Reference

All functions
as.inla.mesh.segment inla.sp2segment	Convert sp curve objects to inla.mesh.segment objects.
BivMetaAnalysis	Data are taken from a meta-analysis to compare the utility of three types of diagnostic imaging - lymphangiography (LAG), computed tomography (CT) and magnetic resonance (MR) - to detect lymph node metastases in patients with cervical cancer. The dataset consists of a total of 46 studies: the first 17 for LAG, the following 19 for CT and the last 10 for MR.
Cancer	~~ A concise (1-5 lines) description of the dataset. ~~
control.bgev.default inla.set.control.bgev.default.default	Control variables in control.bgev.default for use in inla
control.compute inla.set.control.compute.default	Control variables in control.compute for use in inla
control.expert inla.set.control.expert.default	Control variables in control.expert for use in inla
control.family inla.set.control.family.default	Control variables in control.family for use in inla
control.fixed inla.set.control.fixed.default	Control variables in control.fixed for use in inla
control.group inla.set.control.group.default	Control variables in control.group for use in inla
control.hazard inla.set.control.hazard.default	Control variables in control.hazard for use in inla
control.inla inla.set.control.inla.default	Control variables in control.inla for use in inla
control.lincomb inla.set.control.lincomb.default	Control variables in control.lincomb for use in inla
control.link inla.set.control.link.default	Control variables in control.link for use in inla
control.mix inla.set.control.mix.default	Control variables in control.mix for use in inla
control.mode inla.set.control.mode.default	Control variables in control.mode for use in inla
control.predictor inla.set.control.predictor.default	Control variables in control.predictor for use in inla
control.results inla.set.control.results.default	Control variables in control.results for use in inla
control.update inla.set.control.update.default	Control variables in control.update for use in inla
cut inla.cut	This function performs group-wise, cross-validatory model assessment for an INLA model using so-called node-splitting (Marshall and Spiegelhalter, 2007; Presanis et al, 2013). The user inputs an object of class inla (i.e. a result of a call to inla() ) as well as a variable name ( split.by ) specifying a grouping: Data points that share the same value of split.by are in the same group. The function then checks whether each group is an "outlier", or in conflict with the remaining groups, using the methodology described in Ferkingstad et al (2017). The result is a vector containing a p-value for each group, corresponding to a test for each group i , where the null hypothesis is that group i is consistent with the other groups except i (so a small p-value is evidence that the group is an "outlier"). See Ferkingstad et al (2017) for further details.
debug.graph inla.debug.graph	Debug a graph specification on file (ascii-mode only), by checking the specification along the way.
Drivers	Montly total of car drivers killed or several injuried in England from January 1969 to December 1984 NB: The last 12 lines of the data set have the first column set to NULL since these data where not observed but we want to predict them.
Epil	Seizure counts in a randomised trial of anti-convulsant therpay in epilepsy for 59 patients.
extract.groups	Extract boundary or internal segments tagged by group id:s.
f inla.set.control.group.default list c inla.set.control.fixed.default exp	Function used for defining of smooth and spatial terms within inla model formulae. The function does not evaluate anything - it exists purely to help set up a model. The function specifies one smooth function in the linear predictor (see inla.list.models ) as $$w\ f(x)$$
fgn inla.fgn	This function will return the coefficients in the 3-component AR(1) mixture representing FGN(H)
geobugs2inla inla.geobugs2inla	Various utility functions for INLA
geobugs2inla inla.geobugs2inla	Various utility functions for INLA
Germany	Cases of Oral cavity cancer in Germany from 1986-1990
graph2matrix inla.graph2matrix inla.spy	Construct a neighbour-matrix from a graph and disaply it
graph2matrix inla.graph2matrix inla.spy	Construct a neighbour-matrix from a graph and disaply it
idx inla.idx max rep length	Convert indexes given by to triplet `(idx, group, replicate)' to the (one-dimensional) index used in the grouped and replicated model
inla c list inla.getOption parent.frame	inla performs a full Bayesian analysis of additive models using Integrated Nested Laplace approximation
inla-class	The inla class is defined in the INLA package
INLA-package	Package to perform full Bayesian analysis on generalised additive mixed models using Integrated Nested Laplace Approximations.
