R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/

This software is also referenced in ORMS.

References in zbMATH (referenced in 8984 articles , 6 standard articles )

Showing results 1 to 20 of 8984.
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  1. Broström, Göran: Event history analysis with R (to appear) (2022)
  2. Gamerman, Dani (ed.); Prates, Marcos O. (ed.); Paiva, Thais (ed.); Mayrink, Vinicius D. (ed.): Building a platform for data-driven pandemic prediction. From data modelling to visualisation -- the CovidLP project (2022)
  3. Loftus, Stephen C.: Basic statistics with R. Reaching decisions with data (2022)
  4. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  5. Speegle, Darrin; Clair, Bryan: Probability, statistics, and data. A fresh approach using R (to appear) (2022)
  6. Wiberg, Marie; Gonzalez, Jorge; von Davier, Alina A.: Generalized kernel equating with applications in R (to appear) (2022)
  7. Wilcox, Rand R.: Introduction to robust estimation and hypothesis testing (2022)
  8. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2021)
  9. Adithi R. Upadhya, Pratyush Agrawal, Sreekanth Vakacherla, Meenakshi Kushwaha: pollucheck v1.0: A package to explore open-source air pollution data (2021) not zbMATH
  10. Adrian Lamela, Itziar Fernandez, Yolanda Larriba, Alejandro Rodriguez, Cristina Rueda: FMM: An R Package for Modeling Rhythmic Patterns in Oscillatory Systems (2021) arXiv
  11. Adrian Richter; Carsten Oliver Schmidt; Markus Krüger; Stephan Struckmann: dataquieR: assessment of data quality in epidemiological research (2021) not zbMATH
  12. Aeberhard, William H.; Cantoni, Eva; Marra, Giampiero; Radice, Rosalba: Robust fitting for generalized additive models for location, scale and shape (2021)
  13. Agresti, Alan; Kateri, Maria: Foundations of statistics for data scientists. With R and Python (to appear) (2021)
  14. Ai, Mingyao; Yu, Jun; Zhang, Huiming; Wang, Haiying: Optimal subsampling algorithms for big data regressions (2021)
  15. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  16. Alanazi, Fadhah Amer: A mixture of regular vines for multiple dependencies (2021)
  17. Alberto Caimo, Lampros Bouranis, Robert Krause, Nial Friel: Statistical Network Analysis with Bergm (2021) arXiv
  18. Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
  19. Alex Stringer: Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package (2021) arXiv
  20. Algeri, Sara; van Dyk, David A.: Testing one hypothesis multiple times (2021)

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Further publications can be found at: http://journal.r-project.org/