Package: RLoptimal
Type: Package
Title: Optimal Adaptive Allocation Using Deep Reinforcement Learning
Version: 1.2.2
Authors@R: c(
    person("Kentaro", "Matsuura", , "matsuurakentaro55@gmail.com", 
           role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0001-5262-055X")),
    person("Koji", "Makiyama", , "hoxo.smile@gmail.com", role = c("aut", "ctb")))
Description: An implementation to compute an optimal adaptive
        allocation rule using deep reinforcement learning in a
        dose-response study (Matsuura et al. (2022)
        <doi:10.1002/sim.9247>). The adaptive allocation rule can
        directly optimize a performance metric, such as power, accuracy
        of the estimated target dose, or mean absolute error over the
        estimated dose-response curve.
URL: https://github.com/MatsuuraKentaro/RLoptimal
BugReports: https://github.com/MatsuuraKentaro/RLoptimal/issues
VignetteBuilder: knitr
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Imports: DoseFinding, glue, R6, reticulate, stats, utils, zip
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Collate: 'timer.R' 'train_algo.R' 'utils.R' 'allocation_rule.R'
        'generate_setup_code.R' 'rl_dnn_config.R' 'rl_config_set.R'
        'learn_allocation_rule.R' 'setup_python.R' 'zzz.R'
        'simulate_one_trial.R' 'adjust_significance_level.R'
Config/testthat/edition: 3
Config/pak/sysreqs: libpng-dev python3
Repository: https://matsuurakentaro.r-universe.dev
Date/Publication: 2025-10-02 22:55:52 UTC
RemoteUrl: https://github.com/matsuurakentaro/rloptimal
RemoteRef: HEAD
RemoteSha: d594feee850dbc420c45091a6f1888b0304941b0
NeedsCompilation: no
Packaged: 2026-10-17 08:38:53 UTC; root
Author: Kentaro Matsuura [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0001-5262-055X>),
  Koji Makiyama [aut, ctb]
Maintainer: Kentaro Matsuura <matsuurakentaro55@gmail.com>