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


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Table of Contents

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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