Download 'Google' fonts and generate 'CSS' to use in 'rmarkdown' documents and 
  'shiny' applications. Some popular fonts are included and ready to use.

Implements the non-iterative conditional expectation (NICE) 
    algorithm of the g-formula algorithm (Robins (1986) 
    <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). 
    The g-formula can estimate an outcome's counterfactual mean or risk under 
    hypothetical treatment strategies (interventions) when there is sufficient 
    information on time-varying treatments and confounders. 
    This package can be used for discrete or continuous time-varying treatments 
    and for failure time outcomes or continuous/binary end of follow-up 
    outcomes. The package can handle a random measurement/visit process and a 
    priori knowledge of the data structure, as well as censoring (e.g., by loss 
    to follow-up) and two options for handling competing events for failure time
    outcomes. Interventions can be flexibly specified, both as interventions on 
    a single treatment or as joint interventions on multiple treatments.
    See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on 
    how to use the package.

Implements iterative conditional expectation (ICE) estimators of the plug-in g-formula (Wen, Young, Robins, and Hernán (2020) <doi: 10.1111/biom.13321>). 
    Both singly robust and doubly robust ICE estimators based on parametric models are available. 
    The package can be used to estimate survival curves under sustained treatment strategies (interventions) using longitudinal data with time-varying treatments, time-varying confounders, censoring, and competing events.
    The interventions can be static or dynamic, and deterministic or stochastic (including threshold interventions). Both prespecified and user-defined interventions are available.

Implements the G-Formula method for causal inference with time-varying treatments and
  confounders using Bayesian multiple imputation methods, as described by
  Bartlett et al (2025) <doi:10.1177/09622802251316971>. It creates multiple synthetic imputed
  datasets under treatment regimes of interest using the 'mice' package. These can then be analysed
  using rules developed for analysing multiple synthetic datasets.