Package index • INLA

All functions
`BivMetaAnalysis`	Bivariate Meta Analysis
`inla.CRSargs()` `inla.as.CRSargs.list()` `inla.as.list.CRSargs()` `inla.as.list.CRS()` `inla.as.CRS.list()`	Show expanded CRS arguments
`Cancer`	~~ data name/kind ... ~~
`Drivers`	Time series with seasonal effect
`Epil`	Repeated measures on Poisson counts
`Germany`	Disease Mapping
`INLA()`	INLA
`Kidney`	Kidney infection data
`Leuk` `Leukemia`	The Leukemia data
`Munich`	The Munich rent data
`Oral`	~~ data name/kind ... ~~
`PRborder`	The PRborder data
`PRprec`	The PRprec data
`SPDEtoy`	toy simulated data set for the SPDE tutorial
`Salm`	Extra-Poisson variation in dose-response study
`Scotland`	Conditional Autoregressive (CAR) model for disease mapping
`Seeds`	Factorial design
`Surg`	Surgical: Institutional ranking
`SurvSim`	Survival data
`Tokyo`	Binomial time series
`Zambia`	Semiparametric regression
`inla.agaussian()`	Aggregate Gaussian into an equivalent observation
`inla.ar.pacf2phi()` `inla.ar.phi2pacf()` `inla.ar.phi2acf()` `inla.ar.pacf2acf()`	Convert between parameterizations for the AR(p) model
`as.inla.mesh.segment()` `inla.sp2segment()`	Convert `sp` objects to `inla.mesh.segment` objects.
`inla.binary.install()`	Install alternative binary builds.
`inla.cgeneric.define()` `inla.cgeneric.q()`	cgeneric models
`inla.changelog()`	inla.changelog
`inla.cloglike.define()`	cloglike models
`inla.collect.results()`	Collect results from a inla-call
`control.bgev()` `inla.set.control.bgev.default()`	control.bgev
`control.compute()` `inla.set.control.compute.default()`	control.compute
`control.expert()` `inla.set.control.expert.default()`	control.expert
`control.family()` `inla.set.control.family.default()`	control.family
`control.fixed()` `inla.set.control.fixed.default()`	control.fixed
`control.gcpo()` `inla.set.control.gcpo.default()`	control.gcpo
`control.group()` `inla.set.control.group.default()`	control.group
`control.hazard()` `inla.set.control.hazard.default()`	control.hazard
`control.inla()` `inla.set.control.inla.default()`	control.inla
`control.lincomb()` `inla.set.control.lincomb.default()`	control.lincomb
`control.link()` `inla.set.control.link.default()`	control.link
`control.lp.scale()` `inla.set.control.lp.scale.default()`	control.lp.scale
`control.mix()` `inla.set.control.mix.default()`	control.mix
`control.mode()` `inla.set.control.mode.default()`	control.mode
`control.numa()`	control.numa
`control.pardiso()` `inla.set.control.pardiso.default()`	control.pardiso
`control.pom()` `inla.set.control.pom.default()`	control.pom
`control.predictor()` `inla.set.control.predictor.default()`	control.predictor
`control.scopy()` `inla.set.control.scopy.default()`	control.scopy
`control.sem()` `inla.set.control.sem.default()`	control.sem
`control.stiles()` `inla.set.control.stiles.default()`	control.stiles
`control.taucs()` `inla.set.control.taucs.default()`	control.taucs
`control.update()` `inla.set.control.update.default()`	control.update
`control.vb()` `inla.set.control.vb.default()`	control.fixed
`inla.coxph()` `inla.rbind.data.frames()`	Convert a Cox proportional hazard model into Poisson regression
`inla.cpo()`	Improved estimates for the CPO/PIT-values
`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()`	Handling CRS/WKT
`inla.cut()`	Group-wise model criticism using node-splitting
`inla.debug.graph()`	Debug a graph-file
`inla.dev.new()`	Opens a new device
`inla.doc()`	View documentation
`inla.dryrun()`	Do a dryrun to extract some internal information upfront
`inla.external.lib()`	Return the path to the cgeneric-library for a pre-compiled external package
`extract.groups()`	Extract tagged boundary/internal segments.
