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 specificationbootstrap_rmse()
now uses after_stat(density)
instead of the deprecated dot-dot notationcustom_theme
in autoplot.univariate()
is added to customize the themeautoplot.univariate()
now generates a plot even when there are missing values in the rowsrating_factors()
now always returns the correct coefficients when used
on a 'refitsmooth' or 'refitrestricted' class of GLM. update_glm()
now always returns the correct interval in case the function is used in combination with smooth_coef()
rotate_angle
in autoplot.univariate()
is added to rotate x-labelsunivariate()
now accepts external vectors for x
; vec_ext()
must be usedsmooth_coef()
now gives correct results for intervals with scientific notationreduce()
now returns no errors anymore for columns with dates in POSIXt formatrefit_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 linerating_factors()
now accepts GLMs with an intercept onlyfit_truncated_dist()
is added to fit the original distribution (gamma, lognormal) from truncated severity datajoin_to_nearest()
now returns NA in case NA is used as inputsmooth_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 datesmooth_coef()
and restrict_coef()
are added for model refinementhistbin()
now uses darkblue as default fill color summary.reduce()
, name
can be used to change the name of the new column in the output.MTPL
now contains extra columns for power
, bm
, and zip
. 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 outliersreduce
now returns a data.frame as output check_normality()
is now depreciated; use check_residuals()
instead to detect overall deviations from the expected distributionrating_factors()
now shows significance stars for p-valuesperiod_to_months()
arithmetic operations with dates are rewritten; much fasterunivariate()
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()
insteadcheck_overdispersion()
, check_normality()
, model_performance()
, bootstrap_rmse()
, and add_prediction()
are added to test model quality and return performance metricsreduce()
is added to reduce an insurance portfolio by merging redundant date rangeslabel_width
in autoplot()
is added to wrap long labels in multiple linessort_manual
in autoplot()
is added to sort risk factors into an own orderingautoplot()
now works without manually loading package ggplot2
and patchwork
firstrating_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. 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()
.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.construct_tariff_classes()
, model
now also accepts 'severity' as specification. Add the following code to your website.
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