# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "RLescalation" in publications use:'
type: software
license: MIT
title: 'RLescalation: Optimal Dose Escalation Using Deep Reinforcement Learning'
version: 1.0.1
doi: 10.32614/CRAN.package.RLescalation
abstract: An implementation to compute an optimal dose escalation rule using deep
  reinforcement learning in phase I oncology trials (Matsuura et al. (2023) <https://doi.org/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:
- family-names: Matsuura
  given-names: Kentaro
  email: matsuurakentaro55@gmail.com
  orcid: https://orcid.org/0000-0001-5262-055X
repository: https://matsuurakentaro.r-universe.dev
repository-code: https://github.com/MatsuuraKentaro/RLescalation
commit: 59a05711f5687702423930b41ae2fe8a7b785ac2
url: https://github.com/MatsuuraKentaro/RLescalation
contact:
- family-names: Matsuura
  given-names: Kentaro
  email: matsuurakentaro55@gmail.com
  orcid: https://orcid.org/0000-0001-5262-055X