`f()`	Define general Gaussian models in the INLA formula
`inla.fgn()`	Return the coefficients in the 3-component AR(1) mixture representing FGN(H)
`inla.geobugs2inla()`	INLA utility functions
`inla.matrix2graph()` `inla.graph2matrix()` `inla.spy()`	Construct a neighbour-matrix from a `graph`
`inla.group()`	Group or cluster covariates
`inla.group.cv()`	Compute group.cv-values
`inla.hyperpar()`	Improved estimates for the hyperparameters (classic-mode only)
`inla.idx()`	Convert indexes
`inla.iidkd.sample()`	Provide samples from the `iidkd` component (experimental)
`inla-class`	inla estimation object class
`inla.CRS()` `inla.wkt_predef()`	Create a coordinate reference system object
`inla()`	inla
`inla.barrier.pcmatern()` `inla.barrier.polygon()` `inla.barrier.q()` `inla.barrier.fem()`	Functions for defining the Barrier models
`inla.diameter()`	Diameter of a point set
`inla.extract.el()`	Extract elements by matching name from container objects.
`inla.fmesher.smorg()`	Compute various mesh related quantities.
`inla.generate.colors()`	Generate text RGB color specifications.
`inla.has_PROJ6()` `inla.not_for_PROJ6()` `inla.not_for_PROJ4()` `inla.fallback_PROJ6()` `inla.requires_PROJ6()`	PROJ6 detection
`inla.identical.CRS()`	Test CRS and inla.CRS for equality
`inla.matern.cov()` `inla.matern.cov.s2()`	Numerical evaluation of Matern and related covariance functions.
`inla.mesh.1d.bary()` `inla.mesh.1d.A()`	Mapping matrix for 1D meshes
`inla.mesh.1d()` `inla.mesh.1d.fem()`	Function space definition objects for 1D SPDE models.
`inla.mesh.2d()`	High-quality triangulations
`inla.mesh.basis()`	Basis functions for inla.mesh
`inla.mesh.boundary()` `inla.mesh.interior()`	Constraint segment extraction for inla.mesh
`inla.mesh.components()`	Compute connected mesh subsets
`inla.mesh.create()` `inla.delaunay()`	Low level function for high-quality triangulations
`inla.mesh.deriv()`	Directional derivative matrices for functions on meshes.
`inla.mesh.fem()`	Finite element matrices
`inla.mesh.lattice()`	Lattice grids for inla.mesh
`inla.mesh.map.lim()` `inla.mesh.map()`	Coordinate mappings for `inla.mesh` projections.
`inla.mesh.project()` `inla.mesh.projector()`	Methods for projecting to/from an inla.mesh
`inla.mesh.query()`	High-quality triangulations
`inla.mesh.segment()` `inla.contour.segment()`	Constraint segments for inla.mesh
`inla.nonconvex.hull()` `inla.nonconvex.hull.basic()`	Nonconvex set extensions.
`inla.over_sp_mesh()`	Check which mesh triangles are inside a polygon
`inla.pin()` `inla.unpin()`	Control main thread pinning for INLA (experimental)
`inla.run.many()`	inla.run.many
`inla.simplify.curve()`	Recursive curve simplification.
`inla.spTransform()`	Wrapper method for `fmesher::fm_transform`
`inla.sp_get_crs()`	Extract CRS information
`inla.spde.make.A()`	Observation/prediction matrices for mesh models.
`inla.spde.make.block.A()`	Observation matrices for mesh models.
`inla.spde.make.index()`	SPDE model index vector generation
`inla.spde.models()` `inla.spde1.models()` `inla.spde2.models()`	List SPDE models supported by inla.spde objects
`inla.spde.precision()` `inla.spde1.precision()` `inla.spde2.precision()`	Precision matrices for SPDE models
`inla.spde.result()` `inla.spde1.result()` `inla.spde2.result()`	SPDE result extraction from INLA estimation results
`inla.spde.sample()`	Sample from SPDE models
`inla.spde1.create()` `inla.spde1.matern()` `inla.spde1.imatern()` `inla.spde1.matern.osc()`	Old SPDE model objects for INLA
`inla.spde2.generic()`	Generic spde2 model creation.
