CRAN/E | polle

polle

Policy Learning

Installation

About

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.

Citation polle citation info
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Key Metrics

Version 1.4
R ≥ 4.0
Published 2024-04-25 2 days ago
Needs compilation? no
License Apache License (≥ 2)
CRAN checks polle results

Downloads

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Maintainer

Maintainer

Andreas Nordland

andreasnordland@gmail.com

Authors

Andreas Nordland

aut / cre

Klaus Holst

aut

Material

NEWS
Reference manual
Package source

Vignettes

policy_data
policy_eval
policy_learn

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

polle archive

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