Package: insurancerating 0.7.5.9000

insurancerating: Analytic Insurance Rating Techniques

Functions to build, evaluate, and visualize insurance rating models. It simplifies the process of modeling premiums, and allows to analyze insurance risk factors effectively. The package employs a data-driven strategy for constructing insurance tariff classes, drawing on the work of Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>.

Authors:Martin Haringa [aut, cre]

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insurancerating.pdf |insurancerating.html✨
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NEWS

# Install 'insurancerating' in R:
install.packages('insurancerating', repos = c('https://mharinga.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mharinga/insurancerating/issues

Pkgdown site:https://mharinga.github.io

Datasets:
  • MTPL - Characteristics of 30,000 policyholders in a Motor Third Party Liability (MTPL) portfolio.
  • MTPL2 - Characteristics of 3,000 policyholders in a Motor Third Party Liability (MTPL) portfolio.

On CRAN:

Conda:

actuarialactuarial-scienceinsurancepricing

5.89 score 70 stars 28 scripts 612 downloads 26 exports 119 dependencies

Last updated 5 months agofrom:6524e2be41. Checks:6 OK, 3 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 17 2025
R-4.5-winNOTEMar 17 2025
R-4.5-macNOTEMar 17 2025
R-4.5-linuxNOTEMar 17 2025
R-4.4-winOKMar 17 2025
R-4.4-macOKMar 17 2025
R-4.4-linuxOKMar 17 2025
R-4.3-winOKMar 17 2025
R-4.3-macOKMar 17 2025

Exports:add_predictionautoplotbiggest_referencebootstrap_rmsecheck_overdispersioncheck_residualsconstruct_model_pointsconstruct_tariff_classesfisherfit_gamfit_truncated_disthistbinmodel_datamodel_performanceperiod_to_monthsrating_factorsreducerefit_glmrestrict_coefrgammatrlnormtrmserows_per_datesmooth_coefunivariateupdate_glm

Dependencies:abindapearmaskpassbase64encbootbslibcachemciToolsclassclassIntclicodacodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tableDHARMadigestdoParalleldplyre1071evaluateevtreefansifarverfastmapfitdistrplusfontawesomeforeachFormulafsgapgap.datasetsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttpuvhttrinsightinumisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallibcoinlifecyclelme4lmtestlubridatemagrittrMASSMatrixmemoisemgcvmimeminqamunsellmvtnormnlmenloptropensslpartykitpatchworkpillarpkgconfigplotlyplyrpromisesproxypurrrqgamR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangrmarkdownrpartsassscalesscamshinysourcetoolsstringistringrsurvivalsystibbletidyrtidyselecttimechangetinytexutf8vctrsviridisLitewithrxfunxtableyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Add predictions to a data frameadd_prediction
Automatically create a ggplot for objects obtained from bootstrap_rmse()autoplot.bootstrap_rmse
Automatically create a ggplot for objects obtained from check_residuals()autoplot.check_residuals
Automatically create a ggplot for objects obtained from construct_tariff_classes()autoplot.constructtariffclasses
Automatically create a ggplot for objects obtained from fit_gam()autoplot.fitgam
Automatically create a ggplot for objects obtained from restrict_coef()autoplot.restricted
Automatically create a ggplot for objects obtained from rating_factors()autoplot.riskfactor
Automatically create a ggplot for objects obtained from smooth_coef()autoplot.smooth
Automatically create a ggplot for objects obtained from fit_truncated_dist()autoplot.truncated_dist
Automatically create a ggplot for objects obtained from univariate()autoplot.univariate
Set reference group to the group with largest exposurebiggest_reference
Bootstrapped RMSEbootstrap_rmse
Check overdispersion of Poisson GLMcheck_overdispersion
Check model residualscheck_residuals
Construct model points from Generalized Linear Modelconstruct_model_points
Construct insurance tariff classesconstruct_tariff_classes
Fisher's natural breaks classificationfisher
Generalized additive modelfit_gam
Fit a distribution to truncated severity (loss) datafit_truncated_dist
Create a histogram with outlier binshistbin
Get model datamodel_data
Performance of fitted GLMsmodel_performance
Characteristics of 30,000 policyholders in a Motor Third Party Liability (MTPL) portfolio.MTPL
Characteristics of 3,000 policyholders in a Motor Third Party Liability (MTPL) portfolio.MTPL2
Split period to monthsperiod_to_months
Include reference group in regression outputrating_factors
Reduce portfolio by merging redundant date rangesreduce
Refitting Generalized Linear Modelsrefit_glm
Restrict coefficients in the modelrestrict_coef
Generate data from truncated gamma distributionrgammat
Generate data from truncated lognormal distributionrlnormt
Root Mean Squared Errorrmse
Find active rows per daterows_per_date
Smooth coefficients in the modelsmooth_coef
Automatically create a summary for objects obtained from reduce()summary.reduce
Univariate analysis for discrete risk factorsunivariate
Refitting Generalized Linear Modelsupdate_glm