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 Q-learning, sequential policy tree learning, and outcome weighted learning. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
| Version: | 1.4 |
| Depends: | R (≥ 4.0), SuperLearner |
| Imports: | data.table (≥ 1.14.5), lava (≥ 1.7.0), future.apply, progressr, methods, policytree (≥ 1.2.0), survival, targeted (≥ 0.4), DynTxRegime |
| Suggests: | DTRlearn2, glmnet (≥ 4.1-6), mgcv, xgboost, knitr, ranger, rmarkdown, testthat (≥ 3.0), ggplot2 |
| Published: | 2024-04-25 |
| 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) |
| NeedsCompilation: | no |
| Citation: | polle citation info |
| Materials: | NEWS |
| CRAN checks: | polle results |
Documentation:
| Reference manual: | polle.pdf |
| Vignettes: |
policy_data policy_eval policy_learn |
Downloads:
| Package source: | polle_1.4.tar.gz |
| Windows binaries: | r-devel: polle_1.4.zip, r-release: polle_1.4.zip, r-oldrel: polle_1.4.zip |
| macOS binaries: | r-release (arm64): polle_1.4.tgz, r-oldrel (arm64): polle_1.4.tgz, r-release (x86_64): polle_1.4.tgz, r-oldrel (x86_64): polle_1.4.tgz |
| Old sources: | polle archive |
Linking:
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