bm_taylor: Mapper for linear Taylor approximations

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Mapper for linear Taylor approximations

Description

Provides a pre-computed affine mapping, internally used to represent and evaluate linearisation information. The state0 information indicates for which state the offset was evaluated; The affine mapper output is defined as effect(state) = offset + jacobian %*% (state - state0)

Usage

bm_taylor(offset = NULL, jacobian = NULL, state0 = NULL, values_mapper = NULL)

bru_mapper_taylor(...)

Arguments

offset	For bm_taylor, an offset vector giving the value of the linearisation at state0. May be NULL, interpreted as an all-zero vector of length determined by a non-null Jacobian.
jacobian	For bm_taylor(), the Jacobian matrix, evaluated at state0, or, a named list of such matrices. May be NULL or an empty list, for a constant mapping.
state0	For bm_taylor, the reference state for the linearisation, or a list of such states matching the jacobian list. NULL is interpreted as 0.
values_mapper	mapper object to be used for ibm_n and ibm_values for inla_f=TRUE (experimental, currently unused)
...	Arguments passed on to bm_taylor()

Value

A bm_taylor mapper object.

See Also

bru_mapper, bru_mapper_generics

Other mappers: bm_aggregate(), bm_collect(), bm_const(), bm_expr(), bm_factor(), bm_fm_mesh_1d, bm_fmesher(), bm_harmonics(), bm_index(), bm_linear(), bm_logitaverage(), bm_logsumexp(), bm_marginal(), bm_matrix(), bm_multi(), bm_pipe(), bm_reparam(), bm_repeat(), bm_scale(), bm_shift(), bm_sum(), bru_get_mapper(), bru_mapper()

Examples

m <- bm_taylor(
  offset = rep(2, 3),
  jacobian = matrix(1:6, 3, 2),
  state0 = c(1, 2)
)
ibm_eval2(m, state = 2:3)

inlabru documentation built on July 28, 2026, 9:07 a.m.