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 5945 articles , 6 standard articles )

Showing results 1 to 20 of 5945.
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  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Blitzstein, Joseph K.; Hwang, Jessica: Introduction to probability. (2019)
  3. Djeundje, Viani Biatat; Crook, Jonathan: Dynamic survival models with varying coefficients for credit risks. (2019)
  4. Flores-Agreda, Daniel; Cantoni, Eva: Bootstrap estimation of uncertainty in prediction for generalized linear mixed models (2019)
  5. Horváth, Lajos; Rice, Gregory: Asymptotics for empirical eigenvalue processes in high-dimensional linear factor models (2019)
  6. Le Roux, Brigitte; Bienaise, Solène; Durand, Jean-Luc: Combinatorial inference in geometric data analysis (to appear) (2019)
  7. Liebl, Dominik; Rameseder, Stefan: Partially observed functional data: the case of systematically missing parts (2019)
  8. Nadarajah, Saralees; Chan, Stephen: The exact distribution of the sum of stable random variables (2019)
  9. Pak, Abbas; Dey, Sanku: Statistical inference for the power Lindley model based on record values and inter-record times (2019)
  10. Palczewski, Andrzej; Palczewski, Jan: Black-Litterman model for continuous distributions (2019)
  11. Rocio Joo, Matthew E. Boone, Thomas A. Clay, Samantha C. Patrick, Susana Clusella-Trullas, Mathieu Basille: Navigating through the R packages for movement (2019) arXiv
  12. Schiesser, William E.: PDE models for atherosclerosis computer implementation in R (2019)
  13. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2019)
  14. Wilkinson, Darren J.: Stochastic modelling for systems biology (2019)
  15. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2018)
  16. Abramowicz, Konrad; Häger, Charlotte K.; Pini, Alessia; Schelin, Lina; de Luna, Sara Sjöstedt; Vantini, Simone: Nonparametric inference for functional-on-scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament (2018)
  17. Adam Peterson, Brisa Sanchez: rstap: An R Package for Spatial Temporal Aggregated Predictor Models (2018) arXiv
  18. Aghamohammadi, Ali: Bayesian analysis of dynamic panel data by penalized quantile regression (2018)
  19. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  20. Alfaro, Esteban (ed.); Gámez, Matías (ed.); García, Noelia (ed.): Ensemble classification methods with applications in R (2018)

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