Weighted NPMLE for Recurrent Events with a Competing Terminal Event // Provides regression modeling and prediction for the marginal mean of recurrent events in the presence of a competing terminal event using the weighted nonparametric maximum likelihood estimator (wNPMLE) of Bellach and Kosorok (2026) doi:10.48550/arXiv.2605.25934. Two classes of transformation models are implemented: Box-Cox transformation models and logarithmic transformation models. These extend the proportional means model of Ghosh and Lin (2002) doi:10.17615/pt0g-y207 and the transformation model framework of Zeng and Lin (2006) doi:10.1093/biomet/93.3.627. Parameter estimation is performed using automatic differentiation through the Template Model Builder (TMB) framework. Standard errors are computed using sandwich variance estimators that account for estimation of the inverse-probability censoring weights following Bellach, Kosorok, Rschendorf and Fine (2019) doi:10.1080/01621459.2017.1401540.