Package: RLoptimal 1.0.1.9000

RLoptimal: Optimal Adaptive Allocation Using Deep Reinforcement Learning

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.

Authors:Kentaro Matsuura [aut, cre, cph], Koji Makiyama [aut, ctb]

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RLoptimal/json (API)
NEWS

# Install 'RLoptimal' in R:
install.packages('RLoptimal', repos = c('https://matsuurakentaro.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/matsuurakentaro/rloptimal/issues

On CRAN:

5.69 score 4 stars 19 scripts 191 downloads 8 exports 38 dependencies

Last updated 3 days agofrom:1eb3b2b8ba. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-winOKOct 26 2024
R-4.5-linuxOKOct 26 2024
R-4.4-winOKOct 26 2024
R-4.4-macOKOct 26 2024
R-4.3-winOKOct 26 2024
R-4.3-macOKOct 26 2024

Exports:adjust_significance_levelAllocationRuleclean_python_settingslearn_allocation_rulerl_config_setrl_dnn_configsetup_pythonsimulate_one_trial

Dependencies:clicolorspaceDoseFindingfansifarverggplot2gluegtablehereisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellmvtnormnlmepillarpkgconfigpngR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrprojrootscalestibbleutf8vctrsviridisLitewithr

Optimal Adaptive Allocation Using Deep Reinforcement Learning

Rendered fromRLoptimal.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2024-10-23
Started: 2024-09-22