doi:10.48550/arXiv.2212.02335> for documentation and references.">

polle: Policy Learning (original) (raw)

Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.

Version: 1.6.0
Depends: R (≥ 4.1), SuperLearner
Imports: data.table (≥ 1.14.5), lava (≥ 1.7.2.1), future.apply, progressr, methods, policytree (≥ 1.2.0), survival, targeted (≥ 0.6), DynTxRegime
Suggests: DTRlearn2, glmnet (≥ 4.1-6), mets, mgcv, xgboost, knitr, ranger, rmarkdown, testthat (≥ 3.0), ggplot2
Published: 2025-10-30
DOI: 10.32614/CRAN.package.polle
Author: Andreas Nordland [aut, cre], Klaus Holst ORCID iD [aut]
Maintainer: Andreas Nordland
BugReports: https://github.com/AndreasNordland/polle/issues
License: Apache License (≥ 2)
NeedsCompilation: no
Citation: polle citation info
Materials: NEWS
CRAN checks: polle results

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