Package index

Portfolio analysis

Analyse portfolio behaviour and inspect risk factors before model estimation.

factor_analysis() univariate(): Factor analysis for discrete risk factors
autoplot(<univariate>): Automatically create a ggplot for objects obtained from factor analysis
outlier_histogram() histbin(): Histogram with outlier bins
biggest_reference(): Set reference group to the group with largest exposure

Analyse continuous risk factors and convert them to tariff classes.

riskfactor_gam() fit_gam(): Generalized Additive Model for Insurance Risk Factors
summary(<fitgam>): Summary method for fitgam objects
autoplot(<fitgam>): Autoplot for GAM Objects from riskfactor_gam()
construct_tariff_classes(): Construct insurance tariff classes
print(<constructtariffclasses>): Print method for constructtariffclasses objects
as.vector(<constructtariffclasses>): Coerce constructtariffclasses to a vector
autoplot(<constructtariffclasses>): Autoplot for tariff class objects

Estimate pricing models and interpret fitted coefficients in tariff terms.

rating_table() rating_factors(): Include reference group in regression output
autoplot(<riskfactor>): Plot risk factor effects from rating_table() results
add_prediction(): Add Model Predictions to a Data Frame

Apply structured tariff adjustments such as smoothing, restrictions, and relativities.

prepare_refinement(): Prepare a model refinement workflow
autoplot(<rating_refinement>) experimental: Automatically create a ggplot for objects obtained from refinement
add_smoothing() smooth_coef(): Add smoothing to a refinement workflow
edit_smoothing(): Edit an existing smoothing step in a refinement workflow
add_restriction() restrict_coef(): Add coefficient restrictions to a refinement workflow
add_relativities(): Add expert-based relativities to a refinement workflow
relativities_list(): Combine multiple level splits into a relativities list
split_level(): Define a level split with relativities
split_relativities(): Construct a relativities mapping for level splitting
refit(): Refit a prepared refinement workflow
refit_glm() update_glm(): Refit a GLM model or refinement workflow

Assess model fit, predictive stability, dispersion, and residual behaviour.

model_performance(): Performance of fitted GLMs
print(<model_performance>): Print method for model_performance objects
rmse(): Root Mean Squared Error (RMSE)
bootstrap_performance() bootstrap_rmse(): Bootstrapped model performance
as.vector(<bootstrap_performance>): Coerce bootstrap_performance objects to a vector
autoplot(<bootstrap_performance>): Autoplot for bootstrap_performance objects
check_overdispersion(): Check overdispersion of a Poisson GLM
check_residuals(): Check model residuals
autoplot(<check_residuals>): Autoplot for check_residuals objects

Extract model data and analyse observed rating-grid combinations.

model_data() extract_model_data() experimental: Extract model data
rating_grid() construct_model_points(): Construct observed rating-grid points from model data or a data frame

Helper functions for truncated and actuarial distribution workflows.

fit_truncated_dist() experimental: Fit a distribution to truncated severity (loss) data
autoplot(<truncated_dist>): Automatically create a ggplot for objects obtained from fit_truncated_dist()
rlnormt(): Generate random samples from a truncated lognormal distribution
rgammat(): Generate random samples from a truncated gamma distribution

Supporting functions used across pricing workflows.

fisher_classify() fisher(): Fisher's natural breaks classification
split_periods_to_months() period_to_months(): Split periods into monthly intervals
rows_per_date(): Find active rows per date
merge_date_ranges() reduce(): Reduce portfolio by merging redundant date ranges
summary(<reduce>): Summarize reduce objects

Legacy functions retained for backward compatibility. New code should use the updated API.

rating_factors2() deprecated: Include reference group in regression output
model_data() extract_model_data() experimental: Extract model data