A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.
| Version: |
1.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
dplyr, gbm, gam, ggplot2, glmnet, graphics, MASS, MESS, mgcv, rlang, stringr, tidyr, survival, randomForestSRC |
| Published: |
2022-10-12 |
| DOI: |
10.32614/CRAN.package.precmed |
| Author: |
Lu Tian  [aut],
Xiaotong Jiang
[aut],
Gabrielle Simoneau
[aut],
Biogen MA Inc. [cph],
Thomas Debray
[ctb, cre],
Stan Wijn [ctb],
Joana Caldas [ctb] |
| Maintainer: |
Thomas Debray <tdebray at fromdatatowisdom.com> |
| License: |
Apache License (== 2.0) |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
precmed results |