Version 2.5.2

Minor bugfix release

Version 2.5.1

Version 2.5.1, fixing support for R4.0.5

Version 2.5.0

Feature and bugfix release

Version 2.4.0

Bugfix and minor feature release. See NEWS for details.

inlabru 2.3.1

• Documentation and dependency updates for CRAN compatibility

• See NEWS for version 2.3.0 for the major updates since version 2.1.13

inlabru 2.3.0

Breaking changes 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.

New features since version 2.1.13

• 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

Version 2.2.8

Feature release

Version 2.2.7

Bugfix and feature release

Version 2.2.6

Bugfix release. See NEWS for details.

Version 2.2.5

Bugfix release. See NEWS for details.