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).

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 in Lindgren et. al. (2023) <doi:10.48550/arXiv.2006.04917>. 
  Details are provided in the available vignettes and from the URL bellow.

A collection of functions for creating color schemes.
    Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory Project Office.

A collection of analysis-ready datasets for the
   U.S. Geological Survey - Idaho National Laboratory (USGS-INL)
   groundwater and surface-water monitoring networks, administered by the
   USGS-INL Project Office in cooperation with the U.S. Department of Energy.
   The data collected from wells and surface-water stations at the
   Idaho National Laboratory and surrounding areas have been used to describe
   the effects of waste disposal on water contained in the
   eastern Snake River Plain aquifer, located in the southeastern part of
   Idaho, and the availability of water for long-term consumptive and
   industrial use. The package includes long-term monitoring records dating
   back to measurements from 1949. Geospatial data describing the areas from
   which samples were collected or observations were made are also included in
   the package. Bundling this data into a single package significantly reduces
   the magnitude of data processing for researchers and provides a way to
   distribute the data along with its documentation in a standard format.
   Geospatial datasets are made available in a common projection and datum, and
   geohydrologic data have been structured to facilitate analysis.