inla.ar inla.ar.pacf2phi inla.ar.phi2pacf inla.ar.pacf2acf length inla.ar.phi2acf	These functions convert between the AR(p) coefficients phi , the partial autorcorrelation coefficients pacf and the autocorrelation function acf . The phi -parameterization is the same as used for arima -models in R ; see ?arima and the parameter-vector a in Details .
inla.ar inla.ar.pacf2phi inla.ar.phi2pacf inla.ar.pacf2acf length inla.ar.phi2acf	These functions convert between the AR(p) coefficients phi , the partial autorcorrelation coefficients pacf and the autocorrelation function acf . The phi -parameterization is the same as used for arima -models in R ; see ?arima and the parameter-vector a in Details .
inla.as.sparse inla.as.dgTMatrix	Convert a matrix or sparse matrix into the sparse format used by INLA (dgTMatrix)
inla.as.sparse inla.as.dgTMatrix	Convert a matrix or sparse matrix into the sparse format used by INLA (dgTMatrix)
inla.as.wkt_tree.wkt inla.as.wkt.wkt_tree inla.wkt_tree_get_item inla.wkt_tree_set_item	Conversion between WKT and a tree representation
inla.barrier inla.barrier.pcmatern inla.barrier.polygon inla.barrier.q inla.barrier.fem	Functions for defining Barrier models as an inla rgeneric model
inla.barrier inla.barrier.pcmatern inla.barrier.polygon inla.barrier.q inla.barrier.fem	Functions for defining Barrier models as an inla rgeneric model
inla.binary.install	Install alternative binary builds.
inla.binary.install	Install alternative binary builds.
inla.changelog	List the recent changes in the inla-program and its R-interface
inla.changelog	List the recent changes in the inla-program and its R-interface
inla.collect.results inla.set.control.results.default	inla.collect.results collect results from a inla-call
inla.collect.results inla.set.control.results.default	inla.collect.results collect results from a inla-call
inla.compare.results	A small utility to compare INLA and MCMC results (OBSOLETE)
inla.compare.results	A small utility to compare INLA and MCMC results (OBSOLETE)
inla.coxph list inla.rbind.data.frames	Tools to convert a Cox proportional hazard model into Poisson regression
inla.coxph list inla.rbind.data.frames	Tools to convert a Cox proportional hazard model into Poisson regression
inla.cpo	Improve the estimates of the CPO/PIT-values be recomputing the model-fit by removing data-points.
inla.cpo	Improve the estimates of the CPO/PIT-values be recomputing the model-fit by removing data-points.
inla.CRS inla.wkt_predef	Creates either a CRS object or an inla.CRS object, describing a coordinate reference system
inla.CRSargs	Wrapper for sp::CRS and inla.CRS objects to extract the coordinate reference system argument string. Should no longer be used with PROJ6/rgdal3; see inla.crs_get_wkt
inla.dBind	Build a block-diagonal sparse matrix. Obsolete wrapper for bdiag() .
inla.dev.new	Open a new device using dev.new unless using RStudio
inla.dev.new	Open a new device using dev.new unless using RStudio
inla.diameter	Find an upper bound to the convex hull of a point set
inla.doc	View documentation of latent, prior and likelihood models.
inla.doc	View documentation of latent, prior and likelihood models.
inla.extract.el	Extract elements by wildcard name matching from a data.frame , list , or matrix .
inla.fmesher.smorg	Low level function for computing finite element matrices, spherical harmonics, B-splines, and point mappings with barycentric triangle coordinates.
inla.generate.colors	Generates a tex RGB color specification matrix based on a color palette.
