ReSurv: Machine Learning Models for Predicting Claim Counts
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: | 1.0.0 |
| Depends: | tidyverse |
| Imports: | stats,dplyr,dtplyr,fastDummies,forecast,data.table,purrr,tidyr,tibble,ggplot2,survival,reshape2,bshazard,SynthETIC,rpart,reticulate,xgboost,SHAPforxgboost |
| Suggests: | knitr,rmarkdown |
| Published: | 2024-11-14 |
| 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 |
| NeedsCompilation: | no |
| SystemRequirements: | Python (>= 3.8.0) |
| Materials: | README |
| CRAN checks: | ReSurv results |
Documentation:
Downloads:
| Package source: | ReSurv_1.0.0.tar.gz |
| Windows binaries: | r-devel:ReSurv_1.0.0.zip, r-release:ReSurv_1.0.0.zip, r-oldrel:ReSurv_1.0.0.zip |
| macOS binaries: | r-release (arm64):ReSurv_1.0.0.tgz, r-oldrel (arm64):ReSurv_1.0.0.tgz, r-release (x86_64):ReSurv_1.0.0.tgz, r-oldrel (x86_64):ReSurv_1.0.0.tgz |
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