While-Alive Regression for Composite Endpoints with Cluster- Robust Inference // Provides estimation and inference for while-alive regression models targeting the while-alive loss rate for composite endpoints that include recurrent events and a terminal event. The implementation supports flexible time- varying covariate effects through user-selected time bases, including B-splines, natural splines, M-splines, step functions, truncated linear bases, interval-local bases, and piecewise polynomials. Inference can be performed using cluster-robust variance estimators for cluster-randomized trials, with subject-level (IID) variance as a special case. The package includes prediction and plotting utilities and K-fold cross-validation for selecting basis and tuning parameters. Methodology is based on Fang et al. (2025) doi:10.1093/biostatistics/kxaf047.