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precmed: Precision Medicine
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: |
1.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
dplyr, gbm, gam, ggplot2, glmnet, graphics, MASS, mgcv, rlang, stringr, tidyr, survival, randomForestSRC |
| Published: |
2024-10-05 |
| 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> |
| BugReports: |
https://github.com/smartdata-analysis-and-statistics/precmed/issues |
| License: |
Apache License (== 2.0) |
| URL: |
https://github.com/smartdata-analysis-and-statistics/precmed,
https://smartdata-analysis-and-statistics.github.io/precmed/ |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
precmed results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.