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 6593 articles , 6 standard articles )

Showing results 1 to 20 of 6593.
Sorted by year (citations)

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  1. 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)
  2. Ritz, Christian; Jensen, Signe Marie; Gerhard, Daniel; Streibig, Jens Carl: Dose-response analysis using R (2020)
  3. Sengupta, Debasis; Jammalamadaka, Sreenivasa Rao: Linear models and regression with R. An integrated approach (2020)
  4. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2020)
  5. Adam Gudyś, Marek Sikora, Łukasz Wróbel: RuleKit: A Comprehensive Suite for Rule-Based Learning (2019) arXiv
  6. Adam, Timo; Langrock, Roland; Weiß, Christian H.: Penalized estimation of flexible hidden Markov models for time series of counts (2019)
  7. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  8. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  9. Akinkunmi, Mustapha: Introduction to statistics using R (2019)
  10. Alarcón-Soto, Yovaninna; Langohr, Klaus; Fehér, Csaba; García, Felipe; Gómez, Guadalupe: Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox model (2019)
  11. Alex Boyd, Dennis L. Sun: salmon: A Symbolic Linear Regression Package for Python (2019) arXiv
  12. Alfaro, Esteban (ed.); Gámez, Matías (ed.); García, Noelia (ed.): Ensemble classification methods with applications in R (2019)
  13. Alfonso Iodice D’Enza, Angelos Markos, Michel van de Velden: Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R (2019) not zbMATH
  14. Alireza S. Mahani; Mansour T.A. Sharabiani: Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC (2019) not zbMATH
  15. Allévius, Benjamin; Höhle, Michael: An unconditional space-time scan statistic for ZIP-distributed data (2019)
  16. Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes: DRHotNet: An R package for detecting differential risk hotspots on a linear network (2019) arXiv
  17. Amalan Mahendran; Pushpakanthie Wijekoon: fitODBOD: An R Package to Model Binomial Outcome Data using Binomial Mixture and Alternate Binomial Distributions (2019) not zbMATH
  18. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  19. Amrei Stammann, Daniel Czarnowske: Binary Choice Models with High-Dimensional Individual and Time Fixed Effects (2019) arXiv
  20. Anderson, David F.; Higham, Desmond J.; Leite, Saul C.; Williams, Ruth J.: On constrained Langevin equations and (bio)chemical reaction networks (2019)

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