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inlabru (development version)

  • Remove rgdal and maptools dependencies #178

  • Add bru_safe_sp() to check if sp can be used safely (checks rgdal availability and sp evolution status, optionally forcing use of sf) #178

  • Remove PROJ4 support #178

  • Warning: Coordinate names for Spatial* objects have been inconsistently available in the predictor expression evaluation. Avoid relying on those being present, and use explicit calls to coordinates(.data.) if you need the coordinate values (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.), ...).

  • Change rgl.* functions to *3d. Thanks to Duncan Murdoch #181

  • Speed up ibm_jacobian.bru_mapper_harmonics for large models

  • eval_spatial supported to sf objects (for point-in-polygon data lookups)

  • Add workarounds for inconsistent polygon orientation resulting from sf::st_* calls that don’t account for the geos canonical representation being CW, whereas the canonical Simple Features representation being CCW. See

  • Allow precomputed spatial covariates in the data for point process observations

  • Add edge|int|ext.linewidth arguments to gg.inla.mesh #188

  • Rename the predict() 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.

  • Add fm_int() integration methods, replacing the old ipmaker() and ipoints() methods. Supports sf sampler objects.

  • Add fm_pixels() methods for gridded points. pixels() calls fm_pixels(..., format = "sp")

inlabru 2.7.0

CRAN release: 2022-12-02

Feature overview

  • Added support for sf and terra inputs to most methods

  • Expanded geometry and mesh handling methods

  • Expanded bru_mapper() system

  • Added convergence diagnostics plot with bru_convergence_plot()

Feature details

Bug fixes

  • Remove unused spatstat.core dependency. Fixes #165

  • Fixed issue with plain mapper evaluation in the ibm_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.

  • Fix for eval_spatial.SpatRaster; Work around inconsistent logic in terra::extract(..., layer) when length(layer)==1 or nrow(where)==1. Fixes #169

    • Add indexed logical option to bru_mapper_factor(), to allow factor inputs to be mapped to index values, as needed for group and replicate. Fixes #174

inlabru 2.6.0

CRAN release: 2022-10-24


  • Add bru_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.

  • Add a weights argument for like(), for likelihood-specific log-likelihood weights, passed on to the INLA::inla() weights argument. Evaluated in the data context.

  • The <component>_eval() methods available in predictor expressions now handle optional scaling weights, like in ordinary component effect evaluation.

  • Add terra support for covariate inputs

  • The component *_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.

  • New vignettes on the bru_mapper system, component definitions, and prediction_scores

  • General overhaul of the bru_mapper and linearised predictor system, to prepare for new features.

    • Add ibm_eval generic for evaluating mappers for given states.

    • Add 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}.

    • New mapper implementations should use ibm_jacobian instead of ibm_amatrix. This allows defining a linearised mapper via ibm_eval(input, state0) + ibm_jacobian(input, state0) %*% (state - state0).

    • New mapper class bru_mapper_const, which replaces bru_mapper_offset. bru_mapper_offset is now deprecated and will produce warnings.

Bug fixes

  • Enable both datum/ensemble container for ellipsoid information, to support epsg:4326. Fixes #154

  • Make duplicated component names an error instead of a warning. Relates to #155

  • Fix Tsparse assumptions in row_kron to prepare for Matrix 1.5-2. Fixes #162

inlabru 2.5.3

CRAN release: 2022-09-05


  • 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

Bug fixes

  • 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

inlabru 2.5.2

CRAN release: 2022-03-30

  • More robust package checks

  • More robust namespace and INLA availability checks

  • Add package vignette with links to the website examples

inlabru 2.5.1

  • Revert to R language features compatible with R 4.0.5

  • Use strategy="gaussian" during iterations.

inlabru 2.5.0

CRAN release: 2022-03-21


  • 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

Bug fixes

  • 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

inlabru 2.4.0

CRAN release: 2021-12-19


  • 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 as a direct argument to like(). Gives a warning if a argument is supplied to the the options argument of bru(), but at least one likelihood has 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.

inlabru 2.3.1

CRAN release: 2021-03-22

  • 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

CRAN release: 2021-03-16

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

inlabru 2.2.8

  • 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.

inlabru 2.2.7

  • Add support for ngroup and nrep in component definitions

  • Updated mexdolphin and mrsea data sets, with consistent km units and improved mesh designs

inlabru 2.2.6

  • Add predict(..., include) discussion to distance sampling vignette, for handling non-spatial prediction in spatial models.

  • Fix bugs in gg.SpatialLines

inlabru 2.2.5

  • Vignette corrections

  • Documentation improvements

  • Fix minor bug in Spatial* object handling and plotting

inlabru 2.2.4

  • Properly extract the joint latent conditional mode instead of the marginal latent conditional mode

inlabru 2.2.2

  • 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.

inlabru 2.2.1

  • Fixed code regression bug for function input of covariates

inlabru 2.2.0

  • 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.

inlabru 2.1.15

  • 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)

inlabru 2.1.13

CRAN release: 2020-02-16

  • Fix CRAN complaint regarding documentation

inlabru 2.1.12

CRAN release: 2019-06-24

  • Workaround an integration points error for old (ca pre-2018) INLA versions

inlabru 2.1.11

  • Add CITATION file

inlabru 2.1.10

  • Fix internal sampling bug due to INLA changes

inlabru 2.1.9

CRAN release: 2018-07-24

  • Remove unused VignetteBuilder entry from DESCRIPTION

inlabru 2.1.8

  • Update 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)

inlabru 2.1.7

  • Added a 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

inlabru 2.1.4

  • iinla() tracks convergence of both fixed and random effects

inlabru 2.1.3

CRAN release: 2018-02-11