wnpmle
0.1.2Weighted NPMLE for Recurrent Events with a Competing Terminal Event
Overview
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.
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- OK2026-06-197 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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- Examples that run
- 25%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 21%
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Checks run against github.com/abellach/wnpmle on 2026-07-19.
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1 release. R releases are shown for context.
- 0.1.2Latest2026-06-18 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-18
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1.0
- Download size
- not tracked yet
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