Package: RLoptimal 1.2.2

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]

RLoptimal_1.2.2.tar.gz
RLoptimal_1.2.2.zip(r-4.7)RLoptimal_1.2.2.zip(r-4.6)RLoptimal_1.2.2.zip(r-4.5)
RLoptimal_1.2.2.tgz(r-4.6-any)RLoptimal_1.2.2.tgz(r-4.5-any)
RLoptimal_1.2.2.tar.gz(r-4.7-any)RLoptimal_1.2.2.tar.gz(r-4.6-any)
RLoptimal_1.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RLoptimal/json (API)
NEWS

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

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

On CRAN:

Conda:

4.64 score 4 stars 22 scripts 133 downloads 8 exports 30 dependencies

Last updated from:d594feee85. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK136
source / vignettesOK187
linux-release-x86_64OK135
macos-release-arm64OK147
macos-oldrel-arm64OK141
windows-develOK86
windows-releaseOK76
windows-oldrelOK126
wasm-releaseOK117

Exports:adjust_significance_levelAllocationRuleclean_python_settingslearn_allocation_rulerl_config_setrl_dnn_configsetup_pythonsimulate_one_trial

Dependencies:clicpp11DoseFindingfarverggplot2gluegtablehereisobandjsonlitelabelinglatticelifecycleMatrixmvtnormpngR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrprojrootS7scalesvctrsviridisLitewithrzip

Optimal Adaptive Allocation Using Deep Reinforcement Learning

Kentaro Matsuura

Rendered fromRLoptimal.Rmdusingknitr::rmarkdownon Jun 01 2026.

Last update: 2024-12-01
Started: 2024-09-22