Doubly-Robust Estimation for Survival Outcomes in Cluster-Randomized Trials R package DRsurvCRT version 0.0.1 Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" <doi:10.48550/arXiv.2510.08438>. Software Depends: R (>= 3.5) Imports: Rcpp, frailtyEM, survival, ggplot2, pracma, abind LinkingTo: Rcpp, RcppArmadillo Xi Fang <x.fang@yale.edu> Comprehensive R Archive Network (CRAN) Xi Fang [aut, cre], Fan Li [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=DRsurvCRT/LICENSE) 2025-12-30 application/tgz https://CRAN.R-project.org/package=DRsurvCRT doi:10.32614/CRAN.package.DRsurvCRT