Vignettes for the 'survival' package. Split from the 'survival' 
    package since the vignettes were getting large. Also, since 'survival' is a 
    recommended package it cannot make use of other packages outside of 
    base+recommended (e.g. 'rmarkdown').

Several datasets which detail the results and events of each season of Survivor. This includes 
  details on the cast, voting history, immunity and reward challenges, jury votes and viewers. This data is 
  useful for practicing data wrangling, graph analytics and analysing how each season of Survivor played out. 
  Includes 'ggplot2' scales and colour palettes for visualisation.

Provides kernel weighting methods for estimation of proportional 
    hazards models with intermittently observed longitudinal covariates. 
    Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) 
    <doi:10.1080/01621459.2014.957289>.

Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) <doi:10.1016/j.csda.2023.107759>.

An implementation of popular evaluation metrics that are commonly used in survival prediction 
  including Concordance Index, Brier Score, Integrated Brier Score, 
  Integrated Square Error, Integrated Absolute Error and Mean Absolute Error.
  For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) <doi:10.1214/08-AOAS169>) and
  (Moradian H, Larocque D and Bellavance F (2017) <doi:10.1007/s10985-016-9372-1>) for different evaluation metrics.

In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.