Package: wnpmle 0.1.1
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 based on the weighted nonparametric maximum likelihood estimator (wNPMLE) of Bellach and Kosorok (2026) <https://arxiv.org/abs/2605.25934>. Two large classes of transformation models are included: the Box-Cox transformation models and the logarithmic transformation models, extending the proportional means model of Ghosh and Lin (2002) <doi:10.17615/pt0g-y207> and the model of Zeng and Lin (2006) <doi:10.1093/biomet/93.3.627>. Parameter estimates are derived via automatic differentiation using the Template Model Builder (TMB) framework. Standard errors are computed via a sandwich variance estimator with correction for the estimated weights, following Bellach, Kosorok, Ruschendorf and Fine (2019) <doi:10.1080/01621459.2017.1401540>.
Authors:
wnpmle_0.1.1.tar.gz
wnpmle_0.1.1.zip(r-4.7)wnpmle_0.1.1.zip(r-4.6)wnpmle_0.1.1.zip(r-4.5)
wnpmle_0.1.1.tgz(r-4.6-any)wnpmle_0.1.1.tgz(r-4.5-any)
wnpmle_0.1.1.tar.gz(r-4.7-any)wnpmle_0.1.1.tar.gz(r-4.6-any)
wnpmle_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
wnpmle/json (API)
| # Install 'wnpmle' in R: |
| install.packages('wnpmle', repos = c('https://abellach.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/abellach/wnpmle/issues
Last updated from:53411d2ab3. Checks:7 OK, 2 WARNING. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 190 | ||
| source / vignettes | OK | 301 | ||
| linux-release-x86_64 | OK | 221 | ||
| macos-release-arm64 | WARNING | 206 | ||
| macos-oldrel-arm64 | WARNING | 224 | ||
| windows-devel | OK | 200 | ||
| windows-release | OK | 169 | ||
| windows-oldrel | OK | 172 | ||
| wasm-release | OK | 99 |
Exports:AIC.wnpmlebaselineBIC.wnpmlebladder_prepcoef.wnpmlelogLik.wnpmleplot_loglikplot.wnpmlepredict.wnpmleprint.wnpmlesummary.wnpmlevcov.wnpmlewnpmle_fit
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| AIC for wnpmle objects | AIC.wnpmle |
| Extract the estimated baseline mean function | baseline |
| BIC for wnpmle objects | BIC.wnpmle |
| Prepare bladder cancer data for wnpmle analysis | bladder_prep |
| Extract coefficients from a wnpmle object | coef.wnpmle |
| Log-likelihood for wnpmle objects | logLik.wnpmle |
| Log-likelihood profile plot for the transformation parameter | plot_loglik |
| Plot method for wnpmle objects | plot.wnpmle |
| Predict marginal mean for new covariate values | predict.wnpmle |
| Print method for wnpmle objects | print.wnpmle |
| Summary method for wnpmle objects | summary.wnpmle |
| Extract variance-covariance matrix from a wnpmle object | vcov.wnpmle |
| Fit Weighted NPMLE for Survival Data with Recurrent or Competing Events | wnpmle_fit |
