gcomputation: Causal Inference by using G-Computation
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>.
| Version: |
0.34 |
| Depends: |
R (≥ 4.0.0), survival, hdnom, glmnet, MASS, mice |
| Imports: |
graphics, utils, methods, grDevices, stats |
| Published: |
2026-05-11 |
| DOI: |
10.32614/CRAN.package.gcomputation (may not be active yet) |
| Author: |
Yohann Foucher 
[aut, cre],
Joe De Keizer
[aut] |
| Maintainer: |
Yohann Foucher <yohann.foucher at univ-poitiers.fr> |
| BugReports: |
https://github.com/chupverse/gcomputation/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
gcomputation results |
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