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ggpolypath package, and the ggplot2::fortify.SpatialPolygons/DataFrame() methods that were deprecated in ggplot2 version 3.4.4. Code using gg.SpatialPolygons() together with coord_fixed()/coord_equal() for coordinate axis control needs to use coord_sf() instead.bru_forward_transformation to allow bru_mapper_marginal to be applied with e.g. spatially varying parameters. (version 2.10.0.9001)terra version >= 1.7-66 that removes the need for detecting special cases (nrow(where) == 1 and terra::nlyr(data) == 1). Workaround code used for versions < 1.7-66. (version 2.10.0.9002) (Thanks to Robert J. Hijmans)ibm_simplify() generic to handle mapper simplification more generally; needed to properly support non-linear component mappers. (version 2.9.0.9004)bru_mapper_marginal() mapper class that can be used as part of component mapper pipelines. (version 2.9.0.9004)ibm_eval2() generic that computes both evaluation and Jacobian, avoiding double-computing of the Jacobian, when practical. (version 2.9.0.9005)bru_timings_plot() function that plots the time used for each nonlinear iteration (version 2.9.0.9007)bru_fill_missing() (by orders of magnitude) by changing method for finding the nearest available data point. (version 2.9.0.9011)bru_mapper_shift() mapper class that works like bru_mapper_scale() but for additive shifts instead of multiplicative scaling. (version 2.9.0.9012)2.9.0.9013)bru_mapper_matrix, previously used only for component model = "fixed", to allow integer indexing in addition to the previous factor/character-only indexing. (version 2.9.0.9014)is_linear flag wasn’t correctly set for bru_mapper_logsumexp mappers. Since previous versions did not accept non-linear component mappers, this is unlikely to have affected any user code. (Fixed in version 2.9.0.9001)2.9.0.9002 and 2.9.0.9006)NULL in automatic component usage detection. (version 2.9.0.9003)gorillas$plotsample$counts and gorillas_sf$plotsample$counts from +units=m to +units=km. (version 2.9.0.9010) The geometry information in counts is unlikely to have been used in examples or analysis code, as the problem would have been immediately obvious; plotting or other geometric operations that use the crs information would heve been completely wrong, and is only detected now that more code uses the crs information at all. Thanks to Dmytro Perepolkin for reporting in issue #205bru_fill_missing() for cases where the input data object also has missing values. (version 2.9.0.9011)eval_spatial() transform the where coordinates to the same crs as the input data, for SpatRaster and sf inputs, to allow different crs specifications. (version 2.9.0.9012)fmesher for mesh and geometry handling; the interface supports existing objects and methods. See https://inlabru-org.github.io/fmesher/articles/inla_conversion.html for more information.gg.sf() method.stars via eval_spatial(). (version 2.8.0.9007)sp package from ‘Depends’ to ‘Imports’. This means that user code should either use sp:: or library("sp") to access sp methods. The bru_safe_sp() helper function can be used to check for a safe sp package configuration during the transition from rgdal to sf, and is only needed if you may run on systems with sp installations older than “2.0-0” or with sp::get_evolution_status() < 2. (version 2.8.2011)Now preserves the previous log output when using bru_rerun(), and bru_log() is now a set of S3 methods, supporting extracting the full inlabru log as well bru-object specific logs (version 2.8.0.9008).
Note: From version 2.9.0, use bru_log() to access the global log, and bru_log(fit) to access a stored estimation log.
Up to version 2.8.0, bru_log() was a deprecated alias for bru_log_message(). When running on 2.8.0 or earlier, use bru_log_get() to access the global log, and cat(fit$bru_iinla$log, sep = "\n") to print a stored estimation object log.
SpatialPolygonsDataFrame were not automatically passed on to eval_spatial(). The logic has now changed so that any object with a eval_spatial() method will trigger a call to eval_spatial(). See ?input_eval for further information. (version 2.8.0.9001)fm_crs_is_null(), fm_transform() now supports oblique fm_crs CRS objects, and is.na() methods for the fm_crs and inla.CRS classes have been added. (version 2.8.0.9003)predict() by using quantile(..., names = FALSE). (version 2.8.0.9004)row_kron() code, causing speedups of a factor 2-30 in randomised test cases. (version 2.8.0.9005)sf method for eval_spatial(), causing failure when extracting from multiple layers in a single call. (version 2.8.0.9007)generate() and predict(). Now much faster for large models. (version 2.8.0.9009)*_latent form of a component. (version 2.8.0.9015)bru_fill_missing(). (version 2.8.0.9016, fixes #200)eval_SpatialDF removed, deprecated since 2.8.0. See eval_spatial instead.stransform, ibm_amatrix, ibm_valid_input removed, deprecated since 2.7.0. See fm_transform and ibm_jacobian instead.bru_mapper_offset, deprecated since 2.6.0 now returns a pure bru_mapper_const object, and all bru_mapper_offset ibm_* methods have been removed.init.tutorial removed, deprecated since 2.5.0generate.inla and predict.inla removed, deprecated since 2.1.0inla() optimisation criteria for the iterations (thanks to Haavard Rue), and a more relaxed nonlinear iteration stopping criterion; the default bru_method$rel_tol values has been changed from 1 to 10 percent change. The iterations are terminated when all latent and hyper-parameter mode changes fullfil |change|/SD < rel_tol, and the non-linear line search is inactive. This seems to strike a useful balance between the different optimisation criteria, allowing the iterations to converge faster and also detect that convergence sooner.The logic for which components are needed for a predictor expression (in like() or generate()/predict()) has been updated to when possible extract the list of components from the expression itself. The user can override this default if necessary, using the include/exclude arguments.
