Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the 'INLA' package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).

Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.

Prepare objects to implement models over spatial and 
  spacetime domains with the 'INLA' package (<https://www.r-inla.org>).
  These objects contain data to for the 'cgeneric' interface in
  'INLA', enabling fast parallel computations.
  We implemented the spatial barrier model, see Bakka et. al. (2019) 
  <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal 
  models proposed in Lindgren et. al. (2023) 
  <https://www.idescat.cat/sort/sort481/48.1.1.Lindgren-etal.pdf>. 
  Details are provided in the available vignettes and from the URL bellow.

Contain code to work with a C struct, in short
  cgeneric, to define a Gaussian Markov random (GMRF) model.
  The cgeneric contain code to specify GMRF elements such as
  the graph and the precision matrix, and also the initial and 
  prior for its parameters, 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>).
  The 'INLAtools' implement functions to evaluate 
  each one of the model specifications from R.
  The implemented functionalities leverage the use 
  of 'cgeneric' models and provide a way to debug 
  the code as well to work with the prior for the 
  model parameters and to sample from it. 
  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.
  The Kronecker between two cgeneric models was used in 
  Sterrantino et. al. (2024) <doi:10.1007/s10260-025-00788-y>,
  and can be used to build the spatio-temporal intrinsic interaction 
  models for what the needed constraints are automatically set.