DynTxRegime: Methods for Estimating Optimal Dynamic Treatment Regimes (original) (raw)
Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.
| Version: | 4.16 |
|---|---|
| Depends: | methods, modelObj, stats |
| Imports: | kernlab, rgenoud, dfoptim |
| Suggests: | MASS, rpart, nnet |
| Published: | 2025-05-03 |
| DOI: | 10.32614/CRAN.package.DynTxRegime |
| Author: | Shannon T. Holloway [aut, cre], E. B. Laber [aut], K. A. Linn [aut], B. Zhang [aut], M. Davidian [aut], A. A. Tsiatis [aut] |
| Maintainer: | Shannon T. Holloway <shannon.t.holloway at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | |
| In views: | CausalInference |
| CRAN checks: | DynTxRegime results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=DynTxRegimeto link to this page.