add Python vignette

DESCRIPTION

@@ -6,14 +6,20 @@ Authors@R: c(person("Christophe", "Dutang", role = c("aut", "cre"), email = "dut
              person("Arthur", "Charpentier", role = c("aut"), comment = c(ORCID = "0000-0003-3654-6286")),
              person("Ewen", "Gallic", role = c("ctb"), comment = c(ORCID = "0000-0003-3740-2620")),
              person("Julien", "Siharath", role = c("ctb")))
-Description: A collection of insurance datasets, originally for the book 'Computational Actuarial Science with R' 
+Description: A collection of insurance datasets, originally 
+  for the book 'Computational Actuarial Science with R' 
   <https://www.routledge.com/Computational-Actuarial-Science-with-R/Charpentier/p/book/9781138033788> 
-  edited by Arthur Charpentier. Now, the package contains a large variety of actuarial datasets.
+  edited by Arthur Charpentier. Now, the package contains a 
+  large variety of insurance datasets. Vignettes provide 
+  typical use-cases in actuarial science.
 Depends: R (>= 3.6.0), xts, survival
 Imports: lattice
-Suggests: sp, sf, knitr, kableExtra, quarto, rmarkdown, AER, boot, broom, ChainLadder, demography, dplyr, forecast, ggplot2, glmnet, MASS, mgcv, pscl, rainbow, RColorBrewer, splines, tidyr, tidyverse, wesanderson
+Suggests: sp, sf, knitr, kableExtra, quarto, rmarkdown, 
+  AER, boot, broom, ChainLadder, demography, dplyr, forecast, 
+  ggplot2, glmnet, MASS, mgcv, pscl, rainbow, RColorBrewer, 
+  reticulate, splines, tidyr, tidyverse, wesanderson
 License: GPL (>= 2)
-URL: https://dutangc.github.io/CASdatasets/, http://dutangc.free.fr/pub/RRepos/, http://cas.uqam.ca/, http://dutangc.perso.math.cnrs.fr/RRepository/
+URL: https://dutangc.github.io/CASdatasets/, http://dutangc.free.fr/pub/RRepos/, https://freakonometrics.github.io/CASdatasets/, http://dutangc.perso.math.cnrs.fr/RRepository/
 BugReports: https://github.com/dutangc/CASdatasets/issues
 NeedsCompilation: no
 VignetteBuilder: quarto

NEWS.md

@@ -8,7 +8,7 @@ Current version for development.
 - All **Geographical datasets** have been put in the extra directory `geodata/` of this repository.
 - Put new packages in Suggest fields.
 - New datasets have been added (see below).
-- Add quarto vignettes with detailed use cases thanks to Julien.
+- Add quarto vignettes with detailed use cases thanks to Julien. One vignette use Python with the following packages equipy, pandas, numpy, sklearn, seaborn, matplotlib, typing.
 - The package has been uploaded on the [French research data repository](https://entrepot.recherche.data.gouv.fr) and obtained a DOI : [10.57745/P0KHAG](https://doi.org/10.57745/P0KHAG).
 
 ## Datasets lists

_pkgdown.yml

@@ -287,6 +287,14 @@ articles:
   contents:
    - triangle_vignette
    - triangle_aids_vignette
+- title: Life insurance -- use cases of mortality modeling
+  navbar: ~
+  contents:
+   - mortality_vignette
+- title: Fairness analysis
+  navbar: ~
+  contents:
+   - insur_fair_vignette_rf
 
 toc:
   number_sections: yes

man/pricingame.Rd

@@ -249,7 +249,7 @@ data(pg17testyear4)
 }
 
 
-The bonus/malus system is compulsary in France, but we will only use it here as a possible feature. The coefficient is attached to the driver. It starts at 1 for young drivers (i.e. first year of insurance). Then, every year without claim, the bonus decreases by 5 percent until it reaches its minimum of 0.5. Without any claim, the bonus evolution would then be : 1 \eqn{->} 0.95 \eqn{->} 0.9 \eqn{->} 0.85 \eqn{->} 0.8 \eqn{->} 0.76 \eqn{->} 0.72 \eqn{->} 0.68 \eqn{->} 0.64 \eqn{->} 0.6 \eqn{->} 0.57 \eqn{->} 0.54 \eqn{->} 0.51 \eqn{->} 0.5.
+The bonus/malus system is compulsory in France, but we will only use it here as a possible feature. The coefficient is attached to the driver. It starts at 1 for young drivers (i.e. first year of insurance). Then, every year without claim, the bonus decreases by 5 percent until it reaches its minimum of 0.5. Without any claim, the bonus evolution would then be : 1 \eqn{->} 0.95 \eqn{->} 0.9 \eqn{->} 0.85 \eqn{->} 0.8 \eqn{->} 0.76 \eqn{->} 0.72 \eqn{->} 0.68 \eqn{->} 0.64 \eqn{->} 0.6 \eqn{->} 0.57 \eqn{->} 0.54 \eqn{->} 0.51 \eqn{->} 0.5.
 Every time the driver causes a claim (only certain types of claims are taken into account), the coefficient increases by 25 percent, with a maximum of 3.5. Thus, the range of bonus/malus coefficient extends from 0.5 to 3.5 in the datasets.
 
 }