wnpmle: 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, Rüschendorf and Fine
(2019) <doi:10.1080/01621459.2017.1401540>.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=wnpmle
to link to this page.