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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:	1.0.3
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 + fileLICENSE
URL:	https://github.com/MatsuuraKentaro/RLescalation
NeedsCompilation:	no
Language:	en-US
Materials:	README,NEWS
CRAN checks:	RLescalation results

Documentation:

Reference manual:	RLescalation.html ,RLescalation.pdf
Vignettes:	Optimal Dose Escalation Using Deep Reinforcement Learning (source,R code)

Downloads:

Package source:	RLescalation_1.0.3.tar.gz
Windows binaries:	r-devel:RLescalation_1.0.3.zip, r-release:RLescalation_1.0.3.zip, r-oldrel:RLescalation_1.0.3.zip
macOS binaries:	r-release (arm64):RLescalation_1.0.3.tgz, r-oldrel (arm64):RLescalation_1.0.3.tgz, r-release (x86_64):RLescalation_1.0.3.tgz, r-oldrel (x86_64):RLescalation_1.0.3.tgz
Old sources:	RLescalation archive

Linking:

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