# --------------------------------------------
# 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: 1.1.1
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) <https://doi.org/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:
- 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://CRAN.R-project.org/package=RLoptimal
repository-code: https://github.com/MatsuuraKentaro/RLoptimal
url: https://github.com/MatsuuraKentaro/RLoptimal
date-released: '2024-11-21'
contact:
- family-names: Matsuura
  given-names: Kentaro
  email: matsuurakentaro55@gmail.com
  orcid: https://orcid.org/0000-0001-5262-055X