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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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

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:

actuarial actuarial-science insurance pricing

5.94 score 69 stars 28 scripts 641 downloads 26 exports 118 dependencies

Last updated 1 months agofrom:6524e2be41. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 17 2024
R-4.5-win	OK	Nov 17 2024
R-4.5-linux	OK	Nov 17 2024
R-4.4-win	OK	Nov 17 2024
R-4.4-mac	OK	Nov 17 2024
R-4.3-win	OK	Nov 17 2024
R-4.3-mac	OK	Nov 17 2024

Exports:add_prediction autoplot biggest_reference bootstrap_rmse check_overdispersion check_residuals construct_model_points construct_tariff_classes fisher fit_gam fit_truncated_dist histbin model_data model_performance period_to_months rating_factors reduce refit_glm restrict_coef rgammat rlnormt rmse rows_per_date smooth_coef univariate update_glm

Dependencies:abind ape arm askpass base64enc boot bslib cachem ciTools class classInt cli coda codetools colorspace commonmark cpp11 crayon crosstalk curl data.table DHARMa digest doParallel dplyr e1071 evaluate evtree fansi farver fastmap fitdistrplus fontawesome foreach Formula fs gap gap.datasets generics ggplot2 glue gtable highr htmltools htmlwidgets httpuv httr insight inum isoband iterators jquerylib jsonlite KernSmooth knitr labeling later lattice lazyeval libcoin lifecycle lme4 lmtest lubridate magrittr MASS Matrix memoise mgcv mime minqa munsell mvtnorm nlme nloptr openssl partykit patchwork pillar pkgconfig plotly plyr promises proxy purrr qgam R6 rappdirs rbibutils RColorBrewer Rcpp RcppEigen Rdpack rlang rmarkdown rpart sass scales scam shiny sourcetools stringi stringr survival sys tibble tidyr tidyselect timechange tinytex utf8 vctrs viridisLite withr xfun xtable yaml zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add predictions to a data frame	add_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 exposure	biggest_reference
Bootstrapped RMSE	bootstrap_rmse
Check overdispersion of Poisson GLM	check_overdispersion
Check model residuals	check_residuals
Construct model points from Generalized Linear Model	construct_model_points
Construct insurance tariff classes	construct_tariff_classes
Fisher's natural breaks classification	fisher
Generalized additive model	fit_gam
Fit a distribution to truncated severity (loss) data	fit_truncated_dist
Create a histogram with outlier bins	histbin
Get model data	model_data
Performance of fitted GLMs	model_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 months	period_to_months
Include reference group in regression output	rating_factors
Reduce portfolio by merging redundant date ranges	reduce
Refitting Generalized Linear Models	refit_glm
Restrict coefficients in the model	restrict_coef
Generate data from truncated gamma distribution	rgammat
Generate data from truncated lognormal distribution	rlnormt
Root Mean Squared Error	rmse
Find active rows per date	rows_per_date
Smooth coefficients in the model	smooth_coef
Automatically create a summary for objects obtained from reduce()	summary.reduce
Univariate analysis for discrete risk factors	univariate
Refitting Generalized Linear Models	update_glm