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.

High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). 
              Currently supports frailty data analysis. Allows for Weibull and 
              Exponential distribution. Includes function for interval censored data.

Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication.

Contains the core survival analysis routines, including
	     definition of Surv objects, 
	     Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
	     and parametric accelerated failure time models.

Implementation of methodology designed to perform: (i) variable 
    selection, (ii) effect estimation, and (iii) uncertainty quantification, 
    for high-dimensional survival data. Our method uses a spike-and-slab prior 
    with Laplace slab and Dirac spike and approximates the corresponding 
    posterior using variational inference, a popular method in machine learning 
    for scalable conditional inference. Although approximate, the variational 
    posterior provides excellent point estimates and good control of the false 
    discovery rate. For more information see Komodromos et al. (2021) 
    <arXiv:2112.10270>.