Collection of functions designed to compute risk-based portfolios as described 
    in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.

Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) <doi: 10.1007/s10985-012-9240-6>.

Implementation of the following methods for event history analysis.
    Risk regression models for survival endpoints also in the presence of competing
    risks are fitted using binomial regression based on a time sequence of binary
    event status variables. A formula interface for the Fine-Gray regression model
    and an interface for the combination of cause-specific Cox regression models.
    A toolbox for assessing and comparing performance of risk predictions (risk
    markers and risk prediction models). Prediction performance is measured by the
    Brier score and the area under the ROC curve for binary possibly time-dependent
    outcome. Inverse probability of censoring weighting and pseudo values are used
    to deal with right censored data. Lists of risk markers and lists of risk models
    are assessed simultaneously. Cross-validation repeatedly splits the data, trains
    the risk prediction models on one part of each split and then summarizes and
    compares the performance across splits.

Risk ratios and risk differences are estimated using regression 
  models that allow for binary, categorical, and continuous exposures and 
  confounders. Implemented are marginal standardization after fitting logistic 
  models (g-computation) with delta-method and bootstrap standard errors, 
  Miettinen's case-duplication approach (Schouten et al. 1993, 
  <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical 
  variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting 
  values from Poisson models (Spiegelman and Hertzmark 2005, 
  <doi:10.1093/aje/kwi188>), and others.