Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.

Code for a variety of nonlinear conditional independence tests: 
  Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>),
  Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>),
  Invariant environment prediction,
  Invariant target prediction,
  Invariant residual distribution test,
  Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).

Set of functions for the easy analyses of conditioning data.

Impute the survival times for censored observations based on their conditional survival distributions derived from the Kaplan-Meier estimator. 'CondiS' can replace the censored observations with the best approximations from the statistical model, allowing for direct application of machine learning-based methods. When covariates are available, 'CondiS' is extended by incorporating the covariate information through machine learning-based regression modeling ('CondiS_X'), which can further improve the imputed survival time.

Provides some tabulated data to be be referred to in a discussion in a vignette accompanying my upcoming R package 'playWholeHandDriverPassParams'. In addition to that specific purpose, these may also provide data and illustrate some computational approaches that are relevant to card games like hearts or bridge.This package refers to authentic data from Gregory Stoll <https://gregstoll.com/~gregstoll/bridge/math.html>, and details of performing the probability calculations from Jeremy L. Martin <https://jlmartin.ku.edu/~jlmartin/bridge/basics.pdf>.

Provides ability to control how many times in function
    calls conditions are thrown (shown to the user). Includes control of
    warnings and messages.