Introduction to Scientific Programming and Simulation Using R, Second Edition (Chapman & Hall/CRC

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Table of Contents

Preface How to use this book Programming Setting up Installing R Starting R Working directory Writing scripts Help Supporting material R as a calculating environment Arithmetic Variables Functions Vectors Missing data: NA Expressions and assignments Logical expressions Matrices The workspace Exercises Basic programming Introduction Branching with if Looping with for Looping with while Vector-based programming Program flow Basic debugging Good programming habits Exercises Input and output Text Input from a file Input from the keyboard Output to a file Plotting Exercises Programming with functions Functions Arguments Vector-based programming using functions Recursive programming Debugging functions Exercises Sophisticated data structures Factors Dataframes Lists Exercises Better graphics Introduction Graphics parameters: par Graphical augmentation Mathematical typesetting Permanence Grouped graphs: lattice Exercises Pointers to further programming techniques Packages Frames and environments Debugging again Identifying bottlenecks Object-oriented programming: S3 Object-oriented programming: S4 Manipulation of data Compiled code Further reading Exercises Numerical accuracy and program efficiency Machine representation of numbers Significant digits Time Loops versus vectors Parallel processing Memory Caveat Exercises Root-finding Introduction Fixed-point iteration The Newton-Raphson method The secant method The bisection method Exercises Numerical integration Trapezoidal rule Simpson's rule Adaptive quadrature 210 11.4 Exercises 214 Optimisation Newton's method for optimisation The golden-section method Multivariate optimisation Steepest ascent Newton's method in higher dimensions Optimisation in R and the wider world A curve-fitting example Exercises Systems of ordinary differential equations Euler's method Midpoint method Fourth-order Runge-Kutta Efficiency Adaptive step size Exercises Probability The probability axioms Conditional probability Independence The Law of Total Probability Bayes' theorem Exercises Random variables Definition and distribution function Discrete and continuous random variables Empirical cdf's and histograms Expectation and finite approximations Transformations Variance and standard deviation The Weak Law of Large Numbers Exercises Discrete random variables Discrete random variables in R Bernoulli distribution Binomial distribution Geometric distribution Negative binomial distribution Poisson distribution Exercises Continuous random variables Continuous random variables in R Uniform distribution Lifetime models: exponential and Weibull The Poisson process and the gamma distribution Sampling distributions: normal, 2, and t Exercises Parameter estimation Point estimation The Central Limit Theorem Confidence intervals Monte Carlo confidence intervals Exercises Markov chains Introduction to discrete time chains Basic formulae: discrete time Classification of states Limiting behaviour: discrete time Finite absorbing chains Introduction to continuous time chains Rate matrix and associated equations Limiting behaviour: continuous time Defining the state space Simulation Estimation Estimating the mean of the limiting distribution Exercises Simulation Simulating iid uniform samples Simulating discrete random variables Inversion method for continuous rv Rejection method for continuous rv Simulating normals Exercises Monte Carlo integration Hit-and-miss method (Improved) Monte Carlo integration Exercises Variance reduction Antithetic sampling Importance sampling Control variates Exercises Case studies Introduction Epidemics Inventory Seed dispersal Student projects The level of a dam Runoff down a slope Roulette Buffon's needle and cross The pipe spiders of Brunswick Insurance risk Squash Stock prices Conserving water Glossary of R commands Programs and functions developed in the text Index*"The Introduction to Scientific Programming and Simulation Using
R* (2nd Edition) is a useful and well organized book. The
writing is orderly, logical, consistent, intriguing, and engaging.
We have read many programming and simulation oriented books that
vary in context, scope, and difficulty level. This one turned out
to be one of our favorites. It stands out in the sense that a
decent dose of theory is given in addition to the programming
related aspects. It covers an immense amount of material, yet
manages to do so both thoroughly and clearly."

~Hakan Demirtas, Rachel Nordgren, *University of Illinois at
Chicago*

"Computation has become so central to the field of statistics
that any practicing statistician must have a basic understanding of
scientific programming and stochastic modeling.
**Introduction to Scientific Programming and Simulation Using
R** provides an excellent entry-level text on the subject.
This is a well written and well-designed book that will appeal to a
wide readership and prove useful for several different types of
courses. It provides a very good introduction to programming using
the R language that has become widely used in statistical education
and practice. It also introduces the fundamental tools needed for
stochastic modeling: numerical analysis, probability, and
simulation.

~Christopher H. Schmid, Journal of the American Statistical
Association

**Praise for the First Edition:**

**"Overall, the authors have produced a highly readable
text. As prerequisites do not go beyond first-year calculus, the
book should appeal to a wide audience; it should also be eminently
suitable for self-study. On a somewhat larger scale, it may help to
further establish R as a kind of Swiss Army knife for computational
science. I strongly recommend it."
~C. Kleiber, Universitat Basel, Basel, Switzerland, in
Statistical Papers, March 2012**

**"This book is a good resource for someone who wants to
learn R and use R for statistical computing and graphics. It will
also serve well as a textbook or a reference book for students in a
course related to computational statistics."
~Hon Keung Tony Ng, Technometrics, May 2011**

**"... a very coherent and useful account of its chosen
subject matter. ... The programming section ... is more
comprehensive than Braun & Murdoch (2007), but more accessible than
Venables & Ripley (2000). ... The book deserves a place on
university library shelves ... One very useful feature of the book
is that nearly every chapter has a set of exercises. There are also
plenty of well-chosen examples throughout the book that are used to
explain the material. I also appreciated the clear and attractive
programming style of the R code presented in the book. I found very
little in the way of typos or solecisms. ... I can strongly
recommend the book for its intended audience. If I ever again have
to teach our stochastic modelling course, I will undoubtedly use
some of the exercises and examples from Scientific
Programming and Simulation Using R."
~David Scott, Australian & New Zealand Journal of
Statistics, 2011**

**"It is not often that I think that a statistics text is
one that most scientifc statisticians should have in their personal
libraries. Introduction to Scientific Programming and
Simulation Using R is such a text. ... This text provides
scientific researchers with a working knowledge of R for both
reviewing and for engaging in the statistical evaluation of
scientific data. ...It is particularly useful for understanding and
developing modeling and simulation software. I highly recommend the
text, finding it to be one of the most useful books I have read on
the subject."
-Journal of Statistical Software, September 2010, Volume
36**

**"The authors have written an excellent introduction to
scientific programming with R. Their clear prose, logical
structure, well-documented code and realistic examples made the
book a pleasure to read. One particularly useful feature is the
chapter of cases studies at the end, which not only demonstrates
complete analyses but also acts as a pedagogical tool to review and
integrate material introduced throughout the book. ... I would
strongly recommend this book for readers interested in using R for
simulations, particularly for those new to scientific programming
or R. It is also very student-friendly and would be suitable either
as a course textbook or for self-study."
- Significance, September 2009**

**"I think that the techniques of scientific programming
presented will soon enable the novice to apply statistical models
to real-world problems. The writing style is easy to read and the
book is suitable for private study. If you have never read a book
on scientific programming and simulation, then I recommend that you
start with this one."
- International Statistical Review, 2009**

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