Synopsis
Implementing R for Statistics provides comprehensive coverage of basic statistical concepts using this important open-source programming language tool, from installing R and RStudio, to exploring its basic structure and uses, to extending some core functions such as vectors, basic mathematical operations, and data frames. The book will help readers understand the latest advances in the R programming language, as R allows for sophisticated and elegant data visualization. Illustrated examples are an integral part of the text, carefully designed to apply the core principles illustrated in the text to emerging topics in the field.
The text also focuses on exploiting the flexible and user-friendly nature of R. Basic concepts and recent advances in the field, including understanding the R basics, as well as implementing and practicing them in statistics, are also covered. This first edition is an essential text for students, lecturers, data scientists, and applied researchers in all areas of statistics, as well as in related fields such as biostatistics, health care, finance, risk management, social sciences, market research, and environmental and climate research.
- Introduces core statistics concepts and how to apply them using R in an accessible manner
- Explains to readers the newest advances in the area of implementing R in statistics through clear and meticulously crafted examples
- Explores the process of learning R from installation to package creation, employing clear steps and illustrated examples
- Covers important and evolving aspects of R, such as using RStudio, managing metadata, advanced regression modeling techniques, and creating packages
- Includes updated package references to the latest R packages, incorporating up-to-date tools available in the field
About the Authors
Muhammad Imran is an Assistant Director at the Department of Agriculture, Pakistan. In addition to more than 40 papers in reputable journals, he has created nine R packages. He is particularly skilled in probability and statistics, with a focus on real-world applications using R software. Specifically, he made a substantial contribution to the creation of multiple R packages related to distribution theory.
Michail Tsagris is an Associate Professor at the Department of Economics of the University of Crete (UoC) and an Adjunct Professor in the Department of Mathematics and Statistics at the University of New Brunswick Saint John. Prior to these he worked as a Teaching Fellow at the Department of Economics (UoC), as a Research Associate at the Department of Computer Science (UoC), as an Assistant Professor at the American University of the Middle East (Kuwait) and as a Research Associate at the School of Mathematical Sciences of the University of Nottingham. He received his BSc and MSc in statistics from the Athens University of Economics and Business (Greece) and his PhD in statistics from the University of Nottingham. Michail has published more than 70 papers in journals, conference proceedings and book chapters and has (co-)developed 47 R packages. His current research interests include computational statistics, compositional and directional data analysis, applied econometrics, machine learning, Bayesian network learning algorithms and variable selection algorithms.
Farrukh Jamal is currently an Assistant Professor, Department of Statistics, Faculty of Science, University of Tabuk, Saudi Arabia. He has more than 200 publications with more than 100 cumulative impact factors to his credit. He is the reviewer for more than 80 well-reputed international journals. He is a distinguished member of several editorial boards for prestigious journals, further highlighting his influential contributions to the field of probability and statistics. He is the author of six books on statistics, demonstrating his commitment to disseminating knowledge. His specialization lies in the areas of probability and statistics, with an emphasis on practical applications through the use of R software. In particular, he contributed significantly to the development of several R packages, solidifying his impact on statistical software tools.
Christophe Chesneau holds the distinguished position of "exceptional class" associate professor at the University of Caen-Normandie, France. With an extensive teaching career exceeding 20 years, his specialization lies in the areas of probability and statistics, with an emphasis on practical applications through the use of R software. Teaching is a primary passion for him, evident in his role as a responsible figure for the first year of the master's degree "Applied Statistics and Decision Analysis" over the last six years. Beyond his teaching commitments, Christophe Chesneau is the author of five books in French on probability and statistics, demonstrating his commitment to disseminating knowledge. In addition to conventional teaching, he generously shares his expertise through freely accessible online courses. His prolific research encompasses mathematics, probability, statistics, and applied data analysis, with more than 500 articles in esteemed international journals. In particular, he contributed significantly to the development of two R packages, solidifying his impact on statistical software tools. Additionally, Christophe Chesneau is a distinguished member of several editorial boards for prestigious journals, further highlighting his influential contributions to the field of probability and statistics.
"About this title" may belong to another edition of this title.
