Learn-as-You-Go Adaptive Trial Optimization R package LAGOtrials version 1.1.0 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) <doi:10.1214/20-aos1978> and Bing, Spiegelman, Nevo and Lok (2025) <doi:10.1093/biomtc/ujaf061>. Software Depends: R (>= 2.10) Imports: rje, NlcOptim, cli, bslib, shiny, ggplot2, shinyjs, Rcpp LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), tibble, knitr, rmarkdown, jsonlite, spelling Ante Bing <abing@bu.edu> Comprehensive R Archive Network (CRAN) Ante Bing [aut, cre], Minh Bui [aut], Jingyu Cui [aut], Mike Bostock [cph] (Author of the bundled D3 v7 library, inst/js/d3.v7.min.js) GPL-3 2026-10-24 application/tgz https://CRAN.R-project.org/package=LAGOtrials doi:10.32614/CRAN.package.LAGOtrials en-US