`inla.spde2.matern()` `inla.spde2.theta2phi0()` `inla.spde2.theta2phi1()` `inla.spde2.theta2phi2()`	Matern SPDE model object for INLA
`inla.spde2.matern.sd.basis()`	Approximate variance-compensating basis functions
`inla.spde2.pcmatern()`	Matern SPDE model object with PC prior for INLA
`inla.stack.remove.unused()` `inla.stack.compress()` `inla.stack()` `inla.stack.sum()` `inla.stack.join()` `inla.stack.index()` `inla.stack.LHS()` `inla.stack.RHS()` `inla.stack.data()` `inla.stack.A()` `inla.stack.response()` `print(<inla.data.stack>)`	Data stacking for advanced INLA models
`inla.tempdir()`	inla.tempdir
`inla.get.inlaEnv()`	Return the internal environment used by INLA
`inla.rjmarginal()` `inla.rjmarginal.eval()` `print(<inla.jmarginal>)` `summary(<inla.jmarginal>)` `print(<summary.inla.jmarginal>)` `inla.tjmarginal()` `inla.1djmarginal()`	Sample, transform and evaluate from a joint marginal approximation
`inla.jp.define()`	Joint-prior models
`inla.knmodels()`	Spacetime interaction models
`inla.knmodels.sample()`	Spacetime interaction models sampler function
`inla.lattice2node.mapping()` `inla.node2lattice.mapping()` `inla.lattice2node()` `inla.node2lattice()` `inla.matrix2vector()` `inla.vector2matrix()`	Functions to define mapping between a lattice and nodes
`inla.make.lincomb()` `inla.make.lincombs()`	Create linear combinations
`lines(<inla.mesh.segment>)`	Draw `inla.mesh.segment` objects.
`inla.link.cauchit()` `inla.link.invcauchit()` `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.robit()` `inla.link.invrobit()` `inla.link.loglog()` `inla.link.invloglog()` `inla.link.cloglog()` `inla.link.invcloglog()` `inla.link.ccloglog()` `inla.link.invccloglog()` `inla.link.tan()` `inla.link.invtan()` `inla.link.tan.pi()` `inla.link.invtan.pi()` `inla.link.identity()` `inla.link.invidentity()` `inla.link.inverse()` `inla.link.invinverse()` `inla.link.invqpoisson()` `inla.link.sn()` `inla.link.invsn()` `inla.link.gevit()` `inla.link.invgevit()` `inla.link.cgevit()` `inla.link.invcgevit()` `inla.link.invalid()` `inla.link.invinvalid()`	Link functions in INLA
`inla.list.models()`	List available model components, likelihoods, priors, etc
`local_inla_options()`	Helper for locally setting inla options
`inla.smarginal()` `inla.emarginal()` `inla.dmarginal()` `inla.pmarginal()` `inla.qmarginal()` `inla.hpdmarginal()` `inla.rmarginal()` `inla.tmarginal()` `inla.mmarginal()` `inla.zmarginal()` `inla.is.marginal()`	Functions which operates on marginals
`inla.mdata()` `print(<inla.mdata>)` `as.inla.mdata()` `is.inla.mdata()`	Create an mdata-object for INLA
`merge(<inla>)` `inla.merge()`	Merge a mixture of `inla`-objects
`inla.mesh.assessment()`	Interactive mesh building and diagnostics
`meshbuilder()`	Interactive mesh building and diagnostics
`inla.models()`	Valid models in INLA
`inla.nmix.lambda.fitted()`	Estimate posterior distributions of fitted lambda values
`nwEngland`	The North West England map
`inla.getOption()` `inla.setOption()`	Set and get global options for INLA
`param2.matern.orig()`	Parameter settings for `inla.spde2.matern` models.
