Performs support vectors analysis for data sets with survival
    outcome. Three approaches are available in the package: The regression approach
    takes censoring into account when formulating the inequality constraints of
    the support vector problem. In the ranking approach, the inequality constraints
    set the objective to maximize the concordance index for comparable pairs
    of observations. The hybrid approach combines the regression and ranking
    constraints in the same model.

Performs survival analysis for one-way layout. The package includes the generalized test for survival ANOVA (Tsui and Weerahandi (1989) <doi:10.2307/2289949> and (Weerahandi, 2004; ISBN:978-0471470175)). It also performs pairwise comparisons and graphical approaches. Moreover, it assesses the weibullness of data in each group via test. The package computes mean and confidence interval under Weibull distribution.

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').

Datasets detailing the results, castaways, and events of each 
  season of Survivor for the US, Australia, South Africa, New Zealand, and the 
  UK. This includes details on the cast, voting history, immunity and reward 
  challenges, jury votes, boot order, advantage details, and episode ratings. 
  Use this for analysis of trends and statistics of the game.

Implements survival-model-based imputation for censored
    laboratory measurements, including Tobit-type models with several
    distribution options. Suitable for data with values below detection
    or quantification limits, the package identifies the best-fitting
    distribution and produces realistic imputations that respect the
    censoring thresholds.

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>.