Conditional Naive Bayes Survival Modelling for Right-Censored Data R package nbsurv version 0.5.1 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>. Software Depends: R (>= 4.1.0) Imports: graphics, survival, stats, utils Suggests: knitr, pkgload, pec, quarto, ranger, rmarkdown, testthat Imad El Badisy <elbadisyimad@gmail.com> Comprehensive R Archive Network (CRAN) Imad El Badisy [aut, cre] GPL-3 2026-09-11 application/tgz https://CRAN.R-project.org/package=nbsurv doi:10.32614/CRAN.package.nbsurv