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A first course in statistical programming with R 1st Edition by W John Braun, Duncan J Murdoch ISBN 0521872650 9780521872652

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A first course in statistical programming with R 1st Edition W. John Braun Digital Instant Download

Author(s): W. John Braun, Duncan J. Murdoch
ISBN(s): 9780521872652, 0521872650
Edition: 1
File Details: PDF, 1.05 MB
Year: 2007
Language: english
SKU: EB-1076942 Category: Tags: ,

A first course in statistical programming with R 1st Edition by W John Braun, Duncan J Murdoch – Ebook PDF Instant Download/Delivery: 0521872650, 9780521872652
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Product details:

ISBN 10: 0521872650 
ISBN 13: 9780521872652
Author: W John Braun, Duncan J Murdoch

This is the only introduction you’ll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the ground-breaking R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Learning the language is made easier by the frequent exercises and end-of-chapter reviews that help you progress confidently through the book. Solutions, datasets and any errata will be available from the book’s web site. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.

A first course in statistical programming with R 1st Table of contents:

1. Getting Started

  • Introduction to statistical programming and the R environment.

  • Installation and basic usage of R.

  • Command line interface benefits and conventions.

2. Introduction to the R Language

  • Starting and quitting R, recording work.

  • Core R concepts: vectors, functions, data frames, matrices, factors.

  • Handling missing data, logical operations, and data input/output.

  • Use of built-in functions and online help.

3. Programming Statistical Graphics

  • Creating high-level plots: bar charts, histograms, scatterplots, boxplots, etc.

  • Choosing appropriate graphics.

  • Using low-level functions to customize plots.

4. Programming with R

  • Control structures: loops, conditionals.

  • Writing and managing functions.

  • Debugging, documentation, efficient programming practices.

5. Simulation

  • Monte Carlo methods and generating random variables.

  • Integration and advanced simulation techniques.

6. Computational Linear Algebra

  • Matrix creation, operations, and properties.

  • Matrix decompositions, eigenvalues, and advanced matrix topics.

  • Practical use of matrix functions like apply().

7. Numerical Optimization

  • Optimization methods: golden section search, Newton-Raphson, Nelder-Mead.

  • Solving linear and quadratic programming problems.

  • Handling constraints and special cases.

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Tags: W John Braun, Duncan J Murdoch, statistical programming