Showing papers on "R Programming Language published in 2009"
Book•
10 Dec 2009
TL;DR: This book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms.
Abstract: Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader.
76 citations
Book•
14 Jul 2009
TL;DR: This book demystifies and explains fundamental ideas in population and community ecology and provides a tractable introduction to using the R programming environment in ecology.
Abstract: Ecology is more quantitative and theory-driven than ever before, and A Primer of Ecology with R combines an introduction to the major theoretical concepts in general ecology with a cutting edge open source tool, the R programming language. Starting with geometric growth and proceeding through stability of multispecies interactions and species-abundance distributions, this book demystifies and explains fundamental ideas in population and community ecology. Graduate students in ecology, along with upper division undergraduates and faculty, will find this to be a useful overview of important topics. In addition to the most basic topics, this book includes construction and analysis of demographic matrix models, metapopulation and source-sink models, host-parasitoid and disease models, multiple basins of attraction, the storage effect, neutral theory, and diversity partitioning. Several sections include examples of confronting models with data. Chapter summaries and problem sets at the end of each chapter provide opportunities to evaluate and enrich one's understanding of the ecological ideas that each chapter introduces. R is rapidly becoming the lingua franca of quantitative sciences, and this text provides a tractable introduction to using the R programming environment in ecology. An appendix provides a general introduction, and examples of code throughout each chapter give readers the option to hone their growing R skills. "The distinctive strength of this book is that truths are mostly not revealed but discovered, in the way that R-savvy ecologistsempirical and theoreticalwork and think now. For readers still chained to spreadsheets, working through this book could be a revolution in their approach to doing science." (Stephen P. Ellner, Cornell University) "One of the greatest strengthsis the integration of ecological theory with examples ... pulled straight from the literature." (James R. Vonesh, Virginia Commonwealth University)
74 citations
TL;DR: An open source, extensible graphical user interface (GUI) iFlow, which sits on top of the Bioconductor backbone, enabling basic analyses by means of convenient graphical menus and wizards, and is envisioned to be easily extensible in order to quickly integrate novel methodological developments.
Abstract: Flow cytometry (FCM) has become an important analysis technology in health care and medical research, but the large volume of data produced by modern high-throughput experiments has presented significant new challenges for computational analysis tools. The development of an FCM software suite in Bioconductor represents one approach to overcome these challenges. In the spirit of the R programming language (Tree Star Inc., “FlowJo,” http://www.owjo.com), these tools are predominantly console-driven, allowing for programmatic access and rapid development of novel algorithms. Using this software requires a solid understanding of programming concepts and of the R language. However, some of these tools|in particular the statistical graphics and novel analytical methods|are also useful for nonprogrammers. To this end, we have developed an open source, extensible graphical user interface (GUI) iFlow, which sits on top of the Bioconductor backbone, enabling basic analyses by means of convenient graphical menus and wizards. We envision iFlow to be easily extensible in order to quickly integrate novel methodological developments.
9 citations