Causal Inference by using G-Computation R package gcomputation version 0.34 Several functions and S3 methods for G-computation and emulation of clinical trials. It allows for flexible estimation of the outcome model, especially penalized regressions (Lasso, Ridge, or Elasticnet) for binary, continuous, counting, or right-censored time-to-event outcomes. Average treatment effect among the entire population (ATE) or among the treated population (ATT) can be estimated. The method for time-to-events is described by Chatton et al. (2020) <doi:10.1038/s41598-020-65917-x>. For a binary outcome, details are available in the paper proposed by Chatton et al. (2022) <doi:10.1177/09622802211047345>. Software Depends: R (>= 4.0.0), survival, hdnom, glmnet, MASS, mice Imports: graphics, utils, methods, grDevices, stats Yohann Foucher <yohann.foucher@univ-poitiers.fr> Comprehensive R Archive Network (CRAN) Yohann Foucher [aut, cre] (ORCID: <https://orcid.org/0000-0003-0330-7457>), Joe De Keizer [aut] (ORCID: <https://orcid.org/0000-0003-0821-4540>) GPL (>= 2) 2026-05-11 application/tgz https://CRAN.R-project.org/package=gcomputation doi:10.32614/CRAN.package.gcomputation