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.

Modified versions of the lag() and summary() functions: glag() and gsummary(). The prefix 'g' is a reminder of who to blame if things do not work as they should.

It is not very easy to define segments for y-axis in a 'ggplot2' plot. gg.gap() function in this package can carry it out.

Streamlines exploratory data analysis by providing a turnkey approach 
  to visualising n-dimensional data which graphically reveals correlative or associative relationships 
  between 2 or more features. Represents all dataset features as distinct, vertically 
  aligned bar or tile plots, with plot types auto-selected based on whether variables are categorical or numeric.

Creates publication-quality forest plots and effect-size tables that
    display only the unadjusted and adjusted estimates for a user-specified exposure
    variable of interest, hiding confounder coefficients in accordance with causal
    inference principles. Supports logistic, linear, Poisson, and Cox proportional
    hazards models, with optional cumulative-adjustment visualisation. Built on
    'ggplot2' and follows the tidyverse design philosophy.