https://correspondmerchant.r-universe.devRecent package updates in correspondmerchantR-universehttps://github.com/correspondmerchant.pnghttps://correspondmerchant.r-universe.devMon, 26 Oct 2026 16:45:02 GMTabing@bu.edu (Ante Bing)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>.https://github.com/r-universe/correspondmerchant/actions/runs/40388236310Mon, 26 Oct 2026 16:45:02 GMTLAGOtrials1.1.0successhttps://correspondmerchant.r-universe.devhttps://github.com/correspondmerchant/lago-r-packagelago-optimization.Rmdlago-optimization.htmlOptimizing an intervention with LAGO2026-07-29 22:07:512026-08-28 20:06:26