This book aims to provide a broad introduction to computational aspects of actuarial science, in the R environment. We assume that the reader is either learning, or is familiar with actuarial science. It can be seen as a companion to standard textbooks on actuarial science. This book is intended for various audiences: students, researchers, and actuaries. As explained in cite Kendrick and others (2006) (discussing the importance of computational economics) our thesis is that computational economics o ers a way to improve this situation and to bring new life into the teaching of economics in colleges and universities [.] computational economics provides an opportunity for some students to move away from too much use of the lecture-exam paradigm and more use of a laboratorypaper paradigm in teaching under graduate economics. This opens the door for more creative activity on the part of the students by giving them models developed by previous generations and challenging them to modify those models. Based on the assumption that the same holds for computational actuarial science, we decided to publish this book. As claimed by computational scientists, computational actuarial science might simply refer to modern actuarial science methods. Computational methods started probably in the 1950s with Dwyer (1951) and von Neumann (1951). The rst one emphasized the importance of linear computations, and the second one the importance of massive computations, using random number generations (and Monte Carlo methods), while (at that time) access to digital computers was not widespread-- Provided by publisher.