Package for evaluating user-specified finite stage policies and learning optimal treatment policies via doubly robust loss functions. Policy learning methods include doubly robust learning of the blip/conditional average treatment effect and sequential policy tree learning. The package also include methods for optimal subgroup analysis. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
Version: 1.5 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: 2024-09-06 DOI: 10.32614/CRAN.package.polle 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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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