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insurancerating 0.8.0

Main API updates

Portfolio analysis

• factor_analysis() is now the primary function for univariate/factor-level portfolio analysis. It returns objects with primary class "factor_analysis" while retaining "univariate" for compatibility.
• plot_severity_distribution() was added for exploratory severity diagnostics by category. It shows individual claim observations with mean and median markers, optional direct labels, and optional firebrick highlighting for claims above a user-supplied threshold.
• univariate() is deprecated and remains available as a compatibility wrapper. The old NSE interface is still supported through the deprecated wrapper.
• factor_analysis() now validates metric columns and grouping variables early, with clearer error messages for missing columns.
• Metrics with a zero or missing denominator now return NA_real_ instead of Inf or NaN.
• autoplot.factor_analysis() is the primary plot method. The deprecated show_plots argument has been replaced by metrics.
• The factor-analysis plot keeps the established package styling and now uses a consistent grid, axis-line, tick and secondary-axis style.

Outlier histograms

• outlier_histogram() has clearer argument names: lower, upper, density, bar_fill, bar_color, tail_fill, tail_color, and density_color.
• Deprecated argument names remain supported: left, right, line, fill, color, and fill_outliers.
• The default colours now align with the package palette: light grey bars, orange tail bins, and blue density curve.
• Input validation has been expanded for missing columns, non-numeric variables, invalid cutoffs, invalid colours, invalid bin counts, constant variables and all-missing variables.
• histbin() is deprecated and remains available as a compatibility wrapper.

Risk factor GAMs and tariff segmentation

• risk_factor_gam() is the primary spelling for fitting GAMs to continuous risk factors. riskfactor_gam() and fit_gam() remain available for compatibility, with fit_gam() deprecated.
• risk_factor_gam() returns objects with primary class "risk_factor_gam"; compatibility classes are retained for older code.
• model = "pure_premium" replaces the older model = "burning" wording. The old value remains supported with a lifecycle warning.
• Input validation and documentation for frequency, severity and pure-premium GAMs have been improved.
• derive_tariff_segments() replaces construct_tariff_classes() as the primary API for deriving tariff segments from a fitted risk-factor GAM.
• derive_tariff_segments() returns objects with primary class "tariff_segments".
• add_tariff_segments() can add derived tariff segments back to a portfolio.
• construct_tariff_classes() remains available as a deprecated compatibility wrapper.
• Split extraction now handles decimal split points correctly.
• Tree-fitting and split-extraction failures now fail clearly instead of silently returning one broad tariff interval.
• The tariff-segment plot now recognises confidence interval columns produced by the package’s own GAM output.

Rating tables

• rating_table() is the primary API for interpreting fitted GLM coefficients in tariff-table form. rating_factors() and rating_factors2() are deprecated wrappers.
• rating_table() now returns objects with primary class "rating_table" while retaining the legacy "riskfactor" class for compatibility.
• exposure_output replaces the older exposure_name argument.
• significance replaces the older signif_stars argument.
• Deprecated rating-table wrappers and plotting code have been separated more clearly in the source structure.
• add_observed_experience() was added to attach factor_analysis() output to a rating_table() object before plotting. This replaces the earlier direct univariate_* arguments in autoplot.rating_table().
• autoplot.rating_table() now plots attached observed experience from add_observed_experience() and uses cleaner, package-consistent plot styling, including a subtle secondary exposure axis.

Prediction helpers

• add_prediction() now has clearer naming arguments: predictions, prefix, confidence, and interval_names.
• var and conf_int are deprecated in favour of predictions and confidence.
• Confidence interval columns now use _lower and _upper suffixes by default.
• The function now validates alpha, confidence settings, duplicate output names, name collisions with existing columns, missing models and non-GLM inputs.

Model data and rating grids

• extract_model_data() replaces model_data() as the primary API for extracting model data from fitted models.
• model_data() is deprecated and remains available as a wrapper.
• rating_grid() now uses base R internally and returns a regular data.frame.
• Plain GLM metadata extraction has been improved so rating_grid(glm) groups by model terms as expected.
• Refinement metadata is now joined by the related original/new factor columns instead of being cross-joined onto every rating-grid row.

Model refinement

• The refinement API has been clarified around prepare_refinement() |> add_*() |> refit().
• A new excess-loss workflow was added for capped severity and pure-premium modelling: assess_excess_threshold(), calculate_excess_loss(), allocate_excess_loss() and apply_excess_loading().
• assess_excess_threshold() compares candidate large-loss thresholds and shows the impact on excess loss, capped loss and pure premium.
• calculate_excess_loss() now performs only the deterministic historical decomposition into capped and excess claim amounts.
• allocate_excess_loss() handles allocation and bootstrap uncertainty modelling. It supports observed or bootstrap excess burdens, portfolio, risk-factor and partial allocation, and optional severity noise in the bootstrap.
• allocate_excess_loss() now uses clearer allocation argument names: allocation_weight, risk_factor, allocation_subset, allocation, n_bootstrap, bootstrap_seed and preserve_total_excess.
• Automatic credibility in allocate_excess_loss() now uses the transparent formula Z = n / (n + credibility_threshold) with credibility_basis = "claims", "excess_claims" or "allocation_weight".
• allocate_excess_loss() now uses preserve_total_excess = TRUE by default so that partial allocation redistributes the selected excess burden without changing the total allocated excess loss.
• apply_excess_loading() adds the allocated excess loading to pricing data and returns base_premium, excess_loading and loaded_premium.
• apply_excess_loading() now treats premium amounts as the default workflow and keeps the distinction between absolute allocated_excess_loss and per-weight allocated_loading explicit.
• add_smoothing() now uses model_variable and source_variable as the primary argument names.
• edit_smoothing() now uses clearer in-object editing arguments for adjusting smoothing settings without supplying an external data frame.
• add_restriction() can now accept a partial restriction data frame. Missing levels are automatically filled with the already fitted GLM relativities, so users can adjust only selected levels.
• add_relativities() now uses model_variable and split_variable.
• relativities() replaces relativities_list() as the helper for building relativity specifications.
• restrict_coef(), smooth_coef() and refit_glm() remain deprecated compatibility wrappers and now link clearly to add_restriction(), add_smoothing() and refit().
• autoplot.rating_refinement() no longer carries an experimental badge and uses the package plot theme.
• The refinement documentation has been expanded with applied examples and a clearer explanation of smoothing, restrictions, relativities and refitting.

Reference levels

• set_reference_level() replaces biggest_reference() as the primary helper for choosing factor reference levels.
• The default method is method = "largest_weight".
• A manual level argument was added so a specific reference level can be selected explicitly.
• biggest_reference() remains available as a deprecated compatibility wrapper.

Time utilities

• split_periods_to_months(), merge_date_ranges() and active_rows_by_date() now avoid mutating caller-visible input data.
• active_rows_by_date() replaces rows_per_date() as the primary API for matching event dates, such as claim dates, to active portfolio rows.
• period_to_months(), rows_per_date() and reduce() remain available as deprecated compatibility wrappers.
• Date interval validation, column validation, aggregation validation, nomatch validation and mult validation have been improved.
• R CMD check notes from data.table helper columns in active_rows_by_date() have been resolved.

Model validation and performance

• bootstrap_performance() now has an explicit metric = "rmse" argument.
• sampling = c("bootstrap", "split") was added to distinguish bootstrap out-of-bag evaluation from split validation.
• Deprecated arguments n and frac remain supported as aliases for n_resamples and sample_fraction.
• Character and factor rating variables are handled more robustly across resamples so prediction does not fail when a level is absent from a training sample.
• autoplot.bootstrap_performance() now uses a package-consistent visual style: subtle grey histogram, transparent blue density, orange original-model reference line, subtle confidence interval lines and no gap between the bars and x-axis.
• bootstrap_rmse() remains available as a deprecated compatibility wrapper and returned objects retain class "bootstrap_rmse" for older code.
• check_overdispersion() now validates non-GLM input, checks for Poisson models and fails clearly when residual degrees of freedom are not positive.
• print.overdispersion() now bases its conclusion on the original p-value rather than a rounded display value.
• check_residuals() now validates inputs, uses all scaled residuals for the KS test, handles empty residual vectors clearly and documents the DHARMa-based residual workflow for actuarial users.
• autoplot.check_residuals() now has a controllable max_points argument and uses ASCII messages and the package plot theme.

Truncated severity distributions

• fit_truncated_severity() replaces fit_truncated_dist() as the primary API for fitting distributions to truncated claim severities.
• Returned objects use primary class "truncated_severity" while compatibility with "truncated_dist" is retained.
• fit_truncated_dist() remains available as a deprecated compatibility wrapper.
• Observations outside the truncation interval now fail clearly instead of only warning and continuing.
• Validation has been expanded for truncation bounds, optimisation starts, grid sizes, reporting options and random generator arguments.
• Public random generators rlnormt() and rgammat() now validate sample size, distribution parameters, finite intervals and positive truncation mass.
• Plot argument names were modernised to ecdf_geom, x_label, y_label, show_title, digits and truncation_digits, with old names supported for compatibility.

Fisher-Jenks classification

• fisher_classify() and fisher() are deprecated because Fisher-Jenks classification is a general-purpose grouping method and is not directly tied to the insurance-rating workflow.
• classInt moved from Imports to Suggests.

Documentation, website and tests

• The README and vignettes have been revised to present the package as a set of actuarial pricing building blocks rather than a prescribed pricing method.
• The former “Pricing principles” vignette was replaced by “Pricing workflow building blocks”.
• The refinement vignette was rewritten with a more practical tone and current API examples.
• pkgdown reference sections were reorganised; deprecated functions are grouped under “Deprecated” and internal S3 methods are no longer listed as primary reference topics.
• New and expanded tests cover tariff segmentation, rating tables, observed experience plotting, refinement workflows, model-data extraction, model performance, overdispersion, residual checks, outlier histograms, truncated distributions, time utilities and factor analysis.

insurancerating 0.7.5

• rating_factors() now always returns correct output when column with exposure in data is not named exposure
• intercept_only in update_glm() is added to apply the manual changes and refit the intercept, ensuring that the changes have no impact on the other variables.
• smoothing in smooth_coef() is added to choose smoothing specification
• The README has been revised

insurancerating 0.7.4

• bootstrap_rmse() now uses after_stat(density) instead of the deprecated dot-dot notation
• custom_theme in autoplot.univariate() is added to customize the theme

insurancerating 0.7.3

• autoplot.univariate() now generates a plot even when there are missing values in the rows
• rating_factors() now always returns the correct coefficients when used on a ‘refitsmooth’ or ‘refitrestricted’ class of GLM.

insurancerating 0.7.2

• update_glm() now always returns the correct interval in case the function is used in combination with smooth_coef()

insurancerating 0.7.1

• rotate_angle in autoplot.univariate() is added to rotate x-labels
• univariate() now accepts external vectors for x; vec_ext() must be used

insurancerating 0.7.0

• smooth_coef() now gives correct results for intervals with scientific notation
• reduce() now returns no errors anymore for columns with dates in POSIXt format

insurancerating 0.6.9

• refit_glm() is renamed to update_glm()
• construct_model_points() and model_data() are added to create model points

insurancerating 0.6.8

• show_total in autoplot.univariate() is added to add line for total of groups in case by is used in univariate(); total_color can be used to change the color of the line, and total_name is added to change the name of the legend for the line
• rating_factors() now accepts GLMs with an intercept only
• fit_truncated_dist() is added to fit the original distribution (gamma, lognormal) from truncated severity data
• join_to_nearest() now returns NA in case NA is used as input

insurancerating 0.6.7

• smooth_coef() now returns an error message when intervals are not obtained by cut()
• get_data() is added to return the data used in refit_glm()

insurancerating 0.6.6

• summary.reduce() now gives correct aggregation for periods “months” and “quarters”
• rows_per_date() is added to determine active portfolio for a certain date

insurancerating 0.6.5

• smooth_coef() and restrict_coef() are added for model refinement
• histbin() now uses darkblue as default fill color

insurancerating 0.6.4

• In summary.reduce(), name can be used to change the name of the new column in the output.
• Dataset MTPL now contains extra columns for power, bm, and zip.
• Some functions in insight are renamed, therefore insight::format_table() is replaced with insight::export_table().

insurancerating 0.6.3

• fit_gam() for pure premium is now using average premium for each x calculated as sum(pure_premium * exposure) / sum(exposure) instead of sum(pure_premium) / sum(exposure) (#2).
• histbin() is added to create histograms with outliers
• reduce now returns a data.frame as output

insurancerating 0.6.2

• check_normality() is now depreciated; use check_residuals() instead to detect overall deviations from the expected distribution
• rating_factors() now shows significance stars for p-values
• period_to_months() arithmetic operations with dates are rewritten; much faster
• univariate() now has argument by to determine summary statistics for different subgroups

insurancerating 0.6.1

• univariate_all() and autoplot.univ_all() are now depreciated; use univariate() and autoplot.univariate() instead
• check_overdispersion(), check_normality(), model_performance(), bootstrap_rmse(), and add_prediction() are added to test model quality and return performance metrics
• reduce() is added to reduce an insurance portfolio by merging redundant date ranges

insurancerating 0.6.0

• label_width in autoplot() is added to wrap long labels in multiple lines
• sort_manual in autoplot() is added to sort risk factors into an own ordering
• autoplot() now works without manually loading package ggplot2 and patchwork first
• rating_factors() now returns an object of class riskfactor
• autoplot.riskfactor() is added to create the corresponding plots to the output given by rating_factors()

insurancerating 0.5.2

• autoplot.univ_all() now gives correct labels on the x-axis when ncol > 1.

insurancerating 0.5.1

• A package website is added using pkgdown.
• construct_tariff_classes() and fit_gam() now only returns tariff classes and fitted gam respectively; other items are stored as attributes.
• univariate_frequency(), univariate_average_severity(), univariate_risk_premium(), univariate_loss_ratio(), univariate_average_premium(), univariate_exposure(), and univariate_all() are added to perform an univariate analysis on an insurance portfolio.
• autoplot() creates the corresponding plots to the summary statistics calculated by univariate_*.

insurancerating 0.5.0

• construct_tariff_classes() is now split in fit_gam() and construct_tariff_classes().
• A vignette is added on how to use the package.

insurancerating 0.4.3

• period_to_months() is added to split rows with a time period longer than one month to multiple rows with a time period of exactly one month each.

insurancerating 0.4.2

• In construct_tariff_classes(), model now also accepts ‘severity’ as specification.

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They may not be fully stable and should be used with caution. We make no claims about them.