Package: RLescalation 1.0.2
Kentaro Matsuura
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).
Authors:
RLescalation_1.0.2.tar.gz
RLescalation_1.0.2.tar.gz(r-4.5-noble)RLescalation_1.0.2.tar.gz(r-4.4-noble)
RLescalation_1.0.2.tgz(r-4.4-emscripten)RLescalation_1.0.2.tgz(r-4.3-emscripten)
RLescalation.pdf |RLescalation.html✨
RLescalation/json (API)
NEWS
| # Install 'RLescalation' in R: |
| install.packages('RLescalation', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/matsuurakentaro/rlescalation/issues
Last updated 1 months agofrom:e0b5e1147e. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 10 2025 |
| R-4.5-linux | OK | Mar 10 2025 |
| R-4.4-linux | OK | Mar 10 2025 |
Exports:clean_python_settingscompute_rl_scenariosEscalationRulelearn_escalation_rulerl_config_setrl_dnn_configsetup_pythonsimulate_one_trial
Dependencies:glueherejsonlitelatticeMatrixnleqslvpngR6rappdirsRcppRcppTOMLreticulaterlangrprojrootwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Clean the Python Virtual Environment | clean_python_settings |
| Compute DLT Probability Scenarios for Reinforcement Learning | compute_rl_scenarios |
| EscalationRule Class | EscalationRule |
| Build an Optimal Dose Escalation Rule using Reinforcement Learning | learn_escalation_rule |
| Configuration of Reinforcement Learning | rl_config_set |
| DNN Configuration for Reinforcement Learning | rl_dnn_config |
| Setting up a Python Virtual Environment | setup_python |
| Simulate One Trial Using an Obtained Optimal Dose Escalation Rule | simulate_one_trial |
