# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "gcomputation" in publications use:' type: software license: GPL-2.0-or-later title: 'gcomputation: Causal Inference by using G-Computation' version: '0.34' abstract: 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) . For a binary outcome, details are available in the paper proposed by Chatton et al. (2022) . authors: - family-names: Foucher given-names: Yohann email: yohann.foucher@univ-poitiers.fr orcid: https://orcid.org/0000-0003-0330-7457 - family-names: De Keizer given-names: Joe email: joe.de.keizer@univ-poitiers.fr orcid: https://orcid.org/0000-0003-0821-4540 repository: https://cran.r-universe.dev repository-code: https://github.com/chupverse/gcomputation commit: 0d45c2efc3f34a242c768e116eccca2b2708fef8 url: https://github.com/chupverse/gcomputation date-released: '2026-05-11' contact: - family-names: Foucher given-names: Yohann email: yohann.foucher@univ-poitiers.fr orcid: https://orcid.org/0000-0003-0330-7457