Package: insurancerating 0.7.4.9000

insurancerating: Analytic Insurance Rating Techniques

Methods for insurance rating. It helps actuaries to implement GLMs within all relevant steps needed to construct a risk premium from raw data. It provides a data driven strategy for the construction of insurance tariff classes. This strategy is based on the work by Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>. It also provides recipes on how to easily perform one-way, or univariate, analyses on an insurance portfolio. In addition it adds functionality to include reference categories in the levels of the coefficients in the output of a generalized linear regression analysis.

Authors:Martin Haringa [aut, cre]

insurancerating_0.7.4.9000.tar.gz
insurancerating_0.7.4.9000.zip(r-4.5)insurancerating_0.7.4.9000.zip(r-4.4)insurancerating_0.7.4.9000.zip(r-4.3)
insurancerating_0.7.4.9000.tgz(r-4.4-any)insurancerating_0.7.4.9000.tgz(r-4.3-any)
insurancerating_0.7.4.9000.tar.gz(r-4.5-noble)insurancerating_0.7.4.9000.tar.gz(r-4.4-noble)
insurancerating_0.7.4.9000.tgz(r-4.4-emscripten)insurancerating_0.7.4.9000.tgz(r-4.3-emscripten)
insurancerating.pdf |insurancerating.html✨
insurancerating/json (API)
NEWS

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

Peer review:

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

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:

actuarialactuarial-scienceinsurancepricing

26 exports 68 stars 3.63 score 118 dependencies 30 scripts 498 downloads

Last updated 22 hours agofrom:838acf04ac. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 17 2024
R-4.5-winOKSep 17 2024
R-4.5-linuxOKSep 17 2024
R-4.4-winOKSep 17 2024
R-4.4-macOKSep 17 2024
R-4.3-winOKSep 17 2024
R-4.3-macOKSep 17 2024

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.datasetsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttpuvhttrinsightinumisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallibcoinlifecyclelme4lmtestlubridatemagrittrMASSMatrixmemoisemgcvmimeminqamunsellmvtnormnlmenloptropensslpartykitpatchworkpillarpkgconfigplotlyplyrpromisesproxypurrrqgamR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackrlangrmarkdownrpartsassscalesscamshinysourcetoolsstringistringrsurvivalsystibbletidyrtidyselecttimechangetinytexutf8vctrsviridisLitewithrxfunxtableyamlzoo

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