Parse simple '.ini' configuration files to an structured list. Users
  can manipulate this resulting list with lapply() functions. This same
  structured list can be used to write back to file after modifications.

R dependency injection framework. Dependency injection allows
    a program design to follow the dependency inversion principle. The user
    delegates to external code (the injector) the responsibility of providing its
    dependencies. This separates the responsibilities of use and construction.

Calculate the injury severity score (ISS)
    based on the dictionary in 'ICDPIC' from <https://ideas.repec.org/c/boc/bocode/s457028.html>. The original code was written in 
    'STATA 11'. The original 'STATA' code was written by David Clark, 
    Turner Osler and David Hahn. I implement the same logic for easier access.
    Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. 
    "ICDPIC: Stata module to provide methods for translating International 
    Classification of Diseases (Ninth Revision) diagnosis codes into standard injury 
    categories and/or scores," Statistical Software Components S457028, 
    Boston College Department of Economics, revised 29 Oct 2010.

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>.