inla.group c	inla.group group or cluster covariates so to reduce the number of unique values
inla.group c	inla.group group or cluster covariates so to reduce the number of unique values
inla.has_PROJ6 inla.not_for_PROJ6 inla.not_for_PROJ4 inla.fallback_PROJ6 inla.requires_PROJ6	Detect whether PROJ6 is available for INLA
inla.hyperpar	Improve the estimates of the posterior marginals for the hyperparameters of the model using the grid integration strategy.
inla.hyperpar	Improve the estimates of the posterior marginals for the hyperparameters of the model using the grid integration strategy.
inla.hyperpar.sample	Produce samples from the approximated joint posterior for the hyperparameters
inla.hyperpar.sample	Produce samples from the approximated joint posterior for the hyperparameters
inla.identical.CRS	Wrapper for identical, optionally testing only the CRS part of two objects
inla.knmodels list c paste paste0	It implements the models in Knorr-Held, L. (2000) with three different constraint approaches: sum-to-zero, contrast or diagonal add.
inla.knmodels list c paste paste0	It implements the models in Knorr-Held, L. (2000) with three different constraint approaches: sum-to-zero, contrast or diagonal add.
inla.knmodels.sample	It implements the sampling method for the models in Knorr-Held, L. (2000) considering the algorithm 3.1 in Rue & Held (2005) book.
inla.knmodels.sample	It implements the sampling method for the models in Knorr-Held, L. (2000) considering the algorithm 3.1 in Rue & Held (2005) book.
inla.ks.plot	Illustrate a one-sample Kolmogorov-Smirnov test by plotting the empirical distribution deviation.
inla.ks.plot	Illustrate a one-sample Kolmogorov-Smirnov test by plotting the empirical distribution deviation.
inla.list.models names inla.models	List available model components, likelihoods, priors, etc. To read specific documentation for the individual elements, use inla.doc .
inla.list.models names inla.models	List available model components, likelihoods, priors, etc. To read specific documentation for the individual elements, use inla.doc .
inla.load	Load or source a file: (internal use)
inla.load	Load or source a file: (internal use)
inla.matern.cov inla.matern.cov.s2	Calculates covariance and correlation functions for Matern models and related oscillating SPDE models, on \(R^d\) and on the sphere, \(S^2\).
inla.mdata is.inla.mdata as.inla.mdata	This defines an mdata-object for matrix valued response-families
inla.mdata is.inla.mdata as.inla.mdata	This defines an mdata-object for matrix valued response-families
inla.merge merge rep length	Merge a mixture of inla -objects
inla.merge merge rep length	Merge a mixture of inla -objects
inla.mesh.1d range	Create a 1D mesh specification inla.mesh.1d object, that defines a function space for 1D SPDE models.
inla.mesh.1d.A c inla.mesh.1d.bary	Calculates barycentric coordinates and weight matrices for inla.mesh.1d objects.
inla.mesh.2d	Create a triangle mesh based on initial point locations, specified or automatic boundaries, and mesh quality parameters.
inla.mesh.assessment	Assess the finite element approximation errors in a mesh for interactive R sessions. More detailed assessment tools are in meshbuilder .
inla.mesh.assessment	Assess the finite element approximation errors in a mesh for interactive R sessions. More detailed assessment tools are in meshbuilder .
inla.mesh.basis	Calculate basis functions on a 1d or 2d inla.mesh
inla.mesh.boundary inla.mesh.interior	Constructs an list of inla.mesh.segment object from boundary or interior constraint information in an inla.mesh object.
inla.mesh.components	Compute subsets of vertices and triangles in an inla.mesh object that are connected by edges.
inla.mesh.components	Compute subsets of vertices and triangles in an inla.mesh object that are connected by edges.
inla.mesh.create missing is.null inla.delaunay	Create a constrained refined Delaunay triangulation (CRDT) for a set of spatial locations.
inla.mesh.create.helper	Create a triangle mesh based on initial point locations, specified or automatic boundaries, and mesh quality parameters.
