Documentation and dependency updates for CRAN compatibility
See NEWS for version 2.3.0 for the major updates since version 2.1.13
The model component argument map has been deprecated. Use main to specify the main component input, ~ elev(main = elevation, model = "rw2"). Unlike the old map argument, main is the first one, so the shorter version ~ elev(elevation, model = "rw2") also works.
Intercept-like components should now have explicit inputs, e.g. ~ Intercept(1) to avoid accidental confusion with other variables.
The argument list for bru() has been simplified, so that all arguments except components and options must either be outputs from calls to like(), or arguments that can be sent to a single like() call.
The option setting system has been replaced with a more coherent system; see ?bru_options() for details.
The samplers and domain system for lgcp models is now stricter, and requires explicit domain definitions for all the point process dimensions. Alternatively, user-defined integration schemes can be supplied via the ips argument.
The model component input arguments main, group, replicate, and weights can now take general R expressions using the data inputs. Special cases are detected: SpatialPixels/GridDataFrame objects are evaluated at spatial locations if the input data is a SpatialPointsDataFrame object. Functions are evaluated on the data object, e.g. field(coordinates, model = spde)
The component arguments mapper, group_mapper, and replicate_mapper can be used for precise control of the mapping between inputs and latent variables. See ?bru_mapper for more details. Mapper information is automatically extracted from INLA::inla.spde2.pcmatern() model objects.
The R-INLA weights and copy features are now supported.
The predictor expressions can access the data object directly via .data.
If data from several rows can affect the same output row, the allow_combine = TRUE argument must be supplied to like()
The include and exclude arguments to like(), generate(), and predict() can be used to specify which components are used for a given likelihood model or predictor expression. This can be used to prevent evaluation of components that are invalid for a likelihood or predictor.
Predictor expressions can access the latent state of a model component directly, by adding the suffix _latent to the component name, e.g. name_latent. For like(), this requires allow_latent = TRUE to activate the needed linearisation code for this.
Predictor expressions can evaluate component effects for arbitrary inputs by adding the suffix _eval to access special evaluator functions, e.g. name_eval(1:10). This is useful for evaluating the 1D effect of spatial covariates. See the NEWS item for version 2.2.8 for further details.
The internal system for predictor linearisation and iterated INLA inference has been rewritten to be faster and more robust
See the NEWS entries for versions 2.1.14 to 2.2.8 for further details on new features and bug fixes
Add _eval suffix feature for generate.bru and predict.bru, that provides a general evaluator function for each component, allowing evaluation of e.g. nonlinear effects of spatial covariates as a function of the covariate value instead of the by the spatial evaluator used in the component definition. For example, with components = ~ covar(spatial_grid_df, model = "rw1"), the prediction expression can have ~ covar_eval(covariate), where covariate is a data column in the prediction data object.
For components with group and replicate features, these also need to be provided to the _eval function, with ..._eval(..., group = ..., replicate = ...)
This feature is built on top of the _latent suffix feature, that gives direct access to the latent state variables of a component, so in order to use _eval in the model predictor itself, you must use like(..., allow_latent = TRUE) in the model definition.
Add support for ngroup and nrep in component definitions
Updated mexdolphin and mrsea data sets, with consistent km units and improved mesh designs
Add predict(..., include) discussion to distance sampling vignette, for handling non-spatial prediction in spatial models.
Fix bugs in gg.SpatialLines
Vignette corrections
Documentation improvements
Fix minor bug in Spatial* object handling and plotting
Fixed issue with predict() logic for converting output to Spatial*DataFrame
Use control.mode=list(restart=FALSE) in the final inla run for nonlinear models, to avoid an unnecessary optimisation.
Fix issues in pixels() and bru_fill_missing() for Spatial*DataFrame objects with ncol=0 data frame parts.
Support for the INLA “copy” feature, comp2(input, copy = "comp1")
Allow component weights to be an unnamed parameter, comp(input, weights, ...)
Direct access to the data objects in component inputs and predictor expressions, as .data., allowing e.g. covar(fun(.data.), ...) for a complex covariate extractor method fun()
Partial support for spherical manifold meshes
Uses INLA integration strategy “eb” for initial nonlinear iterations, and a specified integration strategy only for the final iteration, so that the computations are faster, and uses the conditional latent mode as linearisation point.
New options system
New faster linearisation method
New line search method to make the nonlinear inla iterations robust
Method for updating old stored estimation objects
System for supplying mappings between latent models and evaluated effects via bru_mapper objects
Improved factor support; Either as “contrast with the 1st level”, via the special "factor_contrast" model, or all levels with model "factor_full". Further options planned (e.g. a simpler options to fix the precision parameter). The estimated coefficients appear as random effects in the inla() output.
Interface restructuring to support new features while keeping most backwards compatibility. Change map= to main= or unnamed first argument; Since main is the first parameter, it doesn’t need to be a named argument.
Keep components with zero derivative in the linearisation
PROJ6 support
Add random seed option for posterior sampling
Add package unit testing
New backend code to make extended feature support easier
New int.args option to control spatial integration resolution, thanks to Martin Jullum (martinju)
VignetteBuilder entry from DESCRIPTIONUpdate default options
Prevent int.polygon from integrating outside the mesh domain, and generally more robust integration scheme construction.
Fix bru() to like() parameter logic. (Thanks to Peter Vesk for bug example)
Added a NEWS.md file to track changes to the package.
Added inla methods for predict() and generate() that convert inla output into bru objects before calling the bru prediction and posterior sample generator.
Added protection for examples requiring optional packages
Fix sample.lgcp output formatting, extended CRS support, and more efficient sampling algorithm
Avoid dense matrices for effect mapping
iinla() tracks convergence of both fixed and random effectsAdded matrix geom gg.matrix()
Fixed CRAN test issues