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:

Reference manual:	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:

Please use the canonical form https://CRAN.R-project.org/package=ReSurv to link to this page.