Free R Programming Books & Tutorials. Read online or download these free R Programming eBooks, lecture notes & tutorials.
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Introduction à la programmation en R - Introduction to R programming (Vincent Goulet)
 
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The R system has experienced amazing growth in terms of its features, the breadth of its application areas, or, more simply, the number of its users for more than ten years.  
Introduction à l'analyse d'enquête avec R et RStudio - Introduction to survey analysis with R and RStudio (Jospeh Lamarange, et al)
 
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Analyze-main R's objective is to demonstrate how to use R to carry out statistical analysis and many common operations (such as modifying data or creating graphs). The various chapters assume that you are already familiar with the various techniques...
Bayes Factors for Forensic Decision Analyses with R (Silvia Bozza, et al)
 
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With the help of the R programming language, this book offers a self-contained introduction to computational Bayesian statistics. This free programming book, which primarily focuses on Bayes factors supported by data sets, has a practical relevance and applicability focus...
Computational and Numerical Simulations (Jan Awrejcewicz)
 
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The edited book Computational and Numerical Simulations has 20 chapters. The book discusses contemporary work on numerical simulations of engineering and physical systems.  
Mathematics of Economics and Business (Frank Werner, et al.)
 
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It has become necessary for all students to have a working knowledge of mathematical techniques in order to comprehend contemporary economic and business literature.
An Introduction to R (Alex Douglas,  et al.)
 
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Using R, a potent and adaptable interactive environment for statistical computing and research, is what this book aims to expose you to.
Bayesian FreeBook (CRC)
 
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From the fundamental ideas of statistical inference to sophisticated applications of analyses, this special guide will assist students in building the statistical confidence and expertise necessary to use the Bayesian formula.
Bayesian Networks and BayesiaLab (Stefan Conrady, et al.)
 
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This hands-on introduction is intended for researchers who want to use the BayesiaLab software platform to apply Bayesian Networks to their work.
Exploratory Data Analysis with R (Roger D. Peng)
 
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The field of data science has swept the globe. As more people recognize the importance of the enormous amounts of data being generated, every field of research and industry has been impacted.
Statistical Foundations of Machine Learning: The Handbook (Gianluca Bontempi)
 
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Big data is prevalent today. People collect more data for two main reasons: first, they believe some valuable assets are implicitly coded inside them, and second, computer technology allows more efficient data storage at lower costs. ...
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