RLescalation: Optimal Dose Escalation Using Deep Reinforcement Learning
An implementation to compute an optimal dose escalation rule
using deep reinforcement learning in phase I oncology trials
(Matsuura et al. (2023) <doi:10.1080/10543406.2023.2170402>).
The dose escalation rule can directly optimize the percentages of correct
selection (PCS) of the maximum tolerated dose (MTD).
Version: |
1.0.3 |
Imports: |
glue, R6, nleqslv, reticulate, stats, utils, zip |
Suggests: |
knitr, rmarkdown |
Published: |
2025-10-07 |
DOI: |
10.32614/CRAN.package.RLescalation |
Author: |
Kentaro Matsuura
[aut, cre, cph] |
Maintainer: |
Kentaro Matsuura <matsuurakentaro55 at gmail.com> |
BugReports: |
https://github.com/MatsuuraKentaro/RLescalation/issues |
License: |
MIT + file LICENSE |
URL: |
https://github.com/MatsuuraKentaro/RLescalation |
NeedsCompilation: |
no |
Language: |
en-US |
Materials: |
README, NEWS |
CRAN checks: |
RLescalation results |
Documentation:
Downloads:
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