Search results for: statistical-computing-in-c-and-r

Statistical Computing in C and R

Author : Randall L. Eubank
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With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a "boot-camp" on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors’ website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment.

Proceedings of the Statistical Computing Section

Author : American Statistical Association. Statistical Computing Section
File Size : 88.17 MB
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Statistical Computing with R Second Edition

Author : Maria L. Rizzo
File Size : 29.7 MB
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Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. This second edition continues to encompass the traditional core material of computational statistics, with an

SIAM Journal on Scientific and Statistical Computing

Author : Society for Industrial and Applied Mathematics
File Size : 33.61 MB
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RStudio for R Statistical Computing Cookbook

Author : Andrea Cirillo
File Size : 46.17 MB
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Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature About This Book 54 useful and practical tasks to improve working systems Includes optimizing performance and reliability or uptime, reporting, system management tools, interfacing to standard data ports, and so on Offers 10-15 real-life, practical improvements for each user type Who This Book Is For This book is targeted at R statisticians, data scientists, and R programmers. Readers with R experience who are looking to take the plunge into statistical computing will find this Cookbook particularly indispensable. What You Will Learn Familiarize yourself with the latest advanced R console features Create advanced and interactive graphics Manage your R project and project files effectively Perform reproducible statistical analyses in your R projects Use RStudio to design predictive models for a specific domain-based application Use RStudio to effectively communicate your analyses results and even publish them to a blog Put yourself on the frontiers of data science and data monetization in R with all the tools that are needed to effectively communicate your results and even transform your work into a data product In Detail The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications. Style and approach RStudio is an open source Integrated Development Environment (IDE) for the R platform. The R programming language is used for statistical computing and graphics, which RStudio facilitates and enhances through its integrated environment. This Cookbook will help you learn to write better R code using the advanced features of the R programming language using RStudio. Readers will learn advanced R techniques to compute the language and control object evaluation within R functions. Some of the contents are: Accessing an API with R Substituting missing values by interpolation Performing data filtering activities R Statistical implementation for Geospatial data Developing shiny add-ins to expand RStudio functionalities Using GitHub with RStudio Modelling a recommendation engine with R Using R Markdown for static and dynamic reporting Curating a blog through RStudio Advanced statistical modelling with R and RStudio

The Art of R Programming

Author : Norman Matloff
File Size : 20.23 MB
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A guide to software development using the R programming language covers such topics as closures, recursion, anonymous functions, and debugging techniques.

Statistical Computation

Author : Roy C. Milton
File Size : 52.9 MB
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Statistical Computation covers the proceedings of a conference held at the University of Wisconsin in Madison, Wisconsin on April 28-30, 1969. The book focuses on the methodologies, techniques, principles, and approaches involved in statistical computation. The selection first elaborates on the description of data structures for statistical computing, autocodes for the statistician, and an experimental data structure for statistical computing. Discussions focus on data-system organization, data structures, autocode requirements, data matrix, structure formulas, and structure formulas in data processing and output. The text then examines statistics and computers in relation to large data bases, statistical data language, facilities in a statistical program system for analysis of multiply-indexed data, and language design and the needs of statisticians. The book takes a look at time sharing and interactive statistics, an approach to conversational statistics, use of APL in statistics, and continuing development of a statistical system. Topics include arithmetic operations and branching statements, ASCOP system, application to statistics, semantics, pragmatics, and implementation. The selection is a valuable reference for statisticians and researchers interested in statistical computation.

Advances in Statistical Analysis and Statistical Computing

Author : Roberto S. Mariano
File Size : 74.48 MB
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The Frontiers of Statistical Computation Simulation Modeling

Author : Peter R. Nelson
File Size : 29.34 MB
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Using R for Introductory Statistics

Author : John Verzani
File Size : 25.97 MB
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The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption. Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. This is an ideal text for integrating the study of statistics with a powerful computational tool.

Probability and Statistics with R

Author : Maria Dolores Ugarte
File Size : 70.29 MB
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This book presents treatments of data analysis using parametric and nonparametyric techniques. It links statistical concepts with R procedures, anabling the application of the language to the vast world of statistics.

Journal of the American Statistical Association

Author :
File Size : 26.13 MB
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Error Analysis for Statistical Computing Procedures

Author : William Harry Wetterstrand
File Size : 59.41 MB
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An Introduction to Computational Statistics

Author : Robert I. Jennrich
File Size : 45.17 MB
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This fully integrated development of the theory, computation, and practice of modern regression analysis—both linear and nonlinear models and analysis of variance—features many examples and problems that involve complete analysis, from data entry to report writing.This book offers a modern, software-oriented approach. It introduces statistical software early and uses it throughout. It ignores traditional topics that have been made obsolete by easy access to statistical software. Data analysis theory and traditional theory are covered. Computational detail is explicit and the book illustrates complete data analyses for a broad variety of applications. Extensive coverage of nonlinear regression is provided, with applications to maximum likelihood estimation and robust regression.

CMG Transactions

Author :
File Size : 60.70 MB
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A Comparative Review of Statistical Software

Author : Ivor Francis
File Size : 49.58 MB
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Author : Thomas M. Coffman
File Size : 67.63 MB
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The MATRIX Procedure

Author : SAS Institute
File Size : 76.70 MB
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High Speed Computing

Author : David Paul Casasent
File Size : 27.91 MB
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Current Index to Statistics Applications Methods and Theory

Author :
File Size : 64.41 MB
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