R

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 9832 articles , 6 standard articles )

Showing results 1 to 20 of 9832.
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  1. Accorsi, Luca; Lodi, Andrea; Vigo, Daniele: Guidelines for the computational testing of machine learning approaches to vehicle routing problems (2022)
  2. Agresti, Alan; Kateri, Maria: Foundations of statistics for data scientists. With R and Python (2022)
  3. Akhter, Zuber; MirMostafaee, S. M. T. K.; Ormoz, E.: On the order statistics of exponentiated moment exponential distribution and associated inference (2022)
  4. Al-Ahmadgaid B. Asaad, Arnold R. Salvacion, Bui Tan Yen: ALUES: R package for Agricultural Land Use Evaluation System (2022) not zbMATH
  5. Alderliesten, Jesse B.; Zwart, Mark P.; de Visser, J. Arjan G. M.; Stegeman, Arjan; Fischer, Egil A. J.: Second compartment widens plasmid invasion conditions: two-compartment pair-formation model of conjugation in the gut (2022)
  6. Alomari, A. I.: Book review of: K. Kleinke et al., Applied multiple imputation. Advantages, pitfalls, new developments and applications in R (2022)
  7. Anastasiou, Andreas; Fryzlewicz, Piotr: Detecting multiple generalized change-points by isolating single ones (2022)
  8. Anderlucci, Laura; Fortunato, Francesca; Montanari, Angela: High-dimensional clustering via random projections (2022)
  9. Anders Granholm, Aksel Karl Georg Jensen, Theis Lange, Benjamin Skov Kaas-Hansen: adaptr: an R package for simulating and comparing adaptive clinical trials (2022) not zbMATH
  10. Andreas Alfons, Nüfer Y. Ateş, Patrick J. F. Groenen: Robust Mediation Analysis: The R Package robmed (2022) arXiv
  11. Arachchige, Chandima N. P. G.; Prendergast, Luke A.; Staudte, Robert G.: Robust analogs to the coefficient of variation (2022)
  12. Arkajyoti Saha, Sumanta Basu, Abhirup Datta: RandomForestsGLS: An R package for Random Forests for dependent data (2022) not zbMATH
  13. Arroyo Bravo, Luis Gabriel; Lasso Balanta, Fabian Alejandro; Tovar Cuevas, José Rafael: Proposal for obtaining a priori distributions or the shape parameters of the beta distribution (2022)
  14. Asar, Yasin; Korkmaz, Merve: Almost unbiased Liu-type estimators in gamma regression model (2022)
  15. Azaïs, R.; Ferrigno, S.; Martinez, M.-J.: cvmgof: an R package for Cramér-von Mises goodness-of-fit tests in regression models (2022)
  16. Baddeley, Adrian; Davies, Tilman M.; Rakshit, Suman; Nair, Gopalan; McSwiggan, Greg: Diffusion smoothing for spatial point patterns (2022)
  17. Battauz, Michela; Vidoni, Paolo: A likelihood-based boosting algorithm for factor analysis models with binary data (2022)
  18. Bauer, Verena; Harhoff, Dietmar; Kauermann, Göran: A smooth dynamic network model for patent collaboration data (2022)
  19. Baumgartner, Matheus Tenório; Faria, Lucas Del Bianco: The sensitivity of complex dynamic food webs to the loss of top omnivores (2022)
  20. Bean, Brennan; Sun, Yan; Maguire, Marc: Interval-valued kriging for geostatistical mapping with imprecise inputs (2022)

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