bru_make_stack

bru_make_stack.bru_like

bru_make_stack.bru_like_list

Arguments passed on to other methods

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lhood

Linearisation information<ul>
<li>For <code>.bru_like</code>, a linearisation information list with elements
<code>A</code> and <code>offset</code></li>
<li>For <code>.bru_like_list</code>, a list of linearisation information lists</li>
</ul>

Output from <code><a rd-options="=evaluate_index" href="/link/evaluate_index()?package=inlabru&version=2.3.1&to=%3Devaluate_index" data-mini-rdoc="=evaluate_index::evaluate_index()">evaluate_index()</a></code>

A <code>bru_like_list</code> object

lhoods

Combine linearisation for multiple likelihoods

Facilitates spatial and general latent Gaussian modeling using
integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>).
Additionally, extends the GAM-like model class to more general nonlinear predictor
expressions, and implements a log Gaussian Cox process likelihood for
modeling univariate and spatial point processes based on ecological survey data.
Model components are specified with general inputs and mapping methods to the
latent variables, and the predictors are specified via general R expressions,
with separate expressions for each observation likelihood model in
multi-likelihood models. A prediction method based on fast Monte Carlo sampling
allows posterior prediction of general expressions of the latent variables.
Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019)
<doi:10.1111/2041-210X.13168>.

Finn Lindgren

inlabru

Bayesian Latent Gaussian Modelling using INLA and Extensions

Fabian E. Bachl

David L. Borchers

Daniel Simpson

Lindesay Scott-Howard

Seaton Andy

bru_make_stack function

Output from <code><a rd-options='=evaluate_index' href='evaluate_index()'>evaluate_index()</a></code>

bru_make_stack: Build an inla data stack from linearisation information

Description

Usage

Arguments