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 5842 articles , 6 standard articles )

Showing results 1 to 20 of 5842.
Sorted by year (citations)

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  1. Flores-Agreda, Daniel; Cantoni, Eva: Bootstrap estimation of uncertainty in prediction for generalized linear mixed models (2019)
  2. Liebl, Dominik; Rameseder, Stefan: Partially observed functional data: the case of systematically missing parts (2019)
  3. Palczewski, Andrzej; Palczewski, Jan: Black-Litterman model for continuous distributions (2019)
  4. Schiesser, William E.: PDE models for atherosclerosis computer implementation in R (2019)
  5. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2019)
  6. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2018)
  7. Aghamohammadi, Ali: Bayesian analysis of dynamic panel data by penalized quantile regression (2018)
  8. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018)
  9. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  10. Al Mohamad, Diaa: Towards a better understanding of the dual representation of phi divergences (2018)
  11. Álvarez de Toledo, Pablo; Núñez, Fernando; Usabiaga, Carlos: Matching and clustering in square contingency tables. Who matches with whom in the Spanish labour market (2018)
  12. Andrés Villegas; Vladimir Kaishev; Pietro Millossovich: StMoMo: An R Package for Stochastic Mortality Modeling (2018)
  13. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018)
  14. Arbia, Giuseppe; Bee, Marco; Espa, Giuseppe; Santi, Flavio: Fitting spatial regressions to large datasets using unilateral approximations (2018)
  15. Archimbaud, Aurore; Nordhausen, Klaus; Ruiz-Gazen, Anne: ICS for multivariate outlier detection with application to quality control (2018)
  16. Ardia, David; Bluteau, Keven; Hoogerheide, Lennart F.: Methods for computing numerical standard errors: review and application to value-at-risk estimation (2018)
  17. Arellano-Valle, Reinaldo B.; Ferreira, Clécio S.; Genton, Marc G.: Scale and shape mixtures of multivariate skew-normal distributions (2018)
  18. Arens, Tilo; Hettlich, Frank; Karpfinger, Christian; Kockelkorn, Ulrich; Lichtenegger, Klaus; Stachel, Hellmuth: Mathematics (2018)
  19. Asfha, Huruy Debessay; Kilinc, Betul Kan: Appraisal of performance of three tree-based classification methods (2018)
  20. Audigier, Vincent; White, Ian R.; Jolani, Shahab; Debray, Thomas P. A.; Quartagno, Matteo; Carpenter, James; van Buuren, Stef; Resche-Rigon, Matthieu: Multiple imputation for multilevel data with continuous and binary variables (2018)

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