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 ORCID 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

Documentation:

Reference manual: ReSurv.html , ReSurv.pdf
Vignettes: A Machine Learning Approach Based On Survival Analysis For IBNR Frequencies In Non-Life Reserving (source, R code)
Claim Counts Prediction Using Individual Data (source, R code)
Hyperparameters Tuning (source, R code)
Simulate Individual Data (source, R code)
Exploring The Variables Importance (source, R code)

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

Linking:

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