INLAtools - Functionalities for the 'INLA' Package
Contain code to work with a C struct, in short cgeneric,
to define a Gaussian Markov random (GMRF) model. The cgeneric
contain required data to specify GMRF elements such as the
graph and the precision matrix, as well hyper-parameters
definition, such as initial values and the prior, which are
useful for model inference. It can be accessed from a C program
and is the recommended way to implement new GMRF models in the
'INLA' package (<https://www.r-inla.org>). We also provide
access from R, useful for code debug and evaluate the prior for
multiple parameter sets. The implemented functionalities
leverage the use of 'cgeneric' models. In order to illustrate
it, a model named `generic0` is included. It can be used to
implement intrinsic models and scale it by default, as proposed
in Sørbye & Rue (2014) <doi:10.1016/j.spasta.2013.06.004>, as
well defining the required constraints. A very useful
functionality is the Kronecker product method that creates a
new model from multiple cgeneric models. It also works with the
rgeneric, the R version of the cgeneric intended to easy try
implementation of new GMRF models before making it for
production. The constraints of a Kronecker between two cgeneric
models, such as spatio-temporal intrinsic interaction models,
see Knorr-Held (2000)
<doi:10.1002/1097-0258%2820000915/30%2919%3A17/18%3C2555%3A%3AAID-SIM587%3E3.0.CO%3B2-%23>,
are automatically set, as shown in the example at
<https://github.com/eliaskrainski/INLAtools>.
