polle: Policy Learning
Framework for evaluating user-specified finite stage policies and learning realistic policies via doubly robust loss functions. Policy learning methods include doubly robust restricted Q-learning, sequential policy tree learning and outcome weighted learning. See Nordland and Holst (2022) for documentation and references.
| Version: |
1.0 |
| Depends: |
R (≥ 4.0) |
| Imports: |
data.table (≥ 1.14.5), future.apply, lava (≥ 1.7.0), methods, policytree (≥ 1.2.0), SuperLearner, survival, targeted, DynTxRegime |
| Suggests: |
DTRlearn2, glmnet, mgcv, knitr, ranger, rmarkdown, testthat (≥ 3.0) |
| Published: |
2022-12-06 |
| Author: |
Andreas Nordland [aut, cre],
Klaus Holst  [aut] |
| Maintainer: |
Andreas Nordland <andreasnordland at gmail.com> |
| BugReports: |
https://github.com/AndreasNordland/polle/issues |
| License: |
Apache License (≥ 2) |
| URL: |
https://arxiv.org/abs/2212.02335 |
| NeedsCompilation: |
no |
| Citation: |
polle citation info |
| Materials: |
NEWS |
| CRAN checks: |
polle results |
Documentation:
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