DynTxRegime: Methods for Estimating Optimal Dynamic Treatment Regimes
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: |
4.7 |
| Depends: |
methods, modelObj, stats |
| Imports: |
kernlab, rgenoud, dfoptim |
| Suggests: |
MASS, rpart, nnet |
| Published: |
2020-07-22 |
| Author: |
S. T. Holloway, E. B. Laber, K. A. Linn, B. Zhang, M. Davidian, and A. A. Tsiatis |
| Maintainer: |
Shannon T. Holloway <sthollow at ncsu.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
DynTxRegime results |
Downloads:
Reverse dependencies:
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