Fast Survival Analysis and Simulation for Clinical Trials // Provides fast alternatives to standard survival analysis functions in the 'survival' package, together with tools for time-to-event trial simulation and sequential analysis. The estimation and testing functions cover a single-time-point Kaplan-Meier estimator (survfit_fast()), log-rank tests including weighted and stratified variants (survdiff_fast()), a closed-form hazard ratio estimator based on the Pike-Halley Estimator method (coxph_fast()), restricted mean survival time (rmst_fast()), window mean survival time (wmst_fast()), milestone survival comparison (milestone_fast()), median survival time (medsurv_fast()), the max-combo test (maxcombo_fast()), the robust modestly-weighted log-rank test (rmw_fast()), the weighted Kaplan-Meier (Pepe-Fleming) test (wkm_fast()), the average hazard with survival weight (ahsw_fast()), and the Kalbfleisch-Prentice average hazard ratio (ahr_fast()). The simulation layer generates individual patient data (simdata_fast()), performs interim or sequential analyses (analysis_fast()), and aggregates operating characteristics (simsummary_fast()). A visualization layer assembles design-stage scenarios (gen_scenario_fast()) and builds analysis-stage Kaplan-Meier curves (kmcurve_fast()), each with plot and print methods. All functions are designed for repeated evaluation inside large simulation loops, such as adaptive sample-size re-estimation, probability-of-success calculations, and regional consistency evaluation in multi- regional trials. Core computations are implemented in 'C++' via 'Rcpp' for maximum performance. Methodological background is described in Collett (2014, ISBN:9780429196294).