Man pages for ReSurv
Machine Learning Models for Predicting Claim Counts
| data_generator | Individual data generator |
| IndividualDataPP | Individual Data Pre-Processing |
| install_pyresurv | Install Python Environment for ReSurv |
| ooslkh | Compute the out-of-sample likelihood |
| ooslkh.default | Compute the out-of-the sample likelihood |
| ooslkh.ReSurvFit | Compute the out-of-the sample likelihood |
| pkg.env | Helper functions |
| plot.ReSurvFit | Plot for machine learning models |
| plot.ReSurvPredict | Plot of the development factors |
| predict.ReSurvFit | Predict IBNR frequency |
| print.summaryReSurvPredict | Print summary of IBNR predictions |
| ReSurv | Fit 'ReSurv' models on the individual data. |
| ReSurvCV | K fold cross-validation of a 'ReSurv' model. |
| ReSurvCV.default | K fold cross-validation of ReSurv model. |
| ReSurvCV.IndividualDataPP | K fold cross-validation of ReSurv model. |
| ReSurv.default | Fit 'ReSurv' models on the individual data. |
| ReSurv.IndividualDataPP | Fit 'ReSurv' models on the individual data. |
| summary.ReSurvPredict | Summary of IBNR predictions |
| survival_crps | Survival continuously ranked probability score. |
| survival_crps.default | Survival continuously ranked probability score. |
| survival_crps.ReSurvFit | Survival continuously ranked probability score. |
