Package: ReSurv 1.1.0
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.
Authors:
ReSurv_1.1.0.tar.gz
ReSurv_1.1.0.zip(r-4.7-any)ReSurv_1.1.0.zip(r-4.6-any)ReSurv_1.1.0.zip(r-4.5-any)
ReSurv_1.1.0.tgz(r-4.6-any)ReSurv_1.1.0.tgz(r-4.5-any)
ReSurv_1.1.0.tar.gz(r-4.7-any)ReSurv_1.1.0.tar.gz(r-4.6-any)
ReSurv_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ReSurv/json (API)
| # Install 'ReSurv' in R: |
| install.packages('ReSurv', repos = c('https://edhofman.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/edhofman/resurv/issues
Pkgdown/docs site:https://edhofman.github.io
Last updated from:17d9ef0995. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 223 | ||
| source / vignettes | OK | 243 | ||
| linux-release | OK | 250 | ||
| macos-release | OK | 242 | ||
| macos-oldrel | OK | 203 | ||
| windows-devel | OK | 235 | ||
| windows-release | OK | 126 | ||
| windows-oldrel | OK | 121 | ||
| wasm-release | OK | 152 |
Exports:data_generatorIndividualDataPPpredictReserveReSurvReSurvCVScore_Reserving
Dependencies:actuarclicpp11data.tabledplyrexpintfarverfastDummiesgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelubridatemagrittrMatrixpillarpkgconfigpurrrR6RColorBrewerrlangS7scalesstringistringrsurvivalSynthETICtibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithrxgboost
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Individual data generator | data_generator |
| Individual Data Pre-Processing | IndividualDataPP |
| Plot of the development factors | plot.ReSurvPredict |
| Predict IBNR frequency | predict.ReSurvFit |
| Predict deterministic reserve table | predictReserve predictReserve.ReSurvFit |
| Print method for ReSurvCV objects | print.ReSurvCV |
| Print summary of IBNR predictions | print.summaryReSurvPredict |
| Fit 'ReSurv' models on the individual data. | ReSurv ReSurv.default ReSurv.IndividualDataPP |
| K-fold cross-validation of a ReSurv model | ReSurvCV |
| Score reserving predictions | Score_Reserving |
| Summary of IBNR predictions | summary.ReSurvPredict |