| add_prediction | Add predictions to a data frame |
| autoplot.bootstrap_rmse | Automatically create a ggplot for objects obtained from bootstrap_rmse() |
| autoplot.check_residuals | Automatically create a ggplot for objects obtained from check_residuals() |
| autoplot.constructtariffclasses | Automatically create a ggplot for objects obtained from construct_tariff_classes() |
| autoplot.fitgam | Automatically create a ggplot for objects obtained from fit_gam() |
| autoplot.riskfactor | Automatically create a ggplot for objects obtained from rating_factors() |
| autoplot.univariate | Automatically create a ggplot for objects obtained from univariate() |
| biggest_reference | Set reference group to the group with largest exposure |
| bootstrap_rmse | Bootstrapped RMSE |
| check_overdispersion | Check overdispersion of Poisson GLM |
| check_residuals | Check model residuals |
| construct_tariff_classes | Construct insurance tariff classes |
| fisher | Fisher's natural breaks classification |
| fit_gam | Generalized additive model |
| model_performance | Performance of fitted GLMs |
| MTPL | Ages of 32,731 policyholders in a Motor Third Party Liability (MTPL) portfolio. |
| MTPL2 | Characteristics of 3,000 policyholders in a Motor Third Party Liability (MTPL) portfolio. |
| period_to_months | Split period to months |
| rating_factors | Include reference group in regression output |
| rating_factors1 | Include reference group in regression output |
| reduce | Reduce portfolio by merging redundant date ranges |
| rmse | Root Mean Squared Error |
| summary.reduce | Automatically create a summary for objects obtained from reduce() |
| univariate | Univariate analysis for discrete risk factors |