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 6934 articles , 6 standard articles )

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  1. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  2. Dasdemir, Erdi; Köksalan, Murat; Tezcaner Öztürk, Diclehan: A flexible reference point-based multi-objective evolutionary algorithm: an application to the UAV route planning problem (2020)
  3. Flórez, Alvaro Jóse; Alonso Abad, Ariel; Molenberghs, Geert; Van Der Elst, Wim: Generating random correlation matrices with fixed values: an application to the evaluation of multivariate surrogate endpoints (2020)
  4. Giovanna Jona Lasinio; Gianluca Mastrantonio; Mario Santoro: CircSpaceTime: an R package for spatial and spatio-temporal modeling of Circular data (2020) arXiv
  5. Irizarry, Rafael A.: Introduction to data science. Data analysis and prediction algorithms with R (2020)
  6. Rachael C. Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Jonathan Chen: Stratified Pilot Matching in R: The stratamatch Package (2020) arXiv
  7. Ramachandran, Kandethody; Tsokos, Chris: Mathematical statistics with applications in R (2020)
  8. Ritz, Christian; Jensen, Signe Marie; Gerhard, Daniel; Streibig, Jens Carl: Dose-response analysis using R (2020)
  9. Schiesser, W. E.: Time delay ODE/PDE models. Applications in biomedical science and engineering (2020)
  10. Sengupta, Debasis; Jammalamadaka, Sreenivasa Rao: Linear models and regression with R. An integrated approach (2020)
  11. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2020)
  12. Vinod, Hrishikesh D. (ed.); Rao, C. R. (ed.): Financial, macro and micro econometrics using R (to appear) (2020)
  13. Wang, Wenjing; Zhang, Xin; Mai, Qing: Model-based clustering with envelopes (2020)
  14. Yoon, Hwan-Jin; Welsh, Alan H.: On the effect of ignoring correlation in the covariates when fitting linear mixed models (2020)
  15. Adam Gudyś, Marek Sikora, Łukasz Wróbel: RuleKit: A Comprehensive Suite for Rule-Based Learning (2019) arXiv
  16. Adam, Timo; Langrock, Roland; Weiß, Christian H.: Penalized estimation of flexible hidden Markov models for time series of counts (2019)
  17. Agostinelli, Claudio; Greco, Luca: Weighted likelihood estimation of multivariate location and scatter (2019)
  18. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  19. Agra, Agostinho; Cerdeira, Jorge Orestes; Requejo, Cristina: A computational comparison of compact MILP formulations for the zero forcing number (2019)
  20. Aguirre-López, Mario A.; Díaz-Hernández, O.; Hueyotl-Zahuantitla, Filiberto; Morales-Castillo, Javier; Almaguer, F.-Javier; Escalera Santos, Gerardo J.: A cardioid-parametric model for the Magnus effect in baseballs (2019)

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