This repository has been archived by the owner. It is now read-only.
Computational actuarial science with R - IME 2017 Workshop
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
docs
.gitignore
Chambers.jpg
Fotolia_99831160.jpg
Inventory.csv
LICENSE
Makefile
README.md
VERSION
backcover.tex
bibliography.tex
by-sa.pdf
by.pdf
casestudy.R
casestudy.tex
colophon.tex
control.R
control.tex
datatypes.Rnw
extensions.R
extensions.tex
floatingpoint.R
floatingpoint.Rnw
frontcover.tex
frontispice.tex
fundamentals.R
fundamentals.Rnw
ime-2017-insurance-of-things.png
ime-2017-workshop-computational-actuarial-science-r.tex
licence.tex
literate.tex
mapping.R
mapping.tex
presentation.Rnw
sa.pdf
speed.R
workshop-abstract.md
wrong.R

README.md

This is the README.md included with the documentation of the workshop Computational actuarial science with R of the 21st International Congress on Insurance: Mathematics and Economics (IME 2017),

The project page provides more details on the workshop.

Computational actuarial science with R - IME 2017 Workshop

This archive contains the material needed for the Computational actuarial science with R workshop of the 21st International Congress on Insurance: Mathematics and Economics (IME 2017).

The workshop aims to improve the general programming skills of the participants and to expand their knowledge of R for quantitative risk analysis.

The workshop focuses on best practices and adopts a hands on approach with lots of demonstrations and exercises. We first review the basic notions of R programming from an actuarial perspective, study the most important tools and learn to be efficient with the language. Because it is an important topic for any programmer, we devote some time to floating point numbers and roundoff error.

Based on a case study, the second part of the workshop follows a typical risk analysis process: manipulation and modeling of insurance data, estimation, measuring of risk, evaluation and simulation. In closing, participants will learn to do more and be more effective in their work with literate programming and version control.

License

Creative Commons Attribution-ShareAlike 4.0 International

Author

Vincent Goulet vincent.goulet@act.ulaval.ca

Contents of the archive

  • README.md: this file;
  • LICENSE: Creative Commons license;
  • ime-2017-workshop-computational-actuarial-science-r.pdf: slides of the workshop;
  • *.R: script files for demonstrations;
  • Inventory.csv: data set for the case study.

Source code

View in GitHub

Release notes

2.1 (2017-07-24)

  • Various typos fixed; thanks to Lukas Fabrykowski and David Beauchemin.
  • Some files were missing in the repository; no effect on the archive.

2.0 (2017-07-07)

  • Material for the second part, the case study, is now part of the release.
  • Fixed display of indication to go to extensions.R.

1.0 (2017-07-06)

  • Initial release. Only the material for the first part is included.