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 4605 articles , 6 standard articles )

Showing results 1 to 20 of 4605.
Sorted by year (citations)

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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. Arbia, Giuseppe; Bee, Marco; Espa, Giuseppe; Santi, Flavio: Fitting spatial regressions to large datasets using unilateral approximations (2018)
  5. Augustyniak, Maciej; Boudreault, Mathieu; Morales, Manuel: Maximum likelihood estimation of the Markov-switching GARCH model based on a general collapsing procedure (2018)
  6. Baeumer, Boris; Kovács, Mihály; Meerschaert, Mark M.; Sankaranarayanan, Harish: Boundary conditions for fractional diffusion (2018)
  7. Bee, Marco; Dickson, Maria Michela; Santi, Flavio: Likelihood-based risk estimation for variance-gamma models (2018)
  8. Bishara, Anthony J.; Li, Jiexiang; Nash, Thomas: Asymptotic confidence intervals for the Pearson correlation via skewness and kurtosis (2018)
  9. Bolfarine, Heleno; Martínez-Flórez, Guillermo; Salinas, Hugo S.: Bimodal symmetric-asymmetric power-normal families (2018)
  10. Borcard, Daniel; Gillet, François; Legendre, Pierre: Numerical ecology with R (2018)
  11. Bornkamp, Björn: Calculating quantiles of noisy distribution functions using local linear regressions (2018)
  12. Bray, John N.; Bäärnhielm, Henrik: A new method for recognising Suzuki groups (2018)
  13. Broemeling, Lyle D.: Bayesian inference for stochastic processes (2018)
  14. Brosse, Nicolas; Durmus, Alain; Moulines, Éric: Normalizing constants of log-concave densities (2018)
  15. Brouste, Alexandre: Statistical inference in financial and insurance mathematics with R (2018)
  16. Caravenna, Francesco; Corbetta, Jacopo: The asymptotic smile of a multiscaling stochastic volatility model (2018)
  17. Cardinali, Alessandro; Nason, Guy P.: Practical powerful wavelet packet tests for second-order stationarity (2018)
  18. Cavoretto, Roberto; Schneider, Teseo; Zulian, Patrick: OpenCL based parallel algorithm for RBF-PUM interpolation (2018)
  19. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using $\ell_1$-penalized likelihood (2018)
  20. Chanialidis, Charalampos; Evers, Ludger; Neocleous, Tereza; Nobile, Agostino: Efficient Bayesian inference for COM-Poisson regression models (2018)

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