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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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