Introduction to Scientific Programming and Simulation Using R（Second Edition）, Owen Jones, Robert Maillardet,
and Andrew Robinson.

This book has two principal aims: to teach scientific programming and to introduce
stochastic modelling. Stochastic modelling, indeed mathematical modelling
more generally, is intimately linked to scientific programming because
the numerical techniques of scientific programming enable the practical application
of mathematical models to real-world problems. In the context of
stochastic modelling, simulation is the numerical technique that enables us to
analyse otherwise intractable models.
Simulation is also the best way we know of developing statistical intuition.
This book assumes that users have completed or are currently undertaking
a first-year university level calculus course. The material is suitable for first
and second year science/engineering/commerce students and masters level students
in applied disciplines. No prior knowledge of programming or probability
is assumed.
It is possible to use the book for a first course on probability, with an emphasis
on applications facilitated by simulation. Modern applied probability and
statistics are numerically intensive, and we give an approach that integrates
programming and probability right from the start.
We chose the programming language R because of its programming features.
We do not describe statistical techniques as implemented in R (though many
of them are admittedly quite remarkable), but rather show how to turn algorithms
into code. Our intended audience is those who want to make tools, not
just use them.
Complementing the book is a package, spuRs, containing most of the code
and data we use. Instructions for installing it are given in the first chapter. In
the back of the book we also provide an index of the programs developed in
the text and a glossary of R commands.