Contains a suite of functions for survival analysis in health economics.
    These can be used to run survival models under a frequentist (based on maximum likelihood)
    or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian
    Monte Carlo). To run the Bayesian models, the user needs to install additional modules 
    (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 
    'remotes::install_github' from their GitHub repositories: 
    (<https://github.com/giabaio/survHEhmc> and <https://github.com/giabaio/survHEinla/> 
    respectively). 'survHEinla' is based on the package INLA, which is available for download at
    <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models 
    using a common notation and select the preferred mode of inference. The results can also be 
    post-processed to produce probabilistic sensitivity analysis and can be used to export the 
    output to an Excel file (e.g. for a Markov model, as often done by modellers and 
    practitioners). <doi:10.18637/jss.v095.i14>.

High dimensional time to events data analysis with variable selection technique.
             Currently support LASSO, clustering and Bonferroni's correction.

Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).

Newly developed methods for the estimation of several probabilities
      in an illness-death model. The package can be used to obtain nonparametric and 
      semiparametric estimates for: transition probabilities, occupation probabilities, 
      cumulative incidence function and the sojourn time distributions. 
      Additionally, it is possible to fit proportional hazards regression models
      in each transition of the Illness-Death Model. Several auxiliary 
      functions are also provided which can be used for marginal 
      estimation of the survival functions.