Adaptive Trial Designs for Survival and Binary Endpoints R package goldilocks version 0.6.0 Implements Goldilocks adaptive trial designs for time-to-event and fixed-time binary endpoints. Outcomes are generated with a piecewise exponential model, with conjugate Gamma priors used for predictive imputation. Final analyses may use log-rank or Cox tests, Bayesian piecewise-exponential inference, frequentist risk differences, or Bayesian beta-binomial inference. The method closely follows Broglio and colleagues <doi:10.1080/10543406.2014.888569> and supports simulation of design operating characteristics. Software Depends: R (>= 4.1.0), survival Imports: dplyr, parallel, pbmcapply, PWEALL, Rcpp, rlang, stats LinkingTo: BH, Rcpp Suggests: bench, covr, DiagrammeR, testthat (>= 3.0.0), knitr, rmarkdown Graeme L. Hickey <graemeleehickey@gmail.com> Comprehensive R Archive Network (CRAN) Graeme L. Hickey [aut, cre] (ORCID: <https://orcid.org/0000-0002-4989-0054>), Ying Wan [aut], Thevaa Chandereng [aut] (ORCID: <https://orcid.org/0000-0003-4078-9176>, bayesDP code as a template), Becton, Dickinson and Company [cph], Tim Kacprowski [ctb] (For code from fastlogrank R package.) GPL-3 2026-07-29 application/tgz https://CRAN.R-project.org/package=goldilocks doi:10.32614/CRAN.package.goldilocks en-US