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 4211 articles , 6 standard articles )

Showing results 1 to 20 of 4211.
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  1. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2019)
  2. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2018)
  3. Bray, John N.; Bäärnhielm, Henrik: A new method for recognising Suzuki groups (2018)
  4. Broemeling, Lyle D.: Bayesian inference for stochastic processes (2018)
  5. Brouste, Alexandre: Statistical inference in financial and insurance mathematics with R (2018)
  6. Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee: SemiCompRisks: An R Package for Independent and Cluster-Correlated Analyses of Semi-Competing Risks Data (2018) arXiv
  7. Garg, Mansi; Dewan, Isha: On estimation of limiting variance of partial sums of functions of associated random variables (2018)
  8. Gzyl, Henryk; Mayoral, Silvia; Gomes-Gonçalves, Erika: Loss data analysis. The maximum entropy approach (to appear) (2018)
  9. Hong Zhang, Tiejun Tong, John E Landers, Zheyang Wu: TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values (2018) arXiv
  10. Iñaki Ucar, José Alberto Hernández, Pablo Serrano, Arturo Azcorra: Design and Analysis of 5G Scenarios with simmer: An R Package for Fast DES Prototyping (2018) arXiv
  11. Jeremy Yee: rlsm: R package for least squares Monte Carlo (2018) arXiv
  12. Jones, Peter Watts; Smith, Peter: Stochastic processes. An introduction. (2018)
  13. Salehi, Younes; Schiesser, William E.: Numerical integration of space fractional partial differential equations. Vol. 2: Applications from classical integer PDEs (2018)
  14. Salehi, Younes; Schiesser, William E.: Numerical integration of space fractional partial differential equations. Vol. 1: Introduction to algorithms and computer coding in R (2018)
  15. Sarah Friedrich, Frank Konietschke, Markus Pauly: Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM (2018) arXiv
  16. Adam Kaplan, Eric F. Lock: Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival (2017) arXiv
  17. Afshari, Mahmoud: Nonlinear wavelet shrinkage estimator of nonparametric regularity regression function via cross-validation with simulation study (2017)
  18. Agasisti, Tommaso; Ieva, Francesca; Paganoni, Anna Maria: Heterogeneity, school-effects and the north/south achievement gap in Italian secondary education: evidence from a three-level mixed model (2017)
  19. Aghamohammadi, Ali; Mohammadi, S.: Bayesian analysis of penalized quantile regression for longitudinal data (2017)
  20. Agresti, Alan; Kateri, Maria: Ordinal probability effect measures for group comparisons in multinomial cumulative link models (2017)

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