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 5148 articles , 6 standard articles )

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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. Aghamohammadi, Ali: Bayesian analysis of dynamic panel data by penalized quantile regression (2018)
  4. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018)
  5. Andrés Villegas; Vladimir Kaishev; Pietro Millossovich: StMoMo: An R Package for Stochastic Mortality Modeling (2018)
  6. Arbia, Giuseppe; Bee, Marco; Espa, Giuseppe; Santi, Flavio: Fitting spatial regressions to large datasets using unilateral approximations (2018)
  7. Arellano-Valle, Reinaldo B.; Ferreira, Clécio S.; Genton, Marc G.: Scale and shape mixtures of multivariate skew-normal distributions (2018)
  8. Arens, Tilo; Hettlich, Frank; Karpfinger, Christian; Kockelkorn, Ulrich; Lichtenegger, Klaus; Stachel, Hellmuth: Mathematics (to appear) (2018)
  9. Augustyniak, Maciej; Boudreault, Mathieu; Morales, Manuel: Maximum likelihood estimation of the Markov-switching GARCH model based on a general collapsing procedure (2018)
  10. Baeumer, Boris; Kovács, Mihály; Meerschaert, Mark M.; Sankaranarayanan, Harish: Reprint of: boundary conditions for fractional diffusion (2018)
  11. Baeumer, Boris; Kovács, Mihály; Meerschaert, Mark M.; Sankaranarayanan, Harish: Boundary conditions for fractional diffusion (2018)
  12. Bai, Haiyan; Pan, Wei: Book review of: W. Leite, Practical propensity score methods using R (2018)
  13. Barroso Da Silva, Eveliny; Ribeiro Diniz, Carlos Alberto; Farfan Carrasco, Jalmar Manuel; De Castro, Mário: A class of beta regression models with multiplicative log-normal measurement errors (2018)
  14. Bee, Marco; Dickson, Maria Michela; Santi, Flavio: Likelihood-based risk estimation for variance-gamma models (2018)
  15. Berenshtein, Igal; Paris, Claire B.; Gildor, Hezi; Fredj, Erick; Amitai, Yael; Lapidot, Omri; Kiflawi, Moshe: Auto-correlated directional swimming can enhance settlement success and connectivity in fish larvae (2018)
  16. Berghaus, Betina; Segers, Johan: Weak convergence of the weighted empirical beta copula process (2018)
  17. Bergtold, Jason S.; Pokharel, Krishna P.; Featherstone, Allen M.; Mo, Lijia: On the examination of the reliability of statistical software for estimating regression models with discrete dependent variables (2018)
  18. Bertoli, Wesley; Conceição, Katiane S.; Andrade, Marinho G.; Louzada, Francisco: On the zero-modified Poisson-Shanker regression model and its application to fetal deaths notification data (2018)
  19. Bishara, Anthony J.; Li, Jiexiang; Nash, Thomas: Asymptotic confidence intervals for the Pearson correlation via skewness and kurtosis (2018)
  20. Bodnar, Taras; Parolya, Nestor; Schmid, Wolfgang: Estimation of the global minimum variance portfolio in high dimensions (2018)

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