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RLoptimal: Optimal Adaptive Allocation Using Deep Reinforcement Learning
An implementation to compute an optimal adaptive allocation rule
using deep reinforcement learning in a dose-response study
(Matsuura et al. (2022) <doi:10.1002/sim.9247>).
The adaptive allocation rule can directly optimize a performance metric,
such as power, accuracy of the estimated target dose, or mean absolute error
over the estimated dose-response curve.
| Version: |
1.2.0 |
| Imports: |
DoseFinding, glue, R6, reticulate, stats, utils |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2024-12-21 |
| DOI: |
10.32614/CRAN.package.RLoptimal |
| Author: |
Kentaro Matsuura 
[aut, cre, cph],
Koji Makiyama [aut, ctb] |
| Maintainer: |
Kentaro Matsuura <matsuurakentaro55 at gmail.com> |
| BugReports: |
https://github.com/MatsuuraKentaro/RLoptimal/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/MatsuuraKentaro/RLoptimal |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README NEWS |
| CRAN checks: |
RLoptimal results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=RLoptimal
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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