Aims and Scope
This book series reflects the recent rapid growth in the development and application of R, the programming language and software environment for statistical computing and graphics. R is now widely used in academic research, education, and industry. It is constantly growing, with new versions of the core software released regularly and more than 10,000 packages available. It is difficult for the documentation to keep pace with the expansion of the software, and this vital book series provides a forum for the publication of books covering many aspects of the development and application of R.
The scope of the series is wide, covering three main threads:
- Applications of R to specific disciplines such as biology, epidemiology, genetics, engineering, finance, and the social sciences.
- Using R for the study of topics of statistical methodology, such as linear and mixed modeling, time series, Bayesian methods, and missing data.
- The development of R, including programming, building packages, and graphics.
The books will appeal to programmers and developers of R software, as well as applied statisticians and data analysts in many fields. The books will feature detailed worked examples and R code fully integrated into the text, ensuring their usefulness to researchers, practitioners and students.
Recently Published Titles
- The R Primer, Second Edition
- Testing R Code
- bookdown: Authoring Books and Technical Documents with R Markdown
- Using the R Commander: A Point-and-Click Interface for R
- Extending R
- Spatial Microsimulation with R
- Statistics in Toxicology Using R
- Introductory Fisheries Analyses with R
- Basics of Matrix Algebra for Statistics with R
- Dynamic Documents with R and knitr, Second Edition
- Reproducible Research with R and R Studio, Second Edition
- Parallel Computing for Data Science: With Examples in R, C++ and CUDA
- R and MATLAB
- Graphical Data Analysis with R
- Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving
- Multiple Factor Analysis by Example Using R
- Analyzing Sensory Data with R
- Nonparametric Statistical Methods Using R
- Advanced R
John M. Chambers
Department of Statistics, Stanford University, Stanford, California, USA
Department of Biostatistics, University of Zurich, Switzerland
Duncan Temple Lang
Department of Statistics, University of California, Davis, California, USA
RStudio, Boston, Massachusetts, USA
Want to Publish With Us?
If you are interested in proposing a book for the series, please contact one of the series editors or one of our statistics acquisitions editors.