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: | 1.0.1 |
| Imports: | glue, R6, nleqslv, reticulate, stats, utils |
| Suggests: | knitr, rmarkdown |
| Published: | 2025-01-09 |
| 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:
| Reference manual: | RLescalation.pdf |
| Vignettes: |
Optimal Dose Escalation Using Deep Reinforcement Learning (source, R code) |
Downloads:
| Package source: | RLescalation_1.0.1.tar.gz |
| Windows binaries: | r-devel: not available, r-release: RLescalation_1.0.1.zip, r-oldrel: not available |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): RLescalation_1.0.1.tgz, r-oldrel (x86_64): RLescalation_1.0.1.tgz |
Linking:
Please use the canonical form https://CRAN.R-project.org/package=RLescalation to link to this page.