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.">

ReSurv: Machine Learning Models for Predicting Claim Counts (original) (raw)

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 PittarelloORCID iD [aut, cph], Munir Hiabu ORCID iD [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

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