# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "RLoptimal" in publications use:' type: software license: MIT title: 'RLoptimal: Optimal Adaptive Allocation Using Deep Reinforcement Learning' version: 1.0.1.9000 doi: 10.32614/CRAN.package.RLoptimal abstract: An implementation to compute an optimal adaptive allocation rule using deep reinforcement learning in a dose-response study (Matsuura et al. (2022) ). 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: - family-names: Matsuura given-names: Kentaro email: matsuurakentaro55@gmail.com orcid: https://orcid.org/0000-0001-5262-055X - family-names: Makiyama given-names: Koji email: hoxo.smile@gmail.com repository: https://matsuurakentaro.r-universe.dev repository-code: https://github.com/MatsuuraKentaro/RLoptimal commit: 1eb3b2b8ba738f49ed3fd0cba91cd56e3eaf3836 url: https://github.com/MatsuuraKentaro/RLoptimal contact: - family-names: Matsuura given-names: Kentaro email: matsuurakentaro55@gmail.com orcid: https://orcid.org/0000-0001-5262-055X