Computational Actuarial Science With R by Arthur Charpentier (editor), 2015, Boca Raton, FL: CRC Press, 618 pages, ISBN: 978‐1‐4665‐9259‐9.

• Published date: 01 March 2017
• Date: 01 March 2017
• DOI: http://doi.org/10.1111/jori.12185

BOOK REVIEW

Computational Actuarial Science With R by Arthur Charpentier (editor), 2015, Boca

Raton, FL: CRC Press, 618 pages, ISBN: 978-1-4665-9259-9.

Reviewer: Tatjana Miljkovic, Ph.D., Department of Statistics, Miami University;

miljkot@miamioh.edu

The popularity of R software in data science, statistical analysis, and predictive

analytics jobs has grown tremendously in the past decade. Based on the study by

Muenchen (2016) on the number of scholarly articles found in 2015 for each

commercial software, R software is reported in second place following SPSS,

surpassing SAS. A number of books using R have already been written in statistics,

economics, engineering, psychology, and other disciplines.

Most books written in the actuarial science area focus exclusively on theory while

lacking practical applications, especially related to a particular use of computational

methods and software. To my knowledge, the first attempt to integrate R with

actuarial science applications was made in the book Modern Actuarial Theory With R,

written by Kaas et al. (2008), focusing mostly on nonlife insurance topics. The book

Computational Actuarial Science With R provides a much broader and comprehensive

review of actuarial topics related not only to nonlife insurance but also to life

insurance and finance areas of actuarial practice.

As the actuarial science field has changed in the pa st two decades with advances in

predictive modeling, mode rn financial economics, and statistical computing

methods, there has been a great need for developi ng modern actuarial methods

that focus on the computational aspects of actuarial science . Implementation of

these methods in R softwar e not only allows the actuar ial field to keep up with

computational science ( e.g., computational statistics) but also to r emain competitive

in the marketplace. Computational Actuarial Sc ience With R elegantly covers a great

deal of useful material an d applications of R in act uarial science and leaves out

much of the actuarial the ory that is commonly found in other actuarial books.

Numerous real data sets t hat accompany the book come from 14 countrie s, bundled

up in an R package, “CASdatasets,” and allow researchers, ind ustry practitioners,

and students to get a hands-on , efficient implementati on of actuarial concepts and

data analysis.

© 2016 The Journal of Risk and Insurance. Vol. 84, No. 1, 267–270 (2017).

DOI: 10.1111/jori.12185

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