The bru_used() methods are used to guess the needed component names, applied to the right-hand side of the formula arguments. The allow_latent argument to like() has been deprecated in favour of include_latent (by default auto-detected for use of _latent and _eval).
bru_used() methods, that can also be used directly by the user and supplied via the used argument to like()/generate()/predict().fm_int() integration methods, replacing the old ipmaker() and ipoints() methods. Supports both sf and sp sampler objects.fm_pixels() methods for gridded points. The old pixels() method now calls fm_pixels(..., format = "sp")eval_spatial support for sf objects (for point-in-polygon data lookups)edge|int|ext.linewidth arguments to gg.inla.mesh #188predict() and generate() data arguments to newdata, for better compatibility with other predict() methods. The old argument name will still be accepted, but give a warning. Code that does not name the data argument is not affected.Note: Coordinate names for Spatial* objects have been inconsistently available in the predictor expression evaluation. However, due to how internal conversions might inadvertently change these names, they can not be relied on, and they are no longer being made available to the predictor expression. As a side effect, this change also speeds up some bru() runs by around a factor 2, since it avoids converting the Spatial* to a regular data.frame in time-sensitive core evaluation code.
If you need access to the raw coordinate values, use explicit calls to sp::coordinates(.data.) (e.g. for custom spatial covariate evaluation.). When possible, use the built-in covariate evaluation method, eval_spatial(), either implicitly with comp(covariate, ...) or explicitly, comp(eval_spatial(covariate, where = .data.), ...), that handles crs information correctly. Also consider transitioning from sp to sf data storage, using geometry instead of raw coordinates.
rgdal and maptools dependencies #178bru_safe_sp() to check if sp can be used safely (checks rgdal availability and sp evolution status, optionally forcing use of sf) #178rgl.* functions to *3d. Thanks to Duncan Murdoch #181ibm_jacobian.bru_mapper_harmonics for large modelssf::st_* calls that don’t account for the geos canonical representation being CW, whereas the canonical Simple Features representation being CCW. See https://github.com/r-spatial/sf/issues/2096sf and terra inputs to most methodsbru_mapper() systembru_convergence_plot()NA input for default 1D mappers to generate effect zero, like in inla().fm_crs(), fm_CRS(), fm_transform(), fm_ellipsoid_radius(), and fm_length_unit() to further support sf objects. The fm_crs() extraction method also supports terra objects.bru_fill_missing() now supports terra SpatRaster data and and sf locations.fm_evaluator() and fm_evaluate(), replacing the INLA inla.mesh.projector and inla.mesh.project methods.sf input to family="cp" models.Further bru_mapper() method updates;
ibm_amatrix() and names() methods, replaced by ibm_jacobian() and ibm_names().bru_mapper_pipe(), used to link mappers in sequence.bru_mapper_aggregate() and bru_mapper_logsumexp(), used for blockwise weighted sums and log-sum-exp mappings, output[k] = sum(weights[block==k]*state[block==k]))) and output[k] = log(sum(weights[block==k]*exp(state[block==k]))), with optional weight normalisation within each block. Allows providing the weights as log-weights, and uses block-wise shifts to avoid potential overflow.summary methods for bru_mapper objects (summary.bru_mapper())methods argument from bru_mapper_define(). Implementations should register S3 methods instead.spatstat.core dependency. Fixes #165ibm_eval.default() and ibm_eval.bru_mapper_collect() methods, where they would return zeros instead of the intended values. The main component evaluation and estimation code was not directly affected as that is based on the bru_mapper_multi() class methods that rely on the Jacobians instead. The bug would therefore mainly have impacted the future, not yet supported nonlinear mapper extensions.eval_spatial.SpatRaster; Work around inconsistent logic in terra::extract(..., layer) when length(layer)==1 or nrow(where)==1. Fixes #169indexed logical option to bru_mapper_factor(), to allow factor inputs to be mapped to index values, as needed for group and replicate. Fixes #174bru_get_mapper generic, and associated methods for inla.spde and inla.rgeneric objects. This allows inlabru to automatically extract the appropriate bru_mapper object for each model component, and can be used as a hook by external packages implementing new INLA object classes.weights argument for like(), for likelihood-specific log-likelihood weights, passed on to the INLA::inla() weights argument. Evaluated in the data context.<component>_eval() methods available in predictor expressions now handle optional scaling weights, like in ordinary component effect evaluation.terra support for covariate inputs*_layer arguments are now evaluated in the data context, to allow dynamic layer selection for spatial raster covariates. A new generic eval_spatial() provides support for grid/pixel based Spatial*DataFrame evaluation, and SpatRaster. Expanded support is in progress.bru_mapper system, component definitions, and prediction_scoresGeneral overhaul of the bru_mapper and linearised predictor system, to prepare for new features.
