nbsurv: Conditional Naive Bayes Survival Modelling for Right-Censored
Data
Fits conditional naive Bayes survival models for right-censored
outcomes using inverse-probability of censoring weighting. The package
provides model fitting, prediction, resampling-based evaluation,
cross-validation, hyper-parameter tuning, and permutation variable
importance utilities for horizon-specific survival prediction. The
model is the censored naive Bayes classifier of Wolfson et al. (2015)
<doi:10.1002/sim.6526>, which combines the marginal Kaplan-Meier
survivor function with horizon-specific class-conditional covariate
densities and inverse-probability-of-censoring weights. Resampling
evaluation uses the inverse-probability-of-censoring-weighted Brier
score of Gerds and Schumacher (2006) <doi:10.1002/bimj.200610301>.
| Version: |
0.5.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
graphics, survival, stats, utils |
| Suggests: |
knitr, pkgload, pec, quarto, ranger, rmarkdown, testthat |
| Published: |
2026-09-11 |
| DOI: |
10.32614/CRAN.package.nbsurv (may not be active yet) |
| Author: |
Imad El Badisy [aut, cre] |
| Maintainer: |
Imad El Badisy <elbadisyimad at gmail.com> |
| BugReports: |
https://github.com/ielbadisy/nbsurv/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/ielbadisy/nbsurv |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
nbsurv results |
Documentation:
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