`refit_glm()`

is renamed to`update_glm()`

`construct_model_points()`

and`model_data()`

are added to create model points

`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

`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()`

`summary.reduce()`

now gives correct aggregation for periods “months” and “quarters”`rows_per_date()`

is added to determine active portfolio for a certain date

`smooth_coef()`

and`restrict_coef()`

are added for model refinement`histbin()`

now uses darkblue as default fill color

- 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()`

.

`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

`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

`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

`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()`

`autoplot.univ_all()`

now gives correct labels on the x-axis when`ncol`

> 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_*`

.

`construct_tariff_classes()`

is now split in`fit_gam()`

and`construct_tariff_classes()`

.- A vignette is added on how to use the package.

`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.

- In
`construct_tariff_classes()`

,`model`

now also accepts ‘severity’ as specification.