ibm_eval generic for evaluating mappers for given states.bru_mapper_taylor, used as an internal mapper for linearised mappers. This and ibm_eval is aimed at future support for nonlinear mappers. Associated new generic methods: ibm_{is_linear,jacobian,linear}.ibm_jacobian instead of ibm_amatrix. This allows defining a linearised mapper via ibm_eval(input, state0) + ibm_jacobian(input, state0) %*% (state - state0).bru_mapper_const, which replaces bru_mapper_offset. bru_mapper_offset is now deprecated and will produce warnings.epsg:4326. Fixes #154Tsparse assumptions in row_kron to prepare for Matrix 1.5-2. Fixes #162bru_mapper_harmonics mapper for cos and sin basis sets.predict() input data to be be a list.predict()cv, var, smin, smax summaries from predict()mean.mc_std_err and sd.mc_std_err output to predict()robins_subset data set and associated variable coefficient web vignettebru_mapper_collect models.inla.mode="classic" to use proper line search.unique method. Fixes #145strategy="gaussian" during iterations.bru() timing information in $bru_timings and $bru_iinla$timingsSpatialPolygonsDataFrame support to gg() methodsE and Ntrials from response_data and data (further special arguments remain to be added)deltaIC improvementsbru_{forward/inverse}_transformation()~ 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.evaluate_model() for cases where the inla_f argument mattersdata argument is now allowed to be a list(), and the new argument response_data allows separate specification of component inputs and response variables.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.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)SpatialPointsDataFrame and SpatialGridDataFrame input to bru_fill_missing()model = "offset" components instead of special options, to avoid interfering with the linearisation system (Issue #123)bru_method$stop_at_max_rel_deviation to bru_method$rel_tol. Automatic conversion to the new name, but a warning is given.bru_method$max_step to control the largest allowed line search scaling factor. See ?bru_optionsbru_compress_cp set to TRUE to compress the predictor expression for family="cp" to use a single element for the linear predictor sum.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(1) to avoid accidental confusion with other variables.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.?bru_options() for details.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.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)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.weights and copy features are now supported..data.allow_combine = TRUE argument must be supplied to like()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._latent to the component name, e.g. name_latent. For like(), this requires allow_latent = TRUE to activate the needed linearisation code for this._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.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.
ngroup and nrep in component definitionsmexdolphin and mrsea data sets, with consistent km units and improved mesh designspredict(..., include) discussion to distance sampling vignette, for handling non-spatial prediction in spatial models.gg.SpatialLinesSpatial* object handling and plottingpredict() logic for converting output to Spatial*DataFramecontrol.mode=list(restart=FALSE) in the final inla run for nonlinear models, to avoid an unnecessary optimisation.pixels() and bru_fill_missing() for Spatial*DataFrame objects with ncol=0 data frame parts.comp2(input, copy = "comp1")comp(input, weights, ...).data., allowing e.g. covar(fun(.data.), ...) for a complex covariate extractor method fun()bru_mapper objects"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.map= to main= or unnamed first argument; Since main is the first parameter, it doesn’t need to be a named argument.int.args option to control spatial integration resolution, thanks to Martin Jullum (martinju)VignetteBuilder entry from DESCRIPTIONint.polygon from integrating outside the mesh domain, and generally more robust integration scheme construction.bru() to like() parameter logic. (Thanks to Peter Vesk for bug example)NEWS.md file to track changes to the package.inla methods for predict() and generate() that convert inla output into bru objects before calling the bru prediction and posterior sample generator.sample.lgcp output formatting, extended CRS support, and more efficient sampling algorithmiinla() tracks convergence of both fixed and random effectsgg.matrix()These binaries (installable software) and packages are in development.
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