inla.mesh.deriv	Calculates directional derivative matrices for functions on inla.mesh objects.
inla.mesh.fem inla.mesh.1d.fem	Constructs finite element matrices for inla.mesh and inla.mesh.1d objects.
inla.mesh.lattice seq if is.matrix dim c length	Construct a lattice grid for inla.mesh
inla.mesh.map c inla.mesh.map.lim	Calculates coordinate mappings for inla.mesh projections.
inla.mesh.project inla.mesh.projector c	Calculate a lattice projection to/from an inla.mesh
inla.mesh.query	Query information about an inla.mesh object.
inla.mesh.segment inla.contour.segment seq nrow ncol pretty range seq_len length	Constructs inla.mesh.segment objects that can be used to specify boundary and interior constraint edges in calls to inla.mesh .
inla.models	This page describe the models implemented in inla , divided into sections: latent, group, mix, link, predictor, hazard, likelihood, prior, wrapper .
inla.nmix.lambda.fitted	For use with 'nmix' and 'nmixnb' models. This function takes the information contained in an object returned by inla() and uses the contents to create fitted lambda values using the linear predictor for log(lambda), the input covariate values, and samples from the posteriors of the model hyperparameters. Fitted values from the linear predictor are exponentiated, by default, before being returned.
inla.nmix.lambda.fitted	For use with 'nmix' and 'nmixnb' models. This function takes the information contained in an object returned by inla() and uses the contents to create fitted lambda values using the linear predictor for log(lambda), the input covariate values, and samples from the posteriors of the model hyperparameters. Fitted values from the linear predictor are exponentiated, by default, before being returned.
inla.nonconvex.hull inla.nonconvex.hull.basic	Constructs a nonconvex boundary for a point set using morphological operations.
inla.option inla.setOption inla.getOption	Set and get global options for INLA
inla.option inla.setOption inla.getOption	Set and get global options for INLA
inla.over_sp_mesh c	Wrapper for the over method to find triangle centroids or vertices inside sp polygon objects
inla.pardiso inla.pardiso.check	Describe and check the PARDISO support in R-INLA
inla.pardiso inla.pardiso.check	Describe and check the PARDISO support in R-INLA
inla.priors.used	Print the priors used for the hyperparameters
inla.priors.used	Print the priors used for the hyperparameters
inla.prune	Prune the INLA-package by deleting binary files not supported by the running OS
inla.prune	Prune the INLA-package by deleting binary files not supported by the running OS
inla.qstat inla.qget inla.qdel inla.qlog inla.qnuke summary print	Control and view a remote inla-queue of submitted jobs
inla.qstat inla.qget inla.qdel inla.qlog inla.qnuke summary print	Control and view a remote inla-queue of submitted jobs
inla.reorderings	Provide the names of all implemented reordering schemes
inla.reorderings	Provide the names of all implemented reordering schemes
inla.rerun	Rerun inla on an inla-object (output from link{inla} )
inla.rerun	Rerun inla on an inla-object (output from link{inla} )
inla.row.kron	Takes two Matrices and computes the row-wise Kronecker product. Optionally applies row-wise weights and/or applies an additional 0/1 row-wise Kronecker matrix product, as needed by inla.spde.make.A .
inla.rw inla.rw1 inla.rw2	Constructs precision matrices for first- and second-order random walk (RW1/RW2) latent models used within INLA .
inla.sample inla.posterior.sample list inla.posterior.sample.eval	This function generate samples, and functions of those, from an approximated posterior of a fitted model (an inla-object)
inla.sample inla.posterior.sample list inla.posterior.sample.eval	This function generate samples, and functions of those, from an approximated posterior of a fitted model (an inla-object)
inla.sens	TODO
inla.sens	TODO
inla.simplify.curve	Attempts to simplify a polygonal curve by joining nearly colinear segments.
inla.sp_get_crs	Wrapper for CRS(projargs) (PROJ4) and CRS(wkt) for sp::Spatial objects.
