Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting.  It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.

Calculates risk differences (or prevalence differences for 
    cross-sectional data) and Number Needed to Treat (NNT) using generalized 
    linear models with automatic link function selection. Provides robust 
    model fitting with fallback methods, support for stratification and 
    adjustment variables, inverse probability of treatment weighting (IPTW) 
    for causal inference with NNT calculations, and publication-ready output 
    formatting. Handles model convergence issues gracefully and provides 
    confidence intervals using multiple approaches.
    Methods are based on approaches described in Mark W. Donoghoe and 
    Ian C. Marschner (2018) "logbin: An R Package for Relative Risk 
    Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09> 
    for robust GLM fitting, Peter C. Austin (2011) "An Introduction to 
    Propensity Score Methods for Reducing the Effects of Confounding in 
    Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods,
    and standard epidemiological methods for risk difference estimation as 
    described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash 
    (2008, ISBN:9780781755641) "Modern Epidemiology".

Geostatistical analysis of continuous and count data.
  Implements stationary Gaussian processes with Matérn correlation for spatial prediction,
  as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7).