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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 ORCID iD [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: RLescalation_1.0.1.zip, 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.

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.