The method focuses on a single environmental exposure and induces 
    a main-effect-before-interaction hierarchical structure for the joint selection of interaction terms 
    in a regularized regression model. For details see Zemlianskaia et al. (2021) <arxiv:2103.13510>.

Provides a suite of function-building tools centered around a
  (forward) composition operator, %>>>%, which extends the semantics of the
  'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables
  you to construct composite functions that can be inspected and transformed as
  list-like objects. In conjunction with %>>>%, a compact function constructor,
  fn(), and a partial-application constructor, partial(), are also provided;
  both support quasiquotation.

Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).

Provides a series of general purpose tools to perform g-estimation using the methods described in Sjolander and Vansteelandt (2016) <doi:10.1515/em-2015-0005> and Dukes and Vansteelandt <doi:10.1093/aje/kwx347>. The package allows for g-estimation in a wide variety of circumstances, including an end of study or time-varying outcome, and an exposure that is a binary, continuous, or a categorical variable with three or more categories. The package also supports g-estimation with time-varying causal effects and effect modification by a confounding variable.