inla.spde.make.A c	Constructs observation/prediction weight matrices for models based on inla.mesh and inla.mesh.1d objects.
inla.spde.make.block.A max c	Constructs observation/prediction weight matrices for numerical integration schemes for regional data problems. Primarily intended for internal use by inla.spde.make.A .
inla.spde.make.index	Generates a list of named index vectors for an SPDE model.
inla.spde.models inla.spde1.models inla.spde2.models	List SPDE models supported by inla.spde objects
inla.spde.precision c inla.spde2.theta2phi0 inla.spde2.theta2phi1 inla.spde2.theta2phi2 inla.spde2.precision inla.spde1.precision	Calculates the precision matrix for given parameter values based on an inla.spde model object.
inla.spde.result inla.spde1.result inla.spde2.result	Exctract field and parameter values and distributions for an inla.spde SPDE effect from an inla result object.
inla.spde.sample	Old methods fo sampling from a SPDE model. For new code, use inla.spde.precision and inla.qsample instead.
inla.spde1.create c inla.spde1.matern inla.spde1.imatern inla.spde1.matern.osc	Create an inla.spde1 model object.
inla.spde2.generic c rep length cbind diag inla.spde2.theta2phi0 inla.spde2.theta2phi1 inla.spde2.theta2phi2	Creates and inla.spde2 object describing the internal structure of an 'spde2' model.
inla.spde2.matern c matrix	Create an inla.spde2 model object for a Matern model. Use inla.spde2.pcmatern instead for a PC prior for the parameters.
inla.spde2.matern.sd.basis	Calculates an approximate basis for tau and kappa for an inla.spde2.matern model where tau is a rescaling parameter.
inla.spde2.pcmatern c	Create an inla.spde2 model object for a Matern model, using a PC prior for the parameters.
inla.spTransform	Handles transformation of various inla objects accorting to coordinate reference systems of sp::CRS or inla.CRS class.
inla.ssh.copy.id inla.remote	Initialize the definition file and print the path to the internal script to transfer ssh-keys
inla.ssh.copy.id inla.remote	Initialize the definition file and print the path to the internal script to transfer ssh-keys
inla.stack inla.stack.remove.unused inla.stack.compress inla.stack.sum inla.stack.join inla.stack.index inla.stack.LHS inla.stack.RHS inla.stack.data inla.stack.A	Functions for combining data, effects and observation matrices into inla.stack objects, and extracting information from such objects.
inla.surv plot print is.inla.surv as.inla.surv	Create a survival object, to be used as a response variable in a model formula for the inla function for survival models.
inla.surv plot print is.inla.surv as.inla.surv	Create a survival object, to be used as a response variable in a model formula for the inla function for survival models.
inla.upgrade inla.update	Functions to upgrade the INLA -package to the current version.
inla.upgrade inla.update	Functions to upgrade the INLA -package to the current version.
inla.version c	Show the version of the INLA-package
inla.version c	Show the version of the INLA-package
inla.wkt_is_geocent inla.crs_is_geocent inla.wkt_get_ellipsoid_radius inla.crs_get_ellipsoid_radius inla.wkt_set_ellipsoid_radius inla.crs_set_ellipsoid_radius inla.wkt_unit_params inla.wkt_get_lengthunit inla.wkt_set_lengthunit inla.crs_get_wkt inla.crs_get_lengthunit inla.crs_set_lengthunit	Get and set CRS object or WKT string properties.
joint.marginal inla.rjmarginal inla.rjmarginal.eval	Sample and evalue from from a joint marginal approximation as returned using argument selection in inla .
joint.marginal inla.rjmarginal inla.rjmarginal.eval	Sample and evalue from from a joint marginal approximation as returned using argument selection in inla .
jp.define inla.jp.define	A framework for defining joint priors in R
jp.define inla.jp.define	A framework for defining joint priors in R
Kidney	Times of infection from the time to insertion of the catheter for 38 kindey patients using portable dialysis equipment
lattice2node inla.lattice2node.mapping inla.node2lattice.mapping inla.lattice2node inla.node2lattice inla.matrix2vector inla.vector2matrix	These functions define mapping in between two-dimensional indices on a lattice and the one-dimensional node representation used in inla . The mapping from node to lattice follows the default R behaviour (which is column based storage), and as.vector(A) and matrix(a, nrow, ncol) can be used instead of inla.matrix2vector and inla.vector2matrix .
