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.

Contains the function 'ggsurvplot()' for drawing easily beautiful
    and 'ready-to-publish' survival curves with the 'number at risk' table
    and 'censoring count plot'. Other functions are also available to plot 
    adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.

A collection of functions to help in the analysis of
    right-censored survival data. These extend the methods available in
    package:survival.

Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in  Bofill, Shen & Gómez (2021) <arXiv:2008.12887>.

Tools for flexible estimation of conditional survival 
              functions using off-the-shelf machine learning tools. Implements both 
              global and local survival stacking. See Wolock CJ, Gilbert PB,
              Simon N, and Carone M (2024) <doi:10.1080/10618600.2024.2304070>.