Sports Injury Data analysis aims to identify and describe the
  magnitude of the injury problem, and to gain more insights (e.g. determine
  potential risk factors) by statistical modelling approaches. The 'injurytools'
  package provides standardized routines and utilities that simplify such
  analyses. It offers functions for data preparation, informative visualizations 
  and descriptive and model-based analyses.

Fit Spatial Econometrics models using Bayesian model averaging 
  on models fitted with INLA. The INLA package can be obtained from 
  <https://www.r-inla.org>.

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>.

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).