Add bru_mapper_harmonics mapper for cos
and sin basis sets.
Allow predict() input data to be be a list.
Allow arbitrary quantile summaries in
predict()
Remove cv, var, smin,
smax summaries from predict()
Add mean.mc_std_err and sd.mc_std_err
output to predict()
Add robins_subset data set and associated variable
coefficient web vignette
Propagate multi-likelihood A-matrix information instead of
recomputing. Fixes iteration issue for bym2 and other
bru_mapper_collect models.
Turn on predictor summaries during iterations to allow
inla.mode="classic" to use proper line search.
Avoid deprecated Matrix (>=1.4-2) class coercion methods
Work around for lack of full Matrix and ModelMatrix support for
the unique method. Fixes #145
More robust package checks
More robust namespace and INLA availability checks
Add package vignette with links to the website examples
Revert to R language features compatible with R 4.0.5
Use strategy="gaussian" during iterations.
Add bru() timing information in
$bru_timings and $bru_iinla$timings
Add SpatialPolygonsDataFrame support to
gg() methods
Allow accessing E and Ntrials from
response_data and data (further special
arguments remain to be added)
deltaIC improvements
New transformation helper tools
bru_{forward/inverse}_transformation()
Experimental support for matrix and formula component inputs.
E.g. with ~ name(~ -1 + a + b + a:b, model = "fixed"),
covariate fixed effect interaction specifications can be made. For
formula input, MatrixModels::model.Matrix() is called to
construct matrix input that is then used as the A-matrix for fixed
effects, one per column, added up to form the combined effect.
Documentation and examples improvements
Fix A-matrix construction for evaluate_model() for
cases where the inla_f argument matters
More efficient and robust mesh integration code
Cleanup of environment handling for component lists
Allow predictors to have different size than the input data. The
data argument is now allowed to be a list(),
and the new argument response_data allows separate
specification of component inputs and response variables.
Add bru_mapper_collect class for handling sequential
collections of mappers, including collections where all but the first
mapper is hidden from the INLA::f() arguments
n and values, as needed to support e.g. “bym2”
models.
Add control.family as a direct argument to
like(). Gives a warning if a control.family
argument is supplied to the the options argument of
bru(), but at least one likelihood has
control.family information. (Issue #109)
Fix support for SpatialPointsDataFrame and
SpatialGridDataFrame input to
bru_fill_missing()
Force explicit model = "offset" components instead
of special options, to avoid interfering with the linearisation system
(Issue #123)
Make the iterations more robust by resetting the internal INLA predictor states to initial value zero at each step
Rename the option
bru_method$stop_at_max_rel_deviation to
bru_method$rel_tol. Automatic conversion to the new name,
but a warning is given.
Add option bru_method$max_step to control the
largest allowed line search scaling factor. See
?bru_options
New default option bru_compress_cp set to
TRUE to compress the predictor expression for
family="cp" to use a single element for the linear
predictor sum.
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