R: Machine Learning Models for Predicting Claim Counts

# Machine Learning Models for Predicting Claim Counts

## Documentation for package ‘ReSurv’ version 1.0.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

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.
ReSurv.default | Fit 'ReSurv' models on the individual data.
ReSurv.IndividualDataPP | 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.
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.