Package: RLescalation
Type: Package
Title: Optimal Dose Escalation Using Deep Reinforcement Learning
Version: 1.0.3
Authors@R: c(
    person("Kentaro", "Matsuura", , "matsuurakentaro55@gmail.com", 
           role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0001-5262-055X")))
Description: 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).
URL: https://github.com/MatsuuraKentaro/RLescalation
BugReports: https://github.com/MatsuuraKentaro/RLescalation/issues
VignetteBuilder: knitr
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.3.2
Imports: glue, R6, nleqslv, reticulate, stats, utils, zip
Suggests: knitr, rmarkdown
Collate: 'timer.R' 'train_algo.R' 'utils.R' 'escalation_rule.R'
        'rl_dnn_config.R' 'rl_config_set.R' 'compute_rl_scenarios.R'
        'learn_escalation_rule.R' 'setup_python.R' 'zzz.R'
        'simulate_one_trial.R'
NeedsCompilation: no
Packaged: 2025-10-07 13:26:45 UTC; kmatsuu
Author: Kentaro Matsuura [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0001-5262-055X>)
Maintainer: Kentaro Matsuura <matsuurakentaro55@gmail.com>
Repository: CRAN
Date/Publication: 2025-10-07 13:50:07 UTC
Built: R 4.6.0; ; 2026-04-27 19:49:27 UTC; unix
