# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# ------------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "LAGOtrials" in publications use:'
type: software
license: GPL-3.0-only
title: 'LAGOtrials: Learn-as-You-Go Adaptive Trial Optimization'
version: 1.1.0
abstract: Optimizes interventions for adaptive "Learn-As-you-GO" (LAGO) trials, in
  which the intervention package is updated between stages using accumulating data.
  Given data from completed stages, lago_optimization() fits the outcome model (via
  'stats' generalized linear models or, for clustered designs, cluster-robust variance)
  and computes the cost-optimal recommended intervention for the next stage that is
  expected to reach a target mean outcome and/or a desired statistical power. get_confidence_set()
  constructs confidence sets for the optimal intervention, and visualize_cost() and
  lago_report() summarize the cost surface and results. Methods are described in Nevo,
  Lok and Spiegelman (2021) <https://doi.org/10.1214/20-aos1978> and Bing, Spiegelman,
  Nevo and Lok (2025) <https://doi.org/10.1093/biomtc/ujaf061>.
authors:
- family-names: Bing
  given-names: Ante
  email: abing@bu.edu
- family-names: Bui
  given-names: Minh
- family-names: Cui
  given-names: Jingyu
repository: https://cran.r-universe.dev
repository-code: https://github.com/correspondMerchant/LAGO-R-Package
commit: 544ca89e120aa44f5eddd1618238340a5d4e91e5
url: https://correspondmerchant.github.io/LAGO-R-Package/
date-released: '2026-10-24'
contact:
- family-names: Bing
  given-names: Ante
  email: abing@bu.edu