`inla.pardiso()` `inla.pardiso.check()`	Describe and check the PARDISO support in R-INLA
`inla.pc.ralphaw()` `inla.pc.dalphaw()` `inla.pc.qalphaw()` `inla.pc.palphaw()`	Utility functions for the PC prior for the `alpha` parameter in the Weibull likelihood
`inla.pc.ar.rpacf()` `inla.pc.ar.dpacf()`	Utility functions for the PC prior for a an AR(p) model
`inla.pc.rcor0()` `inla.pc.dcor0()` `inla.pc.qcor0()` `inla.pc.pcor0()`	Utility functions for the PC prior for correlation in AR(1)
`inla.pc.rcor1()` `inla.pc.dcor1()` `inla.pc.qcor1()` `inla.pc.pcor1()`	Utility functions for the PC prior for correlation in AR(1)
`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()`	Utility functions for the PC prior for a correlation matrix
`inla.pc.rgamma()` `inla.pc.dgamma()` `inla.pc.qgamma()` `inla.pc.pgamma()`	Utility functions for the PC prior for `Gamma(1/a, 1/a)`
`inla.pc.rgammacount()` `inla.pc.dgammacount()` `inla.pc.qgammacount()` `inla.pc.pgammacount()`	Utility functions for the PC prior for the `gammacount` likelihood
`inla.pc.rgevtail()` `inla.pc.dgevtail()` `inla.pc.qgevtail()` `inla.pc.pgevtail()`	Utility functions for the PC prior for the `tail` parameter in the GEV likelihood
`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()`	Multivariate PC priors
`inla.pc.rprec()` `inla.pc.dprec()` `inla.pc.qprec()` `inla.pc.pprec()`	Utility functions for the PC prior for the precision
`inla.pc.rsn()` `inla.pc.dsn()` `inla.pc.qsn()` `inla.pc.psn()`	Utility functions for the PC prior for `skewness` in the skew-normal linkfunction and likelihood
`inla.pc.ddof()`	PC-prior for dof in a standardized Student-t
`inla.pc.rvm0()` `inla.pc.dvm0()` `inla.pc.qvm0()` `inla.pc.pvm0()`	Utility functions for the PC prior for the precision
`inla.pc.rvminf()` `inla.pc.dvminf()` `inla.pc.qvminf()` `inla.pc.pvminf()`	Utility functions for the PC prior for the concentration of von Mises distribution with point mass base model
`plot(<inla>)`	Default INLA plotting
`plot(<inla.mesh>)`	Draw a triangulation mesh object
`plot(<inla.trimesh>)`	Low level triangulation mesh plotting
`inla.posterior.sample()` `inla.posterior.sample.eval()`	Generate samples, and functions thereof, from an approximated posterior of a fitted model
`print(<inla>)`	Print an INLA fit
`inla.priors.used()`	Print priors used
`inla.prune()`	Prune the INLA-package
`prw2.d()`	Utility functions for the PC prior for range in PRW2
`summary(<inla.q>)` `print(<inla.q>)` `inla.qget()` `inla.qdel()` `inla.qstat()` `inla.qlog()` `inla.qnuke()`	Control and view a remote inla-queue
`inla.qinv()`	Computes (parts of) the inverse of a SPD sparse matrix
`inla.qreordering()`	Compute the reordering using the GMRFLib implementation
`inla.qsample()`	Generate samples from a GMRF using the GMRFLib implementation
`inla.qsolve()`	Solves linear SPD systems
`inla.read.graph()` `inla.write.graph()` `plot(<inla.graph>)` `summary(<inla.graph>)` `print(<inla.graph.summary>)`	Read and write a graph-object
`inla.ssh.copy.id()` `inla.remote()`	Setup remote computing
`inla.reorderings()`	Reorderings methods for sparse matrices
`inla.rerun()`	Rerun an analysis
`inla.ks.plot()`	Kolmogorov-Smirnov Test Plots
`inla.rgeneric.ar1.model()` `inla.rgeneric.ar1.model.opt()` `inla.rgeneric.iid.model()` `inla.rgeneric.define()` `inla.rgeneric.wrapper()` `inla.rgeneric.q()`	rgeneric models
`inla.rprior.define()`	Define prior in R
`inla.hyperpar.sample()`	Produce samples from the approximated joint posterior for the hyperparameters
`inla.scale.model.internal()` `inla.scale.model()`	Scale an intrinsic GMRF model
`inla.scopy.define()`	Get the parameterisation of the `scopy` model
`inla.scopy.summary()`	Computes the mean and stdev for the spline from `scopy`
`inla.as.sparse()` `inla.as.dgTMatrix()`	Convert a matrix or sparse matrix into the sparse formate used by INLA
`summary(<inla>)` `print(<summary.inla>)`	Summary for a INLA fit
`summary(<inla.mesh>)` `print(<summary.inla.mesh>)`	Summarizing triangular mesh objects
`inla.surv()` `plot(<inla.surv>)` `print(<inla.surv>)` `as.inla.surv()` `is.inla.surv()`	Create a Survival Object for INLA
`inla.update()` `inla.upgrade()`	Upgrade the INLA-package
`inla.version()`	Show the version of the INLA-package
`inla.as.wkt_tree.wkt()` `inla.as.wkt.wkt_tree()` `inla.wkt_tree_get_item()` `inla.wkt_tree_set_item()`	Internal WKT handling

Reference

All functions