Leuk	This the Leukemia data from Henderson et al (2003); see source.
lines.inla.mesh.segment lines c	Draws a inla.mesh.segment object with generic or rgl graphics.
link inla.link.log inla.link.invlog inla.link.neglog inla.link.invneglog inla.link.logit inla.link.invlogit inla.link.probit inla.link.invprobit inla.link.cloglog inla.link.invcloglog inla.link.loglog inla.link.invloglog inla.link.tan inla.link.invtan inla.link.cauchit inla.link.invcauchit inla.link.identity inla.link.invidentity inla.link.inverse inla.link.invinverse inla.link.robit inla.link.invrobit inla.link.sn inla.link.invsn inla.link.invalid inla.link.invinvalid	Define link-functions and its inverse
link inla.link.log inla.link.invlog inla.link.neglog inla.link.invneglog inla.link.logit inla.link.invlogit inla.link.probit inla.link.invprobit inla.link.cloglog inla.link.invcloglog inla.link.loglog inla.link.invloglog inla.link.tan inla.link.invtan inla.link.cauchit inla.link.invcauchit inla.link.identity inla.link.invidentity inla.link.inverse inla.link.invinverse inla.link.robit inla.link.invrobit inla.link.sn inla.link.invsn inla.link.invalid inla.link.invinvalid	Define link-functions and its inverse
make.lincomb inla.make.lincomb inla.make.lincombs	Create a linear combination or several linear combinations, as input to inla(..., lincomb = <lincomb>)
make.lincomb inla.make.lincomb inla.make.lincombs	Create a linear combination or several linear combinations, as input to inla(..., lincomb = <lincomb>)
marginal inla.dmarginal inla.pmarginal inla.qmarginal inla.rmarginal inla.hpdmarginal inla.smarginal inla.emarginal inla.mmarginal inla.tmarginal c inla.zmarginal	Density, distribution function, quantile function, random generation, hpd-interval, interpolation, expectations, mode and transformations of marginals obtained by inla or inla.hyperpar() . These functions computes the density (inla.dmarginal), the distribution function (inla.pmarginal), the quantile function (inla.qmarginal), random generation (inla.rmarginal), spline smoothing (inla.smarginal), computes expected values (inla.emarginal), computes the mode (inla.mmarginal), transforms the marginal (inla.tmarginal), and provide summary statistics (inla.zmarginal).
meshbuider meshbuilder	Interactively design and build a triangle mesh for use with SPDE models, and assess the finite element approximation errors. The R code needed to recreate the mesh outside the interactive Shiny app is also generated. Spatial objects can be imported from the global workspace.
meshbuider meshbuilder	Interactively design and build a triangle mesh for use with SPDE models, and assess the finite element approximation errors. The R code needed to recreate the mesh outside the interactive Shiny app is also generated. Spatial objects can be imported from the global workspace.
Munich	The Munich rent data
nwEngland	This map is used in association to the Leukemia data from Henderson et al (2003); see source.
Oral	~~ A concise (1-5 lines) description of the dataset. ~~
param2.matern.orig matrix c	Construct parameter settings for inla.spde2.matern models.
pc.alphaw inla.pc.ralphaw inla.pc.dalphaw inla.pc.qalphaw inla.pc.palphaw	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the alpha parameter in the Weibull likelihood
pc.alphaw inla.pc.ralphaw inla.pc.dalphaw inla.pc.qalphaw inla.pc.palphaw	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the alpha parameter in the Weibull likelihood
pc.ar inla.pc.ar.rpacf inla.pc.ar.dpacf	Functions to evaluate and sample from the PC prior for an AR(p) model
pc.ar inla.pc.ar.rpacf inla.pc.ar.dpacf	Functions to evaluate and sample from the PC prior for an AR(p) model
pc.cor0 inla.pc.rcor0 inla.pc.dcor0 inla.pc.qcor0 inla.pc.pcor0	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the correlation in the Gaussian AR(1) model where the base-model is zero correlation.
pc.cor0 inla.pc.rcor0 inla.pc.dcor0 inla.pc.qcor0 inla.pc.pcor0	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the correlation in the Gaussian AR(1) model where the base-model is zero correlation.
pc.cor1 inla.pc.rcor1 inla.pc.dcor1 inla.pc.qcor1 inla.pc.pcor1	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the correlation in the Gaussian AR(1) model where the base-model is correlation one.
pc.cor1 inla.pc.rcor1 inla.pc.dcor1 inla.pc.qcor1 inla.pc.pcor1	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the correlation in the Gaussian AR(1) model where the base-model is correlation one.
pc.cormat inla.pc.cormat.dim2p inla.pc.cormat.p2dim inla.pc.cormat.theta2R inla.pc.cormat.R2theta inla.pc.cormat.r2R inla.pc.cormat.R2r inla.pc.cormat.r2theta inla.pc.cormat.theta2r inla.pc.cormat.permute inla.pc.cormat.rtheta inla.pc.cormat.dtheta	Functions to evaluate and sample from the PC prior for a correlation matrix.
pc.cormat inla.pc.cormat.dim2p inla.pc.cormat.p2dim inla.pc.cormat.theta2R inla.pc.cormat.R2theta inla.pc.cormat.r2R inla.pc.cormat.R2r inla.pc.cormat.r2theta inla.pc.cormat.theta2r inla.pc.cormat.permute inla.pc.cormat.rtheta inla.pc.cormat.dtheta	Functions to evaluate and sample from the PC prior for a correlation matrix.
pc.ddof inla.pc.ddof	A function to evaluate the PC-prior for the degrees of freedom in a standarized Student-t distribution
pc.ddof inla.pc.ddof	A function to evaluate the PC-prior for the degrees of freedom in a standarized Student-t distribution
pc.gamma inla.pc.rgamma inla.pc.dgamma inla.pc.qgamma inla.pc.pgamma	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for Gamma(1/a, 1/a)
pc.gamma inla.pc.rgamma inla.pc.dgamma inla.pc.qgamma inla.pc.pgamma	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for Gamma(1/a, 1/a)
pc.gammacount inla.pc.rgammacount inla.pc.dgammacount inla.pc.qgammacount inla.pc.pgammacount	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the gammacount likelihood
pc.gammacount inla.pc.rgammacount inla.pc.dgammacount inla.pc.qgammacount inla.pc.pgammacount	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the gammacount likelihood
pc.gevtail inla.pc.rgevtail inla.pc.dgevtail inla.pc.qgevtail inla.pc.pgevtail	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the tail parameter in the GEV likelihood
pc.gevtail inla.pc.rgevtail inla.pc.dgevtail inla.pc.qgevtail inla.pc.pgevtail	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the tail parameter in the GEV likelihood
pc.multvar inla.pc.multvar.h.default inla.pc.multvar.simplex.r inla.pc.multvar.simplex.d inla.pc.multvar.sphere.r inla.pc.multvar.sphere.d	Functions to evaluate and simulate from multivariate PC priors: The simplex and sphere case
pc.multvar inla.pc.multvar.h.default inla.pc.multvar.simplex.r inla.pc.multvar.simplex.d inla.pc.multvar.sphere.r inla.pc.multvar.sphere.d	Functions to evaluate and simulate from multivariate PC priors: The simplex and sphere case
pc.prec inla.pc.rprec inla.pc.dprec inla.pc.qprec inla.pc.pprec	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the precision in the Gaussian distribution.
pc.prec inla.pc.rprec inla.pc.dprec inla.pc.qprec inla.pc.pprec	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the precision in the Gaussian distribution.
pc.sn inla.pc.rsn inla.pc.dsn inla.pc.qsn inla.pc.psn	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the alpha parameter in the skew-normal link-function
pc.sn inla.pc.rsn inla.pc.dsn inla.pc.qsn inla.pc.psn	Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the alpha parameter in the skew-normal link-function
plot.inla plot	Takes am inla object produced by inla and plot the results
plot.inla plot	Takes am inla object produced by inla and plot the results
plot.inla.CRS plot c	Plot the outline of a CRS or inla.CRS projection, with optional graticules (transformed parallels and meridians) and Tissot indicatrices.
plot.inla.mesh plot nrow range rgb	Plots an inla.mesh object using either standard graphics or with rgl .
plot.inla.trimesh plot rgb	Plots a triangulation mesh using rgl .
PRborder	A data matrix with Longitude and Latitude coordinates for the boundary of Parana State.
print.inla print	Print a INLA fit
print.inla print	Print a INLA fit
PRprec	A data frame with daily rainfall in the Parana State.
qinv inla.qinv inla.reorderings	This routine use the GMRFLib implementation which compute parts of the inverse of a SPD sparse matrix. The diagonal and values for the neighbours in the inverse, are provided.
qreordering inla.qreordering	This function compute the reordering (or find the best reordering) using the GMRFLib implementation
qsample inla.qsample inla.reorderings ifelse missing if	This function generate samples from a GMRF using the GMRFLib implementation
qsolve inla.qsolve c	This routine use the GMRFLib implementation to solve linear systems with a SPD matrix.
rbind.inla.data.stack.info rbind	Internal function for merging raw stack information
read.graph inla.read.graph inla.write.graph c summary plot print	Construct a graph-object from a file or a matrix; write graph-object to file
rgeneric.define inla.rgeneric.define inla.rgeneric.iid.model c inla.rgeneric.ar1.model inla.rgeneric.wrapper inla.rgeneric.q	A framework for defining latent models in R
rgeneric.define inla.rgeneric.define inla.rgeneric.iid.model c inla.rgeneric.ar1.model inla.rgeneric.wrapper inla.rgeneric.q	A framework for defining latent models in R
Salm	Breslow (1984) analyses some mutagenicity assay data (shown below) on salmonella in which three plates have been processed at each dose i of quinoline and the number of revertant colonies of TA98 Salmonella measured
scale.model inla.scale.model sqrt	This function scales an intrinsic GMRF model so the geometric mean of the marginal variances is one
Scotland	The rate of lip cancer in 56 counties in Scotland is recorder. The data set includes the observed and expected cases (based on the population and its age and sex distribution in the country), a covariate measuring the percentage of the population engaged in agricolture, fishing or forestry and the "position" of each county expressed as a list of adjacent counties
Seeds	Proportion of seeds that germinated on each of 21 plates arranged according to a 2 by 2 factorial layout by seed and type of root extract
SPDEtoy	Simulated data set on 200 location points. The simulation process is made at the introduction of the SPDE tutorial.
summary.inla summary print	Takes a fitted inla or surv.inla object produced by inla or surv.inla and produces a summary from it.
summary.inla summary print	Takes a fitted inla or surv.inla object produced by inla or surv.inla and produces a summary from it.
summary.inla.mesh summary print	Construct and print inla.mesh object summaries
Surg	This example considers mortality rates in 12 hospitals performing cardiac surgery in babies
SurvSim	Simulated data set for Weibull survival model
Tokyo	Recorded days of rain above 1 mm in Tokyo for 2 years, 1983:84
Zambia	Undernutrition of children in each region of Zambia is measured through a score computed on the basis of some anthropometric measures. The data set contains also other infomation about each child.