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Numerical optimization

·2021·A First Course in Statistical Programming with R

Abstract

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and Metropolis–Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.

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Metadata

Venue
A First Course in Statistical Programming with R
Published
2021-05-20
Pages
222-247
OpenAlex
W3029645440
Cited by
13,724
References
0
FWCI
453.72
Type
book chapter
Language
en
Open access
closed

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CoChat. (2026). Citation check: Numerical optimization. https://app.cochat.ai/papers/10.1017/9781108993456.011
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Numerical optimization. (2021). A First Course in Statistical Programming with R. https://doi.org/10.1017/9781108993456.011

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.