comp and bru_comp_list objects
Methods for mapper extraction
Conversion methods for bru_obs and bru_obs_list objects
1D LGCP bin count simulation and comparison with data
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 general fmesher function space objects
Mapper for cos/sin functions
Mapper for indexed variables
Mapper for a linear effect
Methods for mapper lists
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
Re-indexing matrix for repeated mappers
Mapper for repeating a mapper
Mapper for element-wise scaling
Mapper for element-wise shifting
Mapper for adding multiple mappers
mapper object summaries
Mapper for linear Taylor approximations
Additional bru options
Evaluate component values in predictor expressions
Methods for inlabru component lists
Latent model component construction
Compute inlabru model linearisation information
Plot inlabru convergence diagnostics
Get access to the internal environment
Evaluate expressions in data contexts
Fill in missing values in Spatial grids
Convert components to R code
Extract mapper information from INLA model component objects
Extract predictor or component index information
Methods for bru_info objects
Backwards compatibility to handle mexpand for INLA <= 24.06.02
Join stacks intended to be run with different likelihoods
Obtain component inputs
Check for predictor expression additivity
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
Build an inla data stack from linearisation information
Generic methods for bru_mapper objects
Constructors for bru_mapper objects
Mapper methods for model objects
Create an inlabru model object from model components
Utility functions for bru observation model objects
Summary and print methods for observation models
Observation model construction for usage with bru()
Create or update an options objects
Response size queries
Load INLA safely for examples and tests
Check for potential sp version compatibility issues
Set missing values in observation models
Standardise inla hyperparameter names
Summarise and annotate data
Plot inlabru iteration timings
Extract timing information from fitted bru object
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
Summarise DIC and WAIC from lgcp objects.
Variance and correlations measures for prediction components
Evaluate spatial covariates
Evaluate a component effect
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
Generate samples from fitted bru models
Geom for predictions
Geom for data.frame
Geom for fm_mesh_1d objects
Geom for fm_mesh_2d 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
Deprecated alias for sp version of the gorillas dataset
Iterated INLA
Obtain inla index subset information
Deprecated functions in inlabru
inlabru
Log Gaussian Cox process (LGCP) inference using INLA
Unit test helpers
Matern correlation or covariance function approximate credible bands.
Deprecated alias for sp version of the mexdolphin dataset
Multiple ggplots on a page.
Parse inclusion of component labels in a predictor expression
Make hierarchical mesh basis functions
Plot prediction using ggplot2
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
Sample from an inhomogeneous Poisson process
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
Summarise components
Summarise component inputs
Print inlabru options
Summary for an inlabru fit
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