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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.34 |
rolling linux/jammy R-4.5 | gcomputation_0.34.tar.gz |
374.2 KiB |
0.34 |
rolling linux/noble R-4.5 | gcomputation_0.34.tar.gz |
373.9 KiB |
0.34 |
rolling source/ R- | gcomputation_0.34.tar.gz |
121.8 KiB |
0.34 |
latest linux/jammy R-4.5 | gcomputation_0.34.tar.gz |
374.2 KiB |
0.34 |
latest linux/noble R-4.5 | gcomputation_0.34.tar.gz |
373.9 KiB |
0.34 |
latest source/ R- | gcomputation_0.34.tar.gz |
121.8 KiB |
0.34 |
2026-04-23 source/ R- | gcomputation_0.34.tar.gz |
0 B |