MTPL                    Motor Third Party Liability (MTPL) portfolio
MTPL2                   Motor Third Party Liability (MTPL) portfolio
                        (3,000 policyholders)
active_rows_by_date     Find active portfolio rows for event dates
add_observed_experience
                        Add observed portfolio experience to a rating
                        table
add_prediction          Add Model Predictions to a Data Frame
add_relativities        Add expert-based relativities to a refinement
                        workflow
add_restriction         Add coefficient restrictions to a refinement
                        workflow
add_smoothing           Add smoothing to a refinement workflow
add_tariff_segments     Add derived tariff segments to portfolio data
allocate_excess_loss    Allocate excess loss to a pricing portfolio
apply_excess_loading    Apply excess loading to a pricing portfolio
assess_excess_threshold
                        Assess possible excess-loss thresholds
autoplot.bootstrap_performance
                        Autoplot for bootstrap_performance objects
autoplot.check_residuals
                        Autoplot for check_residuals objects
autoplot.excess_loss_allocation
                        Plot an excess-loss allocation
autoplot.excess_threshold_assessment
                        Plot an excess threshold assessment
autoplot.factor_analysis
                        Automatically create a ggplot for objects
                        obtained from factor analysis
autoplot.rating_refinement
                        Plot a model refinement step
autoplot.rating_table   Plot risk factor effects from 'rating_table()'
                        results
autoplot.riskfactor_gam
                        Autoplot for GAM objects from
                        'risk_factor_gam()'
autoplot.tariff_segments
                        Autoplot for tariff segment objects
autoplot.truncated_severity
                        Plot a fitted truncated severity distribution
bootstrap_performance   Bootstrapped model performance
calculate_excess_loss   Decompose claim amounts into capped and excess
                        parts
check_overdispersion    Check overdispersion of a Poisson claim
                        frequency model
check_residuals         Check simulation-based model residuals
derive_tariff_segments
                        Derive insurance tariff segments
edit_smoothing          Edit an existing smoothing step in a refinement
                        workflow
extract_model_data      Extract model data
factor_analysis         Factor analysis for discrete risk factors
fisher_classify         Fisher's natural breaks classification
fit_truncated_severity
                        Fit severity distributions to truncated claim
                        data
merge_date_ranges       Reduce portfolio periods by merging adjacent
                        date ranges
model_performance       Performance of fitted GLMs
outlier_histogram       Portfolio histogram with tail bins
plot_severity_distribution
                        Exploratory severity diagnostics by category
prepare_refinement      Prepare a model refinement workflow
rating_grid             Construct observed rating-grid points from
                        model data or a data frame
rating_table            Build rating tables from fitted pricing models
refit                   Refit a prepared refinement workflow
relativities            Combine multiple level splits into relativities
rgammat                 Generate random samples from a truncated gamma
                        distribution
risk_factor_gam         Fit a GAM for a continuous risk factor
rlnormt                 Generate random samples from a truncated
                        lognormal distribution
rmse                    Root Mean Squared Error (RMSE)
set_reference_level     Set the reference level of a factor
split_level             Define a level split with relativities
split_periods_to_months
                        Split policy periods into monthly rows
split_relativities      Construct a relativities mapping for level
                        splitting
