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>.
| Version: | 0.1.2 |
| Depends: | R (≥ 4.1.0) |
| Imports: | TMB (≥ 1.9.0), survival, methods, MASS, graphics, grDevices |
| Suggests: | testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: | 2026-06-18 |
| DOI: | 10.32614/CRAN.package.wnpmle |
| Author: | Anna Bellach [aut, cre] |
| Maintainer: | Anna Bellach <abellach.biostat at gmail.com> |
| BugReports: | https://github.com/abellach/wnpmle/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/abellach/wnpmle |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | wnpmle results |
| Reference manual: | wnpmle.html , wnpmle.pdf |
| Vignettes: |
Getting Started with wnpmle (source, R code) |
| Package source: | wnpmle_0.1.2.tar.gz |
| Windows binaries: | r-devel: wnpmle_0.1.2.zip, r-release: wnpmle_0.1.2.zip, r-oldrel: wnpmle_0.1.2.zip |
| macOS binaries: | r-release (arm64): wnpmle_0.1.2.tgz, r-oldrel (arm64): wnpmle_0.1.2.tgz, r-release (x86_64): wnpmle_0.1.2.tgz, r-oldrel (x86_64): wnpmle_0.1.2.tgz |
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