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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 pointsshow_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 colorsummary.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 outputcheck_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 subgroupsunivariate_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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.