Prediction of claim counts using the feature based development factors introduced in the manuscript Hiabu M., Hofman E. and Pittarello G. (2023) <doi:10.48550/arXiv.2312.14549>.
Implementation of Neural Networks, Extreme Gradient Boosting,
and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.
| Version: |
1.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
stats, dplyr (≥ 1.1.0), actuar, fastDummies, data.table, purrr, tidyr, ggplot2, lubridate, survival, SynthETIC, xgboost |
| Suggests: |
bshazard, clmplus, knitr, torch, rmarkdown, rpart, testthat (≥ 3.0.0) |
| Published: |
2026-09-15 |
| DOI: |
10.32614/CRAN.package.ReSurv |
| Author: |
Emil Hofman [aut, cre, cph],
Gabriele Pittarello
 [aut, cph],
Munir Hiabu [aut,
cph] |
| Maintainer: |
Emil Hofman <emil_hofman at hotmail.dk> |
| BugReports: |
https://github.com/edhofman/ReSurv/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/edhofman/ReSurv,
https://edhofman.github.io/ReSurv/ |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
ReSurv results |