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 7813 articles , 6 standard articles )

Showing results 1 to 20 of 7813.
Sorted by year (citations)

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  1. Ramachandran, Kandethody M.; Tsokos, Chris P.: Mathematical statistics with applications in R (2021)
  2. Aaron Cochrane: TEfits: Nonlinear regression for time-evolving indices (2020) not zbMATH
  3. Aaron R. Wolen; Chris H.J. Hartgerink; Ryan Hafen; Brian G. Richards; Courtney K. Soderberg; Timothy P. York: osfr: An R Interface to the Open Science Framework (2020) not zbMATH
  4. Abid, Rahma; Kokonendji, Célestin C.; Masmoudi, Afif: Geometric Tweedie regression models for continuous and semicontinuous data with variation phenomenon (2020)
  5. Alan V. Di Vittorio , Chris R. Vernon, Shijie Shu: Moirai Version 3: A Data Processing System to Generate Recent Historical Land Inputs for Global Modeling Applications at Various Scales (2020) not zbMATH
  6. Albert, Jim; Hu, Jingchen: Probability and Bayesian modeling (2020)
  7. Al-Gounmeein, Remal Shaher; Ismail, Mohd Tahir: Forecasting the exchange rate of the Jordanian dinar versus the US dollar using a Box-Jenkins seasonal ARIMA model (2020)
  8. Allouti, Chahira; Barache, Bahia; Dahmani, Abdelnasser: Exponential inequalities for Mann’s stochastic algorithm (2020)
  9. Alodat, M. T.; Shakhatreh, Mohammed K.: Gaussian process regression with skewed errors (2020)
  10. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  11. Andrew G. Allmon, J.S. Marron, Michael G. Hudgens: diproperm: An R Package for the DiProPerm Test (2020) arXiv
  12. Anita K. Nandi, Tim C. D. Lucas, Rohan Arambepola, Peter Gething, Daniel J. Weiss: disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling (2020) arXiv
  13. Annarosa Quarello, Olivier Bock, Emilie Lebarbier: A new segmentation method for the homogenisation of GNSS-derived IWV time-series (2020) arXiv
  14. Arashi, M.; Nadarajah, S.: A paradoxical argument about domination (2020)
  15. Aravkin, Aleksandr; Davis, Damek: Trimmed statistical estimation via variance reduction (2020)
  16. Aryuyuen, Sirinapa; Bodhisuwan, Winai: The type II Topp Leone-power Lomax distribution with analysis in lifetime data (2020)
  17. Ascione, Giacomo; Leonenko, Nikolai; Pirozzi, Enrica: Fractional Erlang queues (2020)
  18. Aslam, Muhammad; Ali, Sajid; Yousaf, Rahila; Shah, Ismail: Mixture of transmuted Pareto distribution: properties, applications and estimation under Bayesian framework (2020)
  19. Bagdonavičius, Vilijandas; Petkevičius, Linas: A new multiple outliers identification method in linear regression (2020)
  20. Bailer, A. John: Statistical programming